Gene-based interaction analysis shows GABAergic genes interacting with parenting in adolescent depressive symptoms.

PubMed

Van Assche, Evelien; Moons, Tim; Cinar, Ozan; Viechtbauer, Wolfgang; Oldehinkel, Albertine J; Van Leeuwen, Karla; Verschueren, Karine; Colpin, Hilde; Lambrechts, Diether; Van den Noortgate, Wim; Goossens, Luc; Claes, Stephan; van Winkel, Ruud

2017-12-01

Most gene-environment interaction studies (G × E) have focused on single candidate genes. This approach is criticized for its expectations of large effect sizes and occurrence of spurious results. We describe an approach that accounts for the polygenic nature of most psychiatric phenotypes and reduces the risk of false-positive findings. We apply this method focusing on the role of perceived parental support, psychological control, and harsh punishment in depressive symptoms in adolescence. Analyses were conducted on 982 adolescents of Caucasian origin (M age (SD) = 13.78 (.94) years) genotyped for 4,947 SNPs in 263 genes, selected based on a literature survey. The Leuven Adolescent Perceived Parenting Scale (LAPPS) and the Parental Behavior Scale (PBS) were used to assess perceived parental psychological control, harsh punishment, and support. The Center for Epidemiologic Studies Depression Scale (CES-D) was the outcome. We used gene-based testing taking into account linkage disequilibrium to identify genes containing SNPs exhibiting an interaction with environmental factors yielding a p-value per single gene. Significant results at the corrected p-value of p < 1.90 × 10 -4 were examined in an independent replication sample of Dutch adolescents (N = 1354). Two genes showed evidence for interaction with perceived support: GABRR1 (p = 4.62 × 10 -5 ) and GABRR2 (p = 9.05 × 10 -6 ). No genes interacted significantly with psychological control or harsh punishment. Gene-based analysis was unable to confirm the interaction of GABRR1 or GABRR2 with support in the replication sample. However, for GABRR2, but not GABRR1, the correlation of the estimates between the two datasets was significant (r (46) = .32; p = .027) and a gene-based analysis of the combined datasets supported GABRR2 × support interaction (p = 1.63 × 10 -4 ). We present a gene-based method for gene-environment interactions in a polygenic context and show that genes

Incorporating gene-environment interaction in testing for association with rare genetic variants.

PubMed

Chen, Han; Meigs, James B; Dupuis, Josée

2014-01-01

The incorporation of gene-environment interactions could improve the ability to detect genetic associations with complex traits. For common genetic variants, single-marker interaction tests and joint tests of genetic main effects and gene-environment interaction have been well-established and used to identify novel association loci for complex diseases and continuous traits. For rare genetic variants, however, single-marker tests are severely underpowered due to the low minor allele frequency, and only a few gene-environment interaction tests have been developed. We aimed at developing powerful and computationally efficient tests for gene-environment interaction with rare variants. In this paper, we propose interaction and joint tests for testing gene-environment interaction of rare genetic variants. Our approach is a generalization of existing gene-environment interaction tests for multiple genetic variants under certain conditions. We show in our simulation studies that our interaction and joint tests have correct type I errors, and that the joint test is a powerful approach for testing genetic association, allowing for gene-environment interaction. We also illustrate our approach in a real data example from the Framingham Heart Study. Our approach can be applied to both binary and continuous traits, it is powerful and computationally efficient.

Childhood temperament: passive gene-environment correlation, gene-environment interaction, and the hidden importance of the family environment.

PubMed

Lemery-Chalfant, Kathryn; Kao, Karen; Swann, Gregory; Goldsmith, H Hill

2013-02-01

Biological parents pass on genotypes to their children, as well as provide home environments that correlate with their genotypes; thus, the association between the home environment and children's temperament can be genetically (i.e., passive gene-environment correlation) or environmentally mediated. Furthermore, family environments may suppress or facilitate the heritability of children's temperament (i.e., gene-environment interaction). The sample comprised 807 twin pairs (mean age = 7.93 years) from the longitudinal Wisconsin Twin Project. Important passive gene-environment correlations emerged, such that home environments were less chaotic for children with high effortful control, and this association was genetically mediated. Children with high extraversion/surgency experienced more chaotic home environments, and this correlation was also genetically mediated. In addition, heritability of children's temperament was moderated by home environments, such that effortful control and extraversion/surgency were more heritable in chaotic homes, and negative affectivity was more heritable under crowded or unsafe home conditions. Modeling multiple types of gene-environment interplay uncovered the complex role of genetic factors and the hidden importance of the family environment for children's temperament and development more generally.

The Impact of Gene-Environment Dependence and Misclassification in Genetic Association Studies Incorporating Gene-Environment Interactions

PubMed Central

Lindström, Sara; Yen, Yu-Chun; Spiegelman, Donna; Kraft, Peter

2009-01-01

The possibility of gene-environment interaction can be exploited to identify genetic variants associated with disease using a joint test of genetic main effect and gene-environment interaction. We consider how exposure misclassification and dependence between the true exposure E and the tested genetic variant G affect this joint test in absolute terms and relative to three other tests: the marginal test (G), the standard test for multiplicative gene-environment interaction (GE), and the case-only test for interaction (GE-CO). All tests can have inflated Type I error rate when E and G are correlated in the underlying population. For the GE and G-GE tests this inflation is only noticeable when the gene-environment dependence is unusually strong; the inflation can be large for the GE-CO test even for modest correlation. The joint G-GE test has greater power than the GE test generally, and greater power than the G test when there is no genetic main effect and the measurement error is small to moderate. The joint G-GE test is an attractive test for assessing genetic association when there is limited knowledge about casual mechanisms a priori, even in the presence of misclassification in environmental exposure measurement and correlation between exposure and genetic variants. PMID:19521099

Childhood Temperament: Passive Gene-Environment Correlation, Gene-Environment Interaction, and the Hidden Importance of the Family Environment

PubMed Central

Lemery-Chalfant, Kathryn; Kao, Karen; Swann, Gregory; Goldsmith, H. Hill

2013-01-01

Biological parents pass on genotypes to their children, as well as provide home environments that correlate with their genotypes; thus, the association between the home environment and children's temperament can be genetically (i.e. passive gene-environment correlation) or environmentally mediated. Furthermore, family environments may suppress or facilitate the heritability of children's temperament (i.e. gene-environment interaction). The sample comprised 807 twin pairs (M age = 7.93 years) from the longitudinal Wisconsin Twin Project. Important passive gene-environment correlations emerged, such that home environments were less chaotic for children with high Effortful Control, and this association was genetically mediated. Children with high Extraversion/Surgency experienced more chaotic home environments, and this correlation was also genetically mediated. In addition, heritability of children's temperament was moderated by home environments, such that Effortful Control and Extraversion/Surgency were more heritable in chaotic homes, and Negative Affectivity was more heritable under crowded or unsafe home conditions. Modeling multiple types of gene-environment interplay uncovered the complex role of genetic factors and the hidden importance of the family environment for children's temperament and development more generally. PMID:23398752

Testing for gene-environment interaction under exposure misspecification.

PubMed

Sun, Ryan; Carroll, Raymond J; Christiani, David C; Lin, Xihong

2017-11-09

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties. © 2017, The International Biometric Society.

Gene-Environment Interactions in Cancer Epidemiology: A National Cancer Institute Think Tank Report

PubMed Central

Hutter, Carolyn M.; Mechanic, Leah E.; Chatterjee, Nilanjan; Kraft, Peter; Gillander, Elizabeth M.

2014-01-01

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF]>0.05) and less common (0.01genes and environment, including gene-environment interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a “Gene-Environment Think Tank” on January 10th–011th, 2012. The objective of the Think Tank was to facilitate discussions on: 1) the state of the science; 2) the goals of gene-environment interaction studies in cancer epidemiology; and 3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of gene-environment interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors. PMID:24123198

BDNF Val66Met polymorphism, life stress and depression: A meta-analysis of gene-environment interaction.

PubMed

Zhao, Mingzhe; Chen, Lu; Yang, Jiarun; Han, Dong; Fang, Deyu; Qiu, Xiaohui; Yang, Xiuxian; Qiao, Zhengxue; Ma, Jingsong; Wang, Lin; Jiang, Shixiang; Song, Xuejia; Zhou, Jiawei; Zhang, Jian; Chen, Mingqi; Qi, Dong; Yang, Yanjie; Pan, Hui

2018-02-01

Depression is thought to be multifactorial in etiology, including genetic and environmental components. While a number of gene-environment interaction studies have been carried out, meta-analyses are scarce. The present meta-analysis aimed to quantify evidence on the interaction between brain-derived neurotrophic factor (BDNF) Val66Met polymorphism and stress in depression. Included were 31 peer-reviewed with a pooled total of 21060 participants published before October 2016 and literature searches were conducted using PubMed, Wolters Kluwer, Web of Science, EBSCO, Elsevier Science Direct and Baidu Scholar databases. The results indicated that the Met allele of BDNF Val66Met polymorphism significantly moderated the relationship between stress and depression (Z=2.666, p = 0.003). The results of subgroup analysis concluded that stressful life events and childhood adversity separately interacted with the Met allele of BDNF Val66Met polymorphism in depression (Z = 2.552, p = 0.005; Z = 1.775, p = 0.03). The results could be affected by errors or bias in primary studies which had small sample sizes with relatively lower statistic power. We could not estimate how strong the interaction effect between gene and environment was. We found evidence that supported the hypothesis that BDNF Val66Met polymorphism moderated the relationship between stress and depression, despite the fact that many included individual studies did not show this effect. Copyright © 2017 Elsevier B.V. All rights reserved.

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions.

PubMed

Chatterjee, Nilanjan; Kalaylioglu, Zeynep; Moslehi, Roxana; Peters, Ulrike; Wacholder, Sholom

2006-12-01

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.

Gene Polymorphism Association with Type 2 Diabetes and Related Gene-Gene and Gene-Environment Interactions in a Uyghur Population

PubMed Central

Xiao, Shan; Zeng, Xiaoyun; Fan, Yong; Su, Yinxia; Ma, Qi; Zhu, Jun; Yao, Hua

2016-01-01

Background We investigated the association between 8 single-nucleotide polymorphisms (SNPs) at 3 genetic loci (CDKAL1, CDKN2A/2B and FTO) with type 2 diabetes (T2D) in a Uyghur population. Material/Methods A case-control study of 879 Uyghur patients with T2D and 895 non-diabetic Uyghur controls was conducted at the Hospital of Xinjiang Medical University between 2010 and 2013. Eight SNPs in CDKAL1, CDKN2A/2B and FTO were analyzed using Sequenom MassARRAY®SNP genotyping. Factors associated with T2D were assessed by logistic regression analyses. Gene-gene and gene-environment interactions were analyzed by generalized multifactor dimensionality reduction. Results Genotype distributions of rs10811661 (CDKN2A/2B), rs7195539, rs8050136, and rs9939609 (FTO) and allele frequencies of rs8050136 and rs9939609 differed significantly between diabetes and control groups (all P<0.05). While rs10811661, rs8050136, and rs9939609 were eliminated after adjusting for covariates (P>0.05), rs7195539 distribution differed significantly in co-dominant and dominant models (P<0.05). In gene-gene interaction analysis, after adjusting for covariates the two-locus rs10811661-rs7195539 interaction model had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.5483 (P=0.014). In gene-environment interaction analysis, the 3-locus interaction model TG-HDL-family history of diabetes had a cross-validation consistency of 10/10 and the highest balanced accuracy of 0.7072 (P<0.001). The 4-locus interaction model, rs7195539-TG-HDL-family history of diabetes had a cross-validation consistency of 8/10 (P<0.001). Conclusions Polymorphisms in CDKN2A/2B and FTO, but not CDKAL1, may be associated with T2D, and alleles rs8050136 and rs9939609 are likely risk alleles for T2D in this population. There were potential interactions among CDKN2A/2B (rs10811661) – FTO (rs7195539) or FTO (rs7195539)-TG-HDL-family history of diabetes in the pathogenesis of T2D in a Uyghur population. PMID

Life events and borderline personality features: the influence of gene-environment interaction and gene-environment correlation.

PubMed

Distel, M A; Middeldorp, C M; Trull, T J; Derom, C A; Willemsen, G; Boomsma, D I

2011-04-01

Traumatic life events are generally more common in patients with borderline personality disorder (BPD) than in non-patients or patients with other personality disorders. This study investigates whether exposure to life events moderates the genetic architecture of BPD features. As the presence of genotype-environment correlation (rGE) can lead to spurious findings of genotype-environment interaction (G × E), we also test whether BPD features increase the likelihood of exposure to life events. The extent to which an individual is at risk to develop BPD was assessed with the Personality Assessment Inventory - Borderline features scale (PAI-BOR). Life events under study were a divorce/break-up, traffic accident, violent assault, sexual assault, robbery and job loss. Data were available for 5083 twins and 1285 non-twin siblings. Gene-environment interaction and correlation were assessed by using structural equation modelling (SEM) and the co-twin control design. There was evidence for both gene-environment interaction and correlation. Additive genetic influences on BPD features interacted with the exposure to sexual assault, with genetic variance being lower in exposed individuals. In individuals who had experienced a divorce/break-up, violent assault, sexual assault or job loss, environmental variance for BPD features was higher, leading to a lower heritability of BPD features in exposed individuals. Gene-environment correlation was present for some life events. The genes that influence BPD features thus also increased the likelihood of being exposed to certain life events. To our knowledge, this study is the first to test the joint effect of genetic and environmental influences and the exposure to life events on BPD features in the general population. Our results indicate the importance of both genetic vulnerability and life events.

Design and analysis issues in gene and environment studies

PubMed Central

2012-01-01

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the “-omics” era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed. PMID:23253229

Design and analysis issues in gene and environment studies.

PubMed

Liu, Chen-yu; Maity, Arnab; Lin, Xihong; Wright, Robert O; Christiani, David C

2012-12-19

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the "-omics" era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed.

Gene-Gene-Environment Interactions of Serotonin Transporter, Monoamine Oxidase A and Childhood Maltreatment Predict Aggressive Behavior in Chinese Adolescents

PubMed Central

Zhang, Yun; Ming, Qing-sen; Yi, Jin-yao; Wang, Xiang; Chai, Qiao-lian; Yao, Shu-qiao

2017-01-01

Gene-environment interactions that moderate aggressive behavior have been identified independently in the serotonin transporter (5-HTT) gene and monoamine oxidase A gene (MAOA). The aim of the present study was to investigate epistasis interactions between MAOA-variable number tandem repeat (VNTR), 5-HTTlinked polymorphism (LPR) and child abuse and the effects of these on aggressive tendencies in a group of otherwise healthy adolescents. A group of 546 Chinese male adolescents completed the Child Trauma Questionnaire and Youth self-report of the Child Behavior Checklist. Buccal cells were collected for DNA analysis. The effects of childhood abuse, MAOA-VNTR, 5-HTTLPR genotypes and their interactive gene-gene-environmental effects on aggressive behavior were analyzed using a linear regression model. The effect of child maltreatment was significant, and a three-way interaction among MAOA-VNTR, 5-HTTLPR and sexual abuse (SA) relating to aggressive behaviors was identified. Chinese male adolescents with high expression of the MAOA-VNTR allele and 5-HTTLPR “SS” genotype exhibited the highest aggression tendencies with an increase in SA during childhood. The findings reported support aggression being a complex behavior involving the synergistic effects of gene-gene-environment interactions. PMID:28203149

Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design.

PubMed

Goldstein, Alisa M; Dondon, Marie-Gabrielle; Andrieu, Nadine

2006-08-01

A design combining both related and unrelated controls, named the case-combined-control design, was recently proposed to increase the power for detecting gene-environment (GxE) interaction. Under a conditional analytic approach, the case-combined-control design appeared to be more efficient and feasible than a classical case-control study for detecting interaction involving rare events. We now propose an unconditional analytic strategy to further increase the power for detecting gene-environment (GxE) interactions. This strategy allows the estimation of GxE interaction and exposure (E) main effects under certain assumptions (e.g. no correlation in E between siblings and the same exposure frequency in both control groups). Only the genetic (G) main effect cannot be estimated because it is biased. Using simulations, we show that unconditional logistic regression analysis is often more efficient than conditional analysis for detecting GxE interaction, particularly for a rare gene and strong effects. The unconditional analysis is also at least as efficient as the conditional analysis when the gene is common and the main and joint effects of E and G are small. Under the required assumptions, the unconditional analysis retains more information than does the conditional analysis for which only discordant case-control pairs are informative leading to more precise estimates of the odds ratios.

A novel approach to simulate gene-environment interactions in complex diseases.

PubMed

Amato, Roberto; Pinelli, Michele; D'Andrea, Daniel; Miele, Gennaro; Nicodemi, Mario; Raiconi, Giancarlo; Cocozza, Sergio

2010-01-05

Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge

Detecting regulatory gene-environment interactions with unmeasured environmental factors.

PubMed

Fusi, Nicoló; Lippert, Christoph; Borgwardt, Karsten; Lawrence, Neil D; Stegle, Oliver

2013-06-01

Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits. In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it is not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability. and implementation: Software available at http://pmbio.github.io/envGPLVM/. Supplementary data are available at Bioinformatics online.

Gene-gene and gene-environment interactions defining lipid-related traits.

PubMed

Ordovás, José M; Robertson, Ruairi; Cléirigh, Ellen Ní

2011-04-01

Steps towards reducing chronic disease progression are continuously being taken through the form of genomic research. Studies over the last year have highlighted more and more polymorphisms, pathways and interactions responsible for metabolic disorders such as cardiovascular disease, obesity and dyslipidemia. Many of these chronic illnesses can be partially blamed by altered lipid metabolism, combined with individual genetic components. Critical evaluation and comparison of these recent studies is essential in order to comprehend the results, conclusions and future prospects in the field of genomics as a whole. Recent literature elucidates significant gene--diet and gene--environment interactions resulting in altered lipid metabolism, inflammation and other metabolic imbalances leading to cardiovascular disease and obesity. Epigenetic and epistatic interactions are now becoming more significantly associated with such disorders, as genomic research digs deeper into the complex nature of genetic individuality and heritability. The vast array of data collected from genome-wide association studies must now be empowered and explored through more complex interaction studies, using standardized methods and larger sample sizes. In doing so the etiology of chronic disease progression will be further understood.

Disentangling Gene-Environment Correlations and Interactions on Adolescent Depressive Symptoms

ERIC Educational Resources Information Center

Lau, Jennifer Y. F.; Eley, Thalia C.

2008-01-01

Background: Genetic risks for depression may be expressed through greater exposure towards environmental stressors (gene-environment correlation, rGE) and increased susceptibility to these stressors (gene-environment interaction, G x E). While these effects are often studied independently, evidence supports their co-occurrence on depression.…

The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits.

PubMed

Li, Shi; Mukherjee, Bhramar; Taylor, Jeremy M G; Rice, Kenneth M; Wen, Xiaoquan; Rice, John D; Stringham, Heather M; Boehnke, Michael

2014-07-01

With challenges in data harmonization and environmental heterogeneity across various data sources, meta-analysis of gene-environment interaction studies can often involve subtle statistical issues. In this paper, we study the effect of environmental covariate heterogeneity (within and between cohorts) on two approaches for fixed-effect meta-analysis: the standard inverse-variance weighted meta-analysis and a meta-regression approach. Akin to the results in Simmonds and Higgins (), we obtain analytic efficiency results for both methods under certain assumptions. The relative efficiency of the two methods depends on the ratio of within versus between cohort variability of the environmental covariate. We propose to use an adaptively weighted estimator (AWE), between meta-analysis and meta-regression, for the interaction parameter. The AWE retains full efficiency of the joint analysis using individual level data under certain natural assumptions. Lin and Zeng (2010a, b) showed that a multivariate inverse-variance weighted estimator retains full efficiency as joint analysis using individual level data, if the estimates with full covariance matrices for all the common parameters are pooled across all studies. We show consistency of our work with Lin and Zeng (2010a, b). Without sacrificing much efficiency, the AWE uses only univariate summary statistics from each study, and bypasses issues with sharing individual level data or full covariance matrices across studies. We compare the performance of the methods both analytically and numerically. The methods are illustrated through meta-analysis of interaction between Single Nucleotide Polymorphisms in FTO gene and body mass index on high-density lipoprotein cholesterol data from a set of eight studies of type 2 diabetes. © 2014 WILEY PERIODICALS, INC.

A Penalized Robust Method for Identifying Gene-Environment Interactions

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Xie, Yang; Ma, Shuangge

2015-01-01

In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model mis-specification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications. PMID:24616063

Gene-Environment Interactions in Schizophrenia: Review of Epidemiological Findings and Future Directions

PubMed Central

van Os, Jim; Rutten, Bart PF; Poulton, Richie

2008-01-01

Concern is building about high rates of schizophrenia in large cities, and among immigrants, cannabis users, and traumatized individuals, some of which likely reflects the causal influence of environmental exposures. This, in combination with very slow progress in the area of molecular genetics, has generated interest in more complicated models of schizophrenia etiology that explicitly posit gene-environment interactions (EU-GEI. European Network of Schizophrenia Networks for the Study of Gene Environment Interactions. Schizophrenia aetiology: do gene-environment interactions hold the key? [published online ahead of print April 25, 2008] Schizophr Res; S0920-9964(08) 00170–9). Although findings of epidemiological gene-environment interaction (G × E) studies are suggestive of widespread gene-environment interactions in the etiology of schizophrenia, numerous challenges remain. For example, attempts to identify gene-environment interactions cannot be equated with molecular genetic studies with a few putative environmental variables “thrown in”: G × E is a multidisciplinary exercise involving epidemiology, psychology, psychiatry, neuroscience, neuroimaging, pharmacology, biostatistics, and genetics. Epidemiological G × E studies using indirect measures of genetic risk in genetically sensitive designs have the advantage that they are able to model the net, albeit nonspecific, genetic load. In studies using direct molecular measures of genetic variation, a hypothesis-driven approach postulating synergistic effects between genes and environment impacting on a final common pathway, such as “sensitization” of mesolimbic dopamine neurotransmission, while simplistic, may provide initial focus and protection against the numerous false-positive and false-negative results that these investigations engender. Experimental ecogenetic approaches with randomized assignment may help to overcome some of the limitations of observational studies and allow for the additional

A Partial Least Square Approach for Modeling Gene-gene and Gene-environment Interactions When Multiple Markers Are Genotyped

PubMed Central

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C.

2008-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense SNPs in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches: the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey’s 1-df model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women’s Health Initiative (WHI), this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with BMI. PMID:18615621

A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.

PubMed

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C

2009-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.

A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

PubMed

Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

2015-09-01

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.

Measured Gene-by-Environment Interaction in Relation to Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Nigg, Joel; Nikolas, Molly; Burt, S. Alexandra

2010-01-01

Objective: To summarize and evaluate the state of knowledge regarding the role of measured gene-by-environment interactions in relation to attention-deficit/hyperactivity disorder. Method: A selective review of methodologic issues was followed by a systematic search for relevant articles on measured gene-by-environment interactions; the search…

Gene-environment interaction involving recently identified colorectal cancer susceptibility loci

PubMed Central

Kantor, Elizabeth D.; Hutter, Carolyn M.; Minnier, Jessica; Berndt, Sonja I.; Brenner, Hermann; Caan, Bette J.; Campbell, Peter T.; Carlson, Christopher S.; Casey, Graham; Chan, Andrew T.; Chang-Claude, Jenny; Chanock, Stephen J.; Cotterchio, Michelle; Du, Mengmeng; Duggan, David; Fuchs, Charles S.; Giovannucci, Edward L.; Gong, Jian; Harrison, Tabitha A.; Hayes, Richard B.; Henderson, Brian E.; Hoffmeister, Michael; Hopper, John L.; Jenkins, Mark A.; Jiao, Shuo; Kolonel, Laurence N.; Le Marchand, Loic; Lemire, Mathieu; Ma, Jing; Newcomb, Polly A.; Ochs-Balcom, Heather M.; Pflugeisen, Bethann M.; Potter, John D.; Rudolph, Anja; Schoen, Robert E.; Seminara, Daniela; Slattery, Martha L.; Stelling, Deanna L.; Thomas, Fridtjof; Thornquist, Mark; Ulrich, Cornelia M.; Warnick, Greg S.; Zanke, Brent W.; Peters, Ulrike; Hsu, Li; White, Emily

2014-01-01

BACKGROUND Genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) that are associated with risk of colorectal cancer (CRC). Prior research has evaluated the presence of gene-environment interaction involving the first 10 identified susceptibility loci, but little work has been conducted on interaction involving SNPs at recently identified susceptibility loci, including: rs10911251, rs6691170, rs6687758, rs11903757, rs10936599, rs647161, rs1321311, rs719725, rs1665650, rs3824999, rs7136702, rs11169552, rs59336, rs3217810, rs4925386, and rs2423279. METHODS Data on 9160 cases and 9280 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and Colon Cancer Family Registry (CCFR) were used to evaluate the presence of interaction involving the above-listed SNPs and sex, body mass index (BMI), alcohol consumption, smoking, aspirin use, post-menopausal hormone (PMH) use, as well as intake of dietary calcium, dietary fiber, dietary folate, red meat, processed meat, fruit, and vegetables. Interaction was evaluated using a fixed-effects meta-analysis of an efficient Empirical Bayes estimator, and permutation was used to account for multiple comparisons. RESULTS None of the permutation-adjusted p-values reached statistical significance. CONCLUSIONS The associations between recently identified genetic susceptibility loci and CRC are not strongly modified by sex, BMI, alcohol, smoking, aspirin, PMH use, and various dietary factors. IMPACT Results suggest no evidence of strong gene-environment interactions involving the recently identified 16 susceptibility loci for CRC taken one at a time. PMID:24994789

Modeling Gene-Environment Interactions With Quasi-Natural Experiments.

PubMed

Schmitz, Lauren; Conley, Dalton

2017-02-01

This overview develops new empirical models that can effectively document Gene × Environment (G×E) interactions in observational data. Current G×E studies are often unable to support causal inference because they use endogenous measures of the environment or fail to adequately address the nonrandom distribution of genes across environments, confounding estimates. Comprehensive measures of genetic variation are incorporated into quasi-natural experimental designs to exploit exogenous environmental shocks or isolate variation in environmental exposure to avoid potential confounders. In addition, we offer insights from population genetics that improve upon extant approaches to address problems from population stratification. Together, these tools offer a powerful way forward for G×E research on the origin and development of social inequality across the life course. © 2015 Wiley Periodicals, Inc.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

PubMed Central

Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia. PMID:27725886

The heritable basis of gene-environment interactions in cardiometabolic traits.

PubMed

Poveda, Alaitz; Chen, Yan; Brändström, Anders; Engberg, Elisabeth; Hallmans, Göran; Johansson, Ingegerd; Renström, Frida; Kurbasic, Azra; Franks, Paul W

2017-03-01

Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software. All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2 ) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.

Genotype-based association models of complex diseases to detect gene-gene and gene-environment interactions.

PubMed

Lobach, Iryna; Fan, Ruzong; Manga, Prashiela

A central problem in genetic epidemiology is to identify and rank genetic markers involved in a disease. Complex diseases, such as cancer, hypertension, diabetes, are thought to be caused by an interaction of a panel of genetic factors, that can be identified by markers, which modulate environmental factors. Moreover, the effect of each genetic marker may be small. Hence, the association signal may be missed unless a large sample is considered, or a priori biomedical data are used. Recent advances generated a vast variety of a priori information, including linkage maps and information about gene regulatory dependence assembled into curated pathway databases. We propose a genotype-based approach that takes into account linkage disequilibrium (LD) information between genetic markers that are in moderate LD while modeling gene-gene and gene-environment interactions. A major advantage of our method is that the observed genetic information enters a model directly thus eliminating the need to estimate haplotype-phase. Our approach results in an algorithm that is inexpensive computationally and does not suffer from bias induced by haplotype-phase ambiguity. We investigated our model in a series of simulation experiments and demonstrated that the proposed approach results in estimates that are nearly unbiased and have small variability. We applied our method to the analysis of data from a melanoma case-control study and investigated interaction between a set of pigmentation genes and environmental factors defined by age and gender. Furthermore, an application of our method is demonstrated using a study of Alcohol Dependence.

Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.

PubMed

Hutter, Carolyn M; Mechanic, Leah E; Chatterjee, Nilanjan; Kraft, Peter; Gillanders, Elizabeth M

2013-11-01

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors. © 2013 WILEY PERIODICALS, INC.

Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility

PubMed Central

Wei, Qingyi Wei

2012-01-01

Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted genome-wide gene–environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P-values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P-value for the most significant SNP rs13383928 was 2.17×10–6, which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54, located on 7q32.1 (P = 8.90×10–5). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene–asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk. Abbreviations:CIconfidence intervalEenvironmentFDRfalse discovery rateGgeneGSEAgene-set-enrichment analysisGWASgenome-wide association studiesi-GSEAimproved gene-set-enrichment analysis approachORodds ratioSNPsingle nucleotide polymorphism PMID:22637743

Gender specific gene-environment interactions on laboratory-assessed aggression.

PubMed

Verona, Edelyn; Joiner, Thomas E; Johnson, Frank; Bender, Theodore W

2006-01-01

We examined gene-environment interactive effects on aggressive behavior among men and women genotyped (short versus long alleles) for the serotonin transporter gene. Aggressive behavior was indexed via a laboratory paradigm that measured the intensity and duration of shocks delivered to a putative "employee". Half of the participants were exposed to a physical stressor during the procedure (stress) and half were not (no-stress). Participants' physiological responses were gauged via acoustic startle eyeblink reactions (startle reactivity). Results were that men with the homozygous short (s/s) genotype showed increased aggression only under stress, whereas women and men carrying the long allele did not show differences in aggression in stress versus no-stress. However, although stress exposure produced increases in startle reactivity, there were no genotype or gender differences in physiology. These results replicate longitudinal research findings confirming the interactive effects of genes and environment on behavioral reactivity and on the development of externalizing psychopathological syndromes, at least in men.

Gene-gene, gene-environment, gene-nutrient interactions and single nucleotide polymorphisms of inflammatory cytokines.

PubMed

Nadeem, Amina; Mumtaz, Sadaf; Naveed, Abdul Khaliq; Aslam, Muhammad; Siddiqui, Arif; Lodhi, Ghulam Mustafa; Ahmad, Tausif

2015-05-15

Inflammation plays a significant role in the etiology of type 2 diabetes mellitus (T2DM). The rise in the pro-inflammatory cytokines is the essential step in glucotoxicity and lipotoxicity induced mitochondrial injury, oxidative stress and beta cell apoptosis in T2DM. Among the recognized markers are interleukin (IL)-6, IL-1, IL-10, IL-18, tissue necrosis factor-alpha (TNF-α), C-reactive protein, resistin, adiponectin, tissue plasminogen activator, fibrinogen and heptoglobins. Diabetes mellitus has firm genetic and very strong environmental influence; exhibiting a polygenic mode of inheritance. Many single nucleotide polymorphisms (SNPs) in various genes including those of pro and anti-inflammatory cytokines have been reported as a risk for T2DM. Not all the SNPs have been confirmed by unifying results in different studies and wide variations have been reported in various ethnic groups. The inter-ethnic variations can be explained by the fact that gene expression may be regulated by gene-gene, gene-environment and gene-nutrient interactions. This review highlights the impact of these interactions on determining the role of single nucleotide polymorphism of IL-6, TNF-α, resistin and adiponectin in pathogenesis of T2DM.

Gene-environment interaction on neural mechanisms of orthographic processing in Chinese children

PubMed Central

Su, Mengmeng; Wang, Jiuju; Maurer, Urs; Zhang, Yuping; Li, Jun; McBride-Chang, Catherine; Tardif, Twila; Liu, Youyi; Shu, Hua

2015-01-01

The ability to process and identify visual words requires efficient orthographic processing of print, consisting of letters in alphabetic languages or characters in Chinese. The N170 is a robust neural marker for orthographic processes. Both genetic and environmental factors, such as home literacy, have been shown to influence orthographic processing at the behavioral level, but their relative contributions and interactions are not well understood. The present study aimed to reveal possible gene-by-environment interactions on orthographic processing at the behavioral and neural level in a normal children sample. Sixty 12 year old Chinese children from a 10-year longitudinal sample underwent an implicit visual-word color decision task on real words and stroke combinations. The ERP analysis focused on the increase of the occipito-temporal N170 to words compared to stroke combinations. The genetic analysis focused on two SNPs (rs1419228, rs1091047) in the gene DCDC2 based on previous findings linking these 2 SNPs to orthographic coding. Home literacy was measured previously as the number of children's books at home, when the children were at the age of 3. Relative to stroke combinations, real words evoked greater N170 in bilateral posterior brain regions. A significant interaction between rs1091047 and home literacy was observed on the changes of N170 comparing real words to stroke combinations in the left hemisphere. Particularly, children carrying the major allele “G” showed a similar N170 effect irrespective of their environment, while children carrying the minor allele “C” showed a smaller N170 effect in low home-literacy environment than those in good environment. PMID:26294811

Antisocial Peer Affiliation and Externalizing Disorders: Evidence for Gene × Environment × Development Interaction

PubMed Central

Samek, Diana R.; Hicks, Brian M.; Keyes, Margaret A.; Iacono, William G.; McGue, Matt

2016-01-01

Gene × environment interaction contributes to externalizing disorders in adolescence, but little is known about whether such effects are long-lasting or present in adulthood. We examined gene-environment interplay in the concurrent and prospective associations between antisocial peer affiliation and externalizing disorders (antisocial behavior and substance use disorders) at ages 17, 20, 24, and 29. The sample included 1,382 same-sex twin pairs participating in the Minnesota Twin Family Study. We detected a gene × environment interaction at age 17, such that additive genetic influences on antisocial behavior and substance use disorders were greater in the context of greater antisocial peer affiliation. This gene × environment interaction was not present for antisocial behavior symptoms after age 17, but was for substance use disorder symptoms through age 29 (though effect sizes were largest at age 17). Results suggest adolescence is a critical period for the development of externalizing disorders wherein exposure to greater environmental adversity is associated with a greater expression of genetic risk. This form of gene × environment interaction may persist through young adulthood for substance use disorders, but is limited to adolescence for antisocial behavior. PMID:27580681

Antisocial peer affiliation and externalizing disorders: Evidence for Gene × Environment × Development interaction.

PubMed

Samek, Diana R; Hicks, Brian M; Keyes, Margaret A; Iacono, William G; McGue, Matt

2017-02-01

Gene × Environment interaction contributes to externalizing disorders in childhood and adolescence, but little is known about whether such effects are long lasting or present in adulthood. We examined gene-environment interplay in the concurrent and prospective associations between antisocial peer affiliation and externalizing disorders (antisocial behavior and substance use disorders) at ages 17, 20, 24, and 29. The sample included 1,382 same-sex twin pairs participating in the Minnesota Twin Family Study. We detected a Gene × Environment interaction at age 17, such that additive genetic influences on antisocial behavior and substance use disorders were greater in the context of greater antisocial peer affiliation. This Gene × Environment interaction was not present for antisocial behavior symptoms after age 17, but it was for substance use disorder symptoms through age 29 (though effect sizes were largest at age 17). The results suggest adolescence is a critical period for the development of externalizing disorders wherein exposure to greater environmental adversity is associated with a greater expression of genetic risk. This form of Gene × Environment interaction may persist through young adulthood for substance use disorders, but it appears to be limited to adolescence for antisocial behavior.

A combination test for detection of gene-environment interaction in cohort studies.

PubMed

Coombes, Brandon; Basu, Saonli; McGue, Matt

2017-07-01

Identifying gene-environment (G-E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G-E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome-wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene. We extend recently developed score statistic based genetic association testing approaches to the G-E interaction testing problem. We also propose tests for interaction using gene-based summary measures that pool variants together. Although it has recently been shown that these summary measures can be biased and may lead to inflated type I error, we show that under several realistic scenarios, we can still provide valid tests of interaction. These tests use significantly less degrees of freedom and thus can have much higher power to detect interaction. Additionally, we demonstrate that the iSeq-aSum-min test, which combines a gene-based summary measure test, iSeq-aSum-G, and an interaction-based summary measure test, iSeq-aSum-I, provides a powerful alternative to test G-E interaction. We demonstrate the performance of these approaches using simulation studies and illustrate their performance to study interaction between the SNPs in several candidate genes and family climate environment on alcohol consumption using the Minnesota Center for Twin and Family Research dataset. © 2017 WILEY PERIODICALS, INC.

A Combinatorial Approach to Detecting Gene-Gene and Gene-Environment Interactions in Family Studies

PubMed Central

Lou, Xiang-Yang; Chen, Guo-Bo; Yan, Lei; Ma, Jennie Z.; Mangold, Jamie E.; Zhu, Jun; Elston, Robert C.; Li, Ming D.

2008-01-01

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G × G) and gene-environment (G × E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G × G and G × E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G × G and G × E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence. PMID:18834969

Finding gene-environment interactions for phobias.

PubMed

Gregory, Alice M; Lau, Jennifer Y F; Eley, Thalia C

2008-03-01

Phobias are common disorders causing a great deal of suffering. Studies of gene-environment interaction (G x E) have revealed much about the complex processes underlying the development of various psychiatric disorders but have told us little about phobias. This article describes what is already known about genetic and environmental influences upon phobias and suggests how this information can be used to optimise the chances of discovering G x Es for phobias. In addition to the careful conceptualisation of new studies, it is suggested that data already collected should be re-analysed in light of increased understanding of processes influencing phobias.

Gene-environment interaction and male reproductive function

PubMed Central

Axelsson, Jonatan; Bonde, Jens Peter; Giwercman, Yvonne L.; Rylander, Lars; Giwercman, Aleksander

2010-01-01

As genetic factors can hardly explain the changes taking place during short time spans, environmental and lifestyle-related factors have been suggested as the causes of time-related deterioration of male reproductive function. However, considering the strong heterogeneity of male fecundity between and within populations, genetic variants might be important determinants of the individual susceptibility to the adverse effects of environment or lifestyle. Although the possible mechanisms of such interplay in relation to the reproductive system are largely unknown, some recent studies have indicated that specific genotypes may confer a larger risk of male reproductive disorders following certain exposures. This paper presents a critical review of animal and human evidence on how genes may modify environmental effects on male reproductive function. Some examples have been found that support this mechanism, but the number of studies is still limited. This type of interaction studies may improve our understanding of normal physiology and help us to identify the risk factors to male reproductive malfunction. We also shortly discuss other aspects of gene-environment interaction specifically associated with the issue of reproduction, namely environmental and lifestyle factors as the cause of sperm DNA damage. It remains to be investigated to what extent such genetic changes, by natural conception or through the use of assisted reproductive techniques, are transmitted to the next generation, thereby causing increased morbidity in the offspring. PMID:20348940

Gene and environment interaction: is the differential susceptibility hypothesis relevant for obesity?

PubMed Central

Dalle Molle, Roberta; Fatemi, Hajar; Dagher, Alain; Levitan, Robert D.; Silveira, Patricia P.; Dubé, Laurette

2017-01-01

The differential susceptibility model states that a given genetic variant is associated with an increased risk of pathology in negative environments but greater than average resilience in enriched ones. While this theory was first implemented in psychiatric-genetic research, it may also help us to unravel the complex ways that genes and environments interact to influence feeding behavior and obesity. We reviewed evidence on gene vs. environment interactions that influence obesity development, aiming to support the applicability of the differential susceptibility model for this condition, and propose that various environmental “layers” relevant for human development should be considered when bearing the differential susceptibility model in mind. Mother-child relationship, socioeconomic status and individual's response are important modifiers of BMI and food intake when interacting with gene variants, “for better and for worse”. While only a few studies to date have investigated obesity outcomes using this approach, we propose that the differential susceptibility hypothesis is in fact highly applicable to the study of genetic and environmental influences on feeding behavior and obesity risk. PMID:28024828

Leveraging Gene-Environment Interactions and Endotypes for Asthma Gene Discovery

PubMed Central

Bønnelykke, Klaus; Ober, Carole

2016-01-01

Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs and the advantages of studying responses to asthma-associated exposures in clinical birth cohorts, as well as in cell models of GEIs, to dissect the context-specific nature of genotypic risks, to prioritize variants in genome-wide association studies, and to identify pathways involved in pathogenesis in subgroups of patients. We propose that such approaches, in spite of their many challenges, present great opportunities for better understanding of asthma pathogenesis and heterogeneity and, ultimately, for improving prevention and treatment of disease. PMID:26947980

On meta- and mega-analyses for gene-environment interactions.

PubMed

Huang, Jing; Liu, Yulun; Vitale, Steve; Penning, Trevor M; Whitehead, Alexander S; Blair, Ian A; Vachani, Anil; Clapper, Margie L; Muscat, Joshua E; Lazarus, Philip; Scheet, Paul; Moore, Jason H; Chen, Yong

2017-12-01

Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a "mega" data set, which can be fit by a joint "mega-analysis." Alternatively, analyses can be done at each site, and results across sites can be combined through a "meta-analysis" procedure without integrating individual level data across sites. Although mega-analysis has been advocated in several recent initiatives, meta-analysis has the advantages of simplicity and feasibility, and has recently led to several important findings in identifying main genetic effects. In this paper, we conducted empirical and simulation studies, using data from a G × E study of lung cancer, to compare the mega- and meta-analyses in four commonly used G × E analyses under the scenario that the number of studies is small and sample sizes of individual studies are relatively large. We compared the two data integration approaches in the context of fixed effect models and random effects models separately. Our investigations provide valuable insights in understanding the differences between mega- and meta-analyses in practice of combining small number of studies in identifying G × E interactions. © 2017 WILEY PERIODICALS, INC.

An Efficient Test for Gene-Environment Interaction in Generalized Linear Mixed Models with Family Data.

PubMed

Mazo Lopera, Mauricio A; Coombes, Brandon J; de Andrade, Mariza

2017-09-27

Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma ( PPARG ) gene associated with diabetes.

Confirmatory and Competitive Evaluation of Alternative Gene-Environment Interaction Hypotheses

ERIC Educational Resources Information Center

Belsky, Jay; Pluess, Michael; Widaman, Keith F.

2013-01-01

Background: Most gene-environment interaction (GXE) research, though based on clear, vulnerability-oriented hypotheses, is carried out using exploratory rather than hypothesis-informed statistical tests, limiting power and making formal evaluation of competing GXE propositions difficult. Method: We present and illustrate a new regression technique…

Effect of occupational exposures on lung cancer susceptibility: a study of gene-environment interaction analysis.

PubMed

Malhotra, Jyoti; Sartori, Samantha; Brennan, Paul; Zaridze, David; Szeszenia-Dabrowska, Neonila; Świątkowska, Beata; Rudnai, Peter; Lissowska, Jolanta; Fabianova, Eleonora; Mates, Dana; Bencko, Vladimir; Gaborieau, Valerie; Stücker, Isabelle; Foretova, Lenka; Janout, Vladimir; Boffetta, Paolo

2015-03-01

Occupational exposures are known risk factors for lung cancer. Role of genetically determined host factors in occupational exposure-related lung cancer is unclear. We used genome-wide association (GWA) data from a case-control study conducted in 6 European countries from 1998 to 2002 to identify gene-occupation interactions and related pathways for lung cancer risk. GWA analysis was performed for each exposure using logistic regression and interaction term for genotypes, and exposure was included in this model. Both SNP-based and gene-based interaction P values were calculated. Pathway analysis was performed using three complementary methods, and analyses were adjusted for multiple comparisons. We analyzed 312,605 SNPs and occupational exposure to 70 agents from 1,802 lung cancer cases and 1,725 cancer-free controls. Mean age of study participants was 60.1 ± 9.1 years and 75% were male. Largest number of significant associations (P ≤ 1 × 10(-5)) at SNP level was demonstrated for nickel, brick dust, concrete dust, and cement dust, and for brick dust and cement dust at the gene-level (P ≤ 1 × 10(-4)). Approximately 14 occupational exposures showed significant gene-occupation interactions with pathways related to response to environmental information processing via signal transduction (P < 0.001 and FDR < 0.05). Other pathways that showed significant enrichment were related to immune processes and xenobiotic metabolism. Our findings suggest that pathways related to signal transduction, immune process, and xenobiotic metabolism may be involved in occupational exposure-related lung carcinogenesis. Our study exemplifies an integrative approach using pathway-based analysis to demonstrate the role of genetic variants in occupational exposure-related lung cancer susceptibility. Cancer Epidemiol Biomarkers Prev; 24(3); 570-9. ©2015 AACR. ©2015 American Association for Cancer Research.

Gene x Environment Interactions in Reading Disability and Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Pennington, Bruce F.; McGrath, Lauren M.; Rosenberg, Jenni; Barnard, Holly; Smith, Shelley D.; Willcutt, Erik G.; Friend, Angela; DeFries, John C.; Olson, Richard K.

2009-01-01

This article examines Gene x Environment (G x E) interactions in two comorbid developmental disorders--reading disability (RD) and attention-deficit/hyperactivity disorder (ADHD)--as a window on broader issues on G x E interactions in developmental psychology. The authors first briefly review types of G x E interactions, methods for detecting…

Boosting for detection of gene-environment interactions.

PubMed

Pashova, H; LeBlanc, M; Kooperberg, C

2013-01-30

In genetic association studies, it is typically thought that genetic variants and environmental variables jointly will explain more of the inheritance of a phenotype than either of these two components separately. Traditional methods to identify gene-environment interactions typically consider only one measured environmental variable at a time. However, in practice, multiple environmental factors may each be imprecise surrogates for the underlying physiological process that actually interacts with the genetic factors. In this paper, we develop a variant of L(2) boosting that is specifically designed to identify combinations of environmental variables that jointly modify the effect of a gene on a phenotype. Because the effect modifiers might have a small signal compared with the main effects, working in a space that is orthogonal to the main predictors allows us to focus on the interaction space. In a simulation study that investigates some plausible underlying model assumptions, our method outperforms the least absolute shrinkage and selection and Akaike Information Criterion and Bayesian Information Criterion model selection procedures as having the lowest test error. In an example for the Women's Health Initiative-Population Architecture using Genomics and Epidemiology study, the dedicated boosting method was able to pick out two single-nucleotide polymorphisms for which effect modification appears present. The performance was evaluated on an independent test set, and the results are promising. Copyright © 2012 John Wiley & Sons, Ltd.

Rigorous tests of gene-environment interactions in a lab study of the oxytocin receptor gene (OXTR), alcohol exposure, and aggression.

PubMed

LoParo, Devon; Johansson, Ada; Walum, Hasse; Westberg, Lars; Santtila, Pekka; Waldman, Irwin

2016-07-01

Naturalistic studies of gene-environment interactions (G X E) have been plagued by several limitations, including difficulty isolating specific environmental risk factors from other correlated aspects of the environment, gene-environment correlation (rGE ), and the use of a single genetic variant to represent the influence of a gene. We present results from 235 Finnish young men in two lab studies of aggression and alcohol challenge that attempt to redress these limitations of the extant G X E literature. Specifically, we use a latent variable modeling approach in an attempt to more fully account for genetic variation across the oxytocin receptor gene (OXTR) and to robustly test its main effects on aggression and its interaction with alcohol exposure. We also modeled aggression as a latent variable comprising various indices, including the average and maximum levels of aggression, the earliest trial on which aggression was expressed, and the proportion of trials on which the minimum and maximum levels of aggression were expressed. The best fitting model for the genetic variation across OXTR included six factors derived from an exploratory factor analysis, roughly corresponding to six haplotype blocks. Aggression levels were higher on trials in which participants were administered alcohol, won, or were provoked. There was a significant main effect of OXTR on aggression across studies after controlling for covariates. The interaction of OXTR and alcohol was also significant across studies, such that OXTR had stronger effects on aggression in the alcohol administration condition. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Bisphenol-A and Female Infertility: A Possible Role of Gene-Environment Interactions

PubMed Central

Huo, Xiaona; Chen, Dan; He, Yonghua; Zhu, Wenting; Zhou, Wei; Zhang, Jun

2015-01-01

Background: Bisphenol-A (BPA) is widely used and ubiquitous in the environment. Animal studies indicate that BPA affects reproduction, however, the gene-environment interaction mechanism(s) involved in this association remains unclear. We performed a literature review to summarize the evidence on this topic. Methods: A comprehensive search was conducted in PubMed using as keywords BPA, gene, infertility and female reproduction. Full-text articles in both human and animals published in English prior to December 2014 were selected. Results: Evidence shows that BPA can interfere with endocrine function of hypothalamic-pituitary axis, such as by changing gonadotropin-releasing hormones (GnRH) secretion in hypothalamus and promoting pituitary proliferation. Such actions affect puberty, ovulation and may even result in infertility. Ovary, uterus and other reproductive organs are also targets of BPA. BPA exposure impairs the structure and functions of female reproductive system in different times of life cycle and may contribute to infertility. Both epidemiological and experimental evidences demonstrate that BPA affects reproduction-related gene expression and epigenetic modification that are closely associated with infertility. The detrimental effects on reproduction may be lifelong and transgenerational. Conclusions: Evidence on gene-environment interactions, especially from human studies, is still limited. Further research on this topic is warranted. PMID:26371021

Gene-environment studies: any advantage over environmental studies?

PubMed

Bermejo, Justo Lorenzo; Hemminki, Kari

2007-07-01

Gene-environment studies have been motivated by the likely existence of prevalent low-risk genes that interact with common environmental exposures. The present study assessed the statistical advantage of the simultaneous consideration of genes and environment to investigate the effect of environmental risk factors on disease. In particular, we contemplated the possibility that several genes modulate the environmental effect. Environmental exposures, genotypes and phenotypes were simulated according to a wide range of parameter settings. Different models of gene-gene-environment interaction were considered. For each parameter combination, we estimated the probability of detecting the main environmental effect, the power to identify the gene-environment interaction and the frequency of environmentally affected individuals at which environmental and gene-environment studies show the same statistical power. The proportion of cases in the population attributable to the modeled risk factors was also calculated. Our data indicate that environmental exposures with weak effects may account for a significant proportion of the population prevalence of the disease. A general result was that, if the environmental effect was restricted to rare genotypes, the power to detect the gene-environment interaction was higher than the power to identify the main environmental effect. In other words, when few individuals contribute to the overall environmental effect, individual contributions are large and result in easily identifiable gene-environment interactions. Moreover, when multiple genes interacted with the environment, the statistical benefit of gene-environment studies was limited to those studies that included major contributors to the gene-environment interaction. The advantage of gene-environment over plain environmental studies also depends on the inheritance mode of the involved genes, on the study design and, to some extend, on the disease prevalence.

The case-only test for gene-environment interaction is not uniformly powerful: an empirical example

PubMed Central

Wu, Chen; Chang, Jiang; Ma, Baoshan; Miao, Xiaoping; Zhou, Yifeng; Liu, Yu; Li, Yun; Wu, Tangchun; Hu, Zhibin; Shen, Hongbing; Jia, Weihua; Zeng, Yixin; Lin, Dongxin; Kraft, Peter

2016-01-01

The case-only test has been proposed as a more powerful approach to detect gene-environment (G×E) interactions. This approach assumes that the genetic and environmental factors are independent. While it is well known that Type I error rate will increase if this assumption is violated, it is less widely appreciated that gene-environment correlation can also lead to power loss. We illustrate this phenomenon by comparing the performance of the case-only test to other approaches to detect G×E interactions in a genome-wide association study of esophageal squamous carcinoma (ESCC) in Chinese populations. Some of these approaches do not use information on the correlation between exposure and genotype (standard logistic regression), while others seek to use this information in a robust fashion to boost power without increasing Type I error (two-step, empirical Bayes and cocktail methods). G×E interactions were identified involving drinking status and two regions containing genes in the alcohol metabolism pathway, 4q23 and 12q24. Although the case-only test yielded the most significant tests of G×E interaction in the 4q23 region, the case-only test failed to identify significant interactions in the 12q24 region which were readily identified using other approaches. The low power of the case-only test in the 12q24 region is likely due to the strong inverse association between the SNPs in this region and drinking status. This example underscores the need to consider multiple approaches to detect gene-environment interactions, as different tests are more or less sensitive to different alternative hypotheses and violations of the gene-environment independence assumption. PMID:23595356

Gene-Environment Interactions in Genome-Wide Association Studies: Current Approaches and New Directions

ERIC Educational Resources Information Center

Winham, Stacey J.; Biernacka, Joanna M.

2013-01-01

Background: Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized…

[From stone-craved genes to Michelangelo: significance and different aspects of gene-environment interaction].

PubMed

Lazary, Judit

2017-12-01

Although genetic studies have improved a lot in recent years, without clinical relevance sometimes their significance is devalued. Reviewing the major milestones of psychogenomics it can be seen that break-through success is just a question of time. Investigations of direct effect of genetic variants on phenotypes have not yielded positive findings. However, an important step was taken by adapting the gene-environment interaction model. In this model genetic vulnerability stepped into the place of "stone craved" pathology. Further progress happened when studies of environmental factors were combined with genetic function (epigenetics). This model provided the possibility for investigation of therapeutic interventions as environmental factors and it was proven that effective treatments exert a modifying effect on gene expression. Moreover, recent developments focus on therapeutic manipulation of gene function (e.g. chemogenetics). Instead of "stone craved" genes up-to-date dynamically interacting gene function became the basis of psychogenomics in which correction of the expression is a potential therapeutic tool. Keeping in mind these trends and developments, there is no doubt that genetics will be a fundamental part of daily clinical routine in the future.

Three Approaches to Modeling Gene-Environment Interactions in Longitudinal Family Data: Gene-Smoking Interactions in Blood Pressure.

PubMed

Basson, Jacob; Sung, Yun Ju; de Las Fuentes, Lisa; Schwander, Karen L; Vazquez, Ana; Rao, Dabeeru C

2016-01-01

Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene-smoking interactions have detected novel BP loci in cross-sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene-smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree-based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single-visit analyses. The pedigree-based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene-smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency <1% were included in the analysis. The choice of analysis method should depend on the model and the structure and complexity of the familial and longitudinal data. © 2015 WILEY PERIODICALS, INC.

Refining the Candidate Environment: Interpersonal Stress, the Serotonin Transporter Polymorphism, and Gene-Environment Interactions in Major Depression.

PubMed

Vrshek-Schallhorn, Suzanne; Mineka, Susan; Zinbarg, Richard E; Craske, Michelle G; Griffith, James W; Sutton, Jonathan; Redei, Eva E; Wolitzky-Taylor, Kate; Hammen, Constance; Adam, Emma K

2014-05-01

Meta-analytic evidence supports a gene-environment (G×E) interaction between life stress and the serotonin transporter polymorphism (5-HTTLPR) on depression, but few studies have examined factors that influence detection of this effect, despite years of inconsistent results. We propose that the "candidate environment" (akin to a candidate gene) is key. Theory and evidence implicate major stressful life events (SLEs)-particularly major interpersonal SLEs-as well as chronic family stress. Participants ( N = 400) from the Youth Emotion Project (which began with 627 high school juniors oversampled for high neuroticism) completed up to five annual diagnostic and life stress interviews and provided DNA samples. A significant G×E effect for major SLEs and S -carrier genotype was accounted for significantly by major interpersonal SLEs but not significantly by major non-interpersonal SLEs. S -carrier genotype and chronic family stress also significantly interacted. Identifying such candidate environments may facilitate future G×E research in depression and psychopathology more broadly.

Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status

PubMed Central

Karlsson, Torgny; Ek, Weronica E.

2017-01-01

Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.45*10−29, p = 3.83*10−26, p = 4.66*10−11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount resulted in a significant interaction. The FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after omitting the FTO locus from the genetic score. Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score. However, the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors. PMID:28873402

Nature versus nurture: A systematic approach to elucidate gene-environment interactions in the development of myopic refractive errors.

PubMed

Miraldi Utz, Virginia

2017-01-01

Myopia is the most common eye disorder and major cause of visual impairment worldwide. As the incidence of myopia continues to rise, the need to further understand the complex roles of molecular and environmental factors controlling variation in refractive error is of increasing importance. Tkatchenko and colleagues applied a systematic approach using a combination of gene set enrichment analysis, genome-wide association studies, and functional analysis of a murine model to identify a myopia susceptibility gene, APLP2. Differential expression of refractive error was associated with time spent reading for those with low frequency variants in this gene. This provides support for the longstanding hypothesis of gene-environment interactions in refractive error development.

Gene-environment interactions in the aetiology of systemic lupus erythematosus.

PubMed

Jönsen, Andreas; Bengtsson, Anders A; Nived, Ola; Truedsson, Lennart; Sturfelt, Gunnar

2007-12-01

Systemic lupus erythematosus (SLE) is a disease that displays a multitude of symptoms and a vast array of autoantibodies. The disease course may vary substantially between patients. The current understanding of SLE aetiology includes environmental factors acting on a genetically prone individual during an undetermined time period resulting in autoimmunity and finally surpassing that individual's disease threshold. Genetic differences and environmental factors may interact specifically in the pathogenetic processes and may influence disease development and modify the disease course. Identification of these factors and their interactions in the pathogenesis of SLE is vital in understanding the disease and may contribute to identify new treatment targets and perhaps also aid in disease prevention. However, there are several problems that need to be overcome, such as the protracted time frame of environmental influence, time dependent epigenetic alterations and the possibility that different pathogenetic pathways may result in a similar disease phenotype. This is mirrored by the relatively few studies that suggest specific gene-environment interactions. These include an association between SLE diagnosis and glutation S-transferase gene variants combined with occupational sun exposure as well as variants of the N-acetyl transferase gene in combination with either aromatic amine exposure or hydralazine. With increased knowledge on SLE pathogenesis, the role of environmental factors and their genetic interactions may be further elucidated.

Gene environment interaction studies in depression and suicidal behavior: An update.

PubMed

Mandelli, Laura; Serretti, Alessandro

2013-12-01

Increasing evidence supports the involvement of both heritable and environmental risk factors in major depression (MD) and suicidal behavior (SB). Studies investigating gene-environment interaction (G × E) may be useful for elucidating the role of biological mechanisms in the risk for mental disorders. In the present paper, we review the literature regarding the interaction between genes modulating brain functions and stressful life events in the etiology of MD and SB and discuss their potential added benefit compared to genetic studies only. Within the context of G × E investigation, thus far, only a few reliable results have been obtained, although some genes have consistently shown interactive effects with environmental risk in MD and, to a lesser extent, in SB. Further investigation is required to disentangle the direct and mediated effects that are common or specific to MD and SB. Since traditional G × E studies overall suffer from important methodological limitations, further effort is required to develop novel methodological strategies with an interdisciplinary approach. Copyright © 2013 Elsevier Ltd. All rights reserved.

Culture as a mediator of gene-environment interaction: Cultural consonance, childhood adversity, a 2A serotonin receptor polymorphism, and depression in urban Brazil.

PubMed

Dressler, William W; Balieiro, Mauro C; Ferreira de Araújo, Luiza; Silva, Wilson A; Ernesto Dos Santos, José

2016-07-01

Research on gene-environment interaction was facilitated by breakthroughs in molecular biology in the late 20th century, especially in the study of mental health. There is a reliable interaction between candidate genes for depression and childhood adversity in relation to mental health outcomes. The aim of this paper is to explore the role of culture in this process in an urban community in Brazil. The specific cultural factor examined is cultural consonance, or the degree to which individuals are able to successfully incorporate salient cultural models into their own beliefs and behaviors. It was hypothesized that cultural consonance in family life would mediate the interaction of genotype and childhood adversity. In a study of 402 adult Brazilians from diverse socioeconomic backgrounds, conducted from 2011 to 2014, the interaction of reported childhood adversity and a polymorphism in the 2A serotonin receptor was associated with higher depressive symptoms. Further analysis showed that the gene-environment interaction was mediated by cultural consonance in family life, and that these effects were more pronounced in lower social class neighborhoods. The findings reinforce the role of the serotonergic system in the regulation of stress response and learning and memory, and how these processes in turn interact with environmental events and circumstances. Furthermore, these results suggest that gene-environment interaction models should incorporate a wider range of environmental experience and more complex pathways to better understand how genes and the environment combine to influence mental health outcomes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

PubMed

Song, Minsun; Wheeler, William; Caporaso, Neil E; Landi, Maria Teresa; Chatterjee, Nilanjan

2018-03-01

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package. © 2017 WILEY PERIODICALS, INC.

Effects of the Family Environment: Gene-Environment Interaction and Passive Gene-Environment Correlation

ERIC Educational Resources Information Center

Price, Thomas S.; Jaffee, Sara R.

2008-01-01

The classical twin study provides a useful resource for testing hypotheses about how the family environment influences children's development, including how genes can influence sensitivity to environmental effects. However, existing statistical models do not account for the possibility that children can inherit exposure to family environments…

Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting.

PubMed

Zhao, Wei; Ware, Erin B; He, Zihuai; Kardia, Sharon L R; Faul, Jessica D; Smith, Jennifer A

2017-09-29

Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms) modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS). In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs) within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS). Childhood socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488) by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA) ( p = 0.07).

Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting

PubMed Central

Zhao, Wei; He, Zihuai; Kardia, Sharon L. R.; Faul, Jessica D.

2017-01-01

Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms) modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS). In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs) within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS). Childhood socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488) by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA) (p = 0.07). PMID:28961216

Gene by Environment Interaction and Resilience: Effects of Child Maltreatment and Serotonin, Corticotropin Releasing Hormone, Dopamine, and Oxytocin Genes

PubMed Central

Cicchetti, Dante; Rogosch, Fred A.

2013-01-01

In this investigation, gene-environment interaction effects in predicting resilience in adaptive functioning among maltreated and nonmaltreated low-income children (N = 595) were examined. A multi-component index of resilient functioning was derived and levels of resilient functioning were identified. Variants in four genes, 5-HTTLPR, CRHR1, DRD4 -521C/T, and OXTR, were investigated. In a series of ANCOVAs, child maltreatment demonstrated a strong negative main effect on children’s resilient functioning, whereas no main effects for any of the genotypes of the respective genes were found. However, gene-environment interactions involving genotypes of each of the respective genes and maltreatment status were obtained. For each respective gene, among children with a specific genotype, the relative advantage in resilient functioning of nonmaltreated compared to maltreated children was stronger than was the case for nonmaltreated and maltreated children with other genotypes of the respective gene. Across the four genes, a composite of the genotypes that more strongly differentiated resilient functioning between nonmaltreated and maltreated children provided further evidence of genetic variations influencing resilient functioning in nonmaltreated children, whereas genetic variation had a negligible effect on promoting resilience among maltreated children. Additional effects were observed for children based on the number of subtypes of maltreatment children experienced, as well as for abuse and neglect subgroups. Finally, maltreated and nonmaltreated children with high levels of resilience differed in their average number of differentiating genotypes. These results suggest that differential resilient outcomes are based on the interaction between genes and developmental experiences. PMID:22559122

Exposure enriched outcome dependent designs for longitudinal studies of gene-environment interaction.

PubMed

Sun, Zhichao; Mukherjee, Bhramar; Estes, Jason P; Vokonas, Pantel S; Park, Sung Kyun

2017-08-15

Joint effects of genetic and environmental factors have been increasingly recognized in the development of many complex human diseases. Despite the popularity of case-control and case-only designs, longitudinal cohort studies that can capture time-varying outcome and exposure information have long been recommended for gene-environment (G × E) interactions. To date, literature on sampling designs for longitudinal studies of G × E interaction is quite limited. We therefore consider designs that can prioritize a subsample of the existing cohort for retrospective genotyping on the basis of currently available outcome, exposure, and covariate data. In this work, we propose stratified sampling based on summaries of individual exposures and outcome trajectories and develop a full conditional likelihood approach for estimation that adjusts for the biased sample. We compare the performance of our proposed design and analysis with combinations of different sampling designs and estimation approaches via simulation. We observe that the full conditional likelihood provides improved estimates for the G × E interaction and joint exposure effects over uncorrected complete-case analysis, and the exposure enriched outcome trajectory dependent design outperforms other designs in terms of estimation efficiency and power for detection of the G × E interaction. We also illustrate our design and analysis using data from the Normative Aging Study, an ongoing longitudinal cohort study initiated by the Veterans Administration in 1963. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Genome-wide gene–gene interaction analysis for next-generation sequencing

PubMed Central

Zhao, Jinying; Zhu, Yun; Xiong, Momiao

2016-01-01

The critical barrier in interaction analysis for next-generation sequencing (NGS) data is that the traditional pairwise interaction analysis that is suitable for common variants is difficult to apply to rare variants because of their prohibitive computational time, large number of tests and low power. The great challenges for successful detection of interactions with NGS data are (1) the demands in the paradigm of changes in interaction analysis; (2) severe multiple testing; and (3) heavy computations. To meet these challenges, we shift the paradigm of interaction analysis between two SNPs to interaction analysis between two genomic regions. In other words, we take a gene as a unit of analysis and use functional data analysis techniques as dimensional reduction tools to develop a novel statistic to collectively test interaction between all possible pairs of SNPs within two genome regions. By intensive simulations, we demonstrate that the functional logistic regression for interaction analysis has the correct type 1 error rates and higher power to detect interaction than the currently used methods. The proposed method was applied to a coronary artery disease dataset from the Wellcome Trust Case Control Consortium (WTCCC) study and the Framingham Heart Study (FHS) dataset, and the early-onset myocardial infarction (EOMI) exome sequence datasets with European origin from the NHLBI's Exome Sequencing Project. We discovered that 6 of 27 pairs of significantly interacted genes in the FHS were replicated in the independent WTCCC study and 24 pairs of significantly interacted genes after applying Bonferroni correction in the EOMI study. PMID:26173972

A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects

PubMed Central

Marigorta, Urko M.; Gibson, Greg

2014-01-01

The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05–2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits. PMID:25101110

Interaction of rearing environment and reproductive tactic on gene expression profiles in Atlantic salmon

USGS Publications Warehouse

Aubin-Horth, N.; Letcher, B.H.; Hofmann, H.A.

2005-01-01

Organisms that share the same genotype can develop into divergent phenotypes, depending on environmental conditions. In Atlantic salmon, young males of the same age can be found either as sneakers or immature males that are future anadromous fish. Just as the organism-level phenotype varies between divergent male developmental trajectories, brain gene expression is expected to vary as well. We hypothesized that rearing environment can also have an important effect on gene expression in the brain and possibly interact with the reproductive tactic adopted. We tested this hypothesis by comparing brain gene expression profiles of the two male tactics in fish from the same population that were reared in either a natural stream or under laboratory conditions. We found that expression of certain genes was affected by rearing environment only, while others varied between male reproductive tactics independent of rearing environment. Finally, more than half of all genes that showed variable expression varied between the two male tactics only in one environment. Thus, in these fish, very different molecular pathways can give rise to similar macro-phenotypes depending on rearing environment. This result gives important insights into the molecular underpinnings of developmental plasticity in relationship to the environment. ?? 2005 The American Genetic Association.

Music training and speech perception: a gene-environment interaction.

PubMed

Schellenberg, E Glenn

2015-03-01

Claims of beneficial side effects of music training are made for many different abilities, including verbal and visuospatial abilities, executive functions, working memory, IQ, and speech perception in particular. Such claims assume that music training causes the associations even though children who take music lessons are likely to differ from other children in music aptitude, which is associated with many aspects of speech perception. Music training in childhood is also associated with cognitive, personality, and demographic variables, and it is well established that IQ and personality are determined largely by genetics. Recent evidence also indicates that the role of genetics in music aptitude and music achievement is much larger than previously thought. In short, music training is an ideal model for the study of gene-environment interactions but far less appropriate as a model for the study of plasticity. Children seek out environments, including those with music lessons, that are consistent with their predispositions; such environments exaggerate preexisting individual differences. © 2015 New York Academy of Sciences.

Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups.

PubMed

Shiao, S Pamela K; Grayson, James; Yu, Chong Ho; Wasek, Brandi; Bottiglieri, Teodoro

2018-02-16

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene-environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls ( p < 0.05), on MTHFR C677T , MTR A2756G , MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike's information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene-environment interactions in the prevention of CRC.

Examining Gene-Environment Interactions in Comorbid Depressive and Disruptive Behavior Disorders using a Bayesian Approach

PubMed Central

Adrian, Molly; Kiff, Cara; Glazner, Chris; Kohen, Ruth; Tracy, Julia Helen; Zhou, Chuan; McCauley, Elizabeth; Stoep, Ann Vander

2015-01-01

Objective The objective of this study was to apply a Bayesian statistical analytic approach that minimizes multiple testing problems to explore the combined effects of chronic low familial support and variants in 12 candidate genes on risk for a common and debilitating childhood mental health condition. Method Bayesian mixture modeling was used to examine gene by environment interactions among genetic variants and environmental factors (family support) associated in previous studies with the occurrence of comorbid depression and disruptive behavior disorders youth, using a sample of 255 children. Results One main effects, variants in the oxytocin receptor (OXTR, rs53576) was associated with increased risk for comorbid disorders. Two significant gene x environment and one signification gene x gene interaction emerged. Variants in the nicotinic acetylcholine receptor α5 subunit (CHRNA5, rs16969968) and in the glucocorticoid receptor chaperone protein FK506 binding protein 5 (FKBP5, rs4713902) interacted with chronic low family support in association with child mental health status. One gene x gene interaction, 5-HTTLPR variant of the serotonin transporter (SERT/SLC6A4) in combination with μ opioid receptor (OPRM1, rs1799971) was associated with comorbid depression and conduct problems. Conclusions Results indicate that Bayesian modeling is a feasible strategy for conducting behavioral genetics research. This approach, combined with an optimized genetic selection strategy (Vrieze, Iacono, & McGue, 2012), revealed genetic variants involved in stress regulation ( FKBP5, SERTxOPMR), social bonding (OXTR), and nicotine responsivity (CHRNA5) in predicting comorbid status. PMID:26228411

Gene-environment interactions in atherosclerosis.

PubMed

Hegele, R A

1991-06-01

It is becoming clear that genetic and environmental factors can interact to varying degrees in a given individual. In some cases, genetically determined resistance to CAD (eg, genetic hyperalpha- or hypobetalipoproteinemia), or genetically determined susceptibility to CAD (eg, high Lp[a] levels) may not be significantly modulated by a prudent lifestyle. Estimates of the prevalence in the general population of these genetic extremes average around 5% (4). In the remaining 95% of cases, nature and nurture interact. For example, a genetic flaw that is usually expressed phenotypically as premature death due to CAD (eg, some cases of FH) can be ameliorated by a prudent diet. There is little doubt that an individual's responsiveness to environmental factors can be determined by many different genes. The exact candidate genes and the nature of most of the genetic changes affecting response to diet still need to be determined. Once identified, they may one day form the basis for early diagnosis of metabolic problems and individually tailored diet and drug treatment programs.

Robust discovery of genetic associations incorporating gene-environment interaction and independence.

PubMed

Tchetgen Tchetgen, Eric

2011-03-01

This article considers the detection and evaluation of genetic effects incorporating gene-environment interaction and independence. Whereas ordinary logistic regression cannot exploit the assumption of gene-environment independence, the proposed approach makes explicit use of the independence assumption to improve estimation efficiency. This method, which uses both cases and controls, fits a constrained retrospective regression in which the genetic variant plays the role of the response variable, and the disease indicator and the environmental exposure are the independent variables. The regression model constrains the association of the environmental exposure with the genetic variant among the controls to be null, thus explicitly encoding the gene-environment independence assumption, which yields substantial gain in accuracy in the evaluation of genetic effects. The proposed retrospective regression approach has several advantages. It is easy to implement with standard software, and it readily accounts for multiple environmental exposures of a polytomous or of a continuous nature, while easily incorporating extraneous covariates. Unlike the profile likelihood approach of Chatterjee and Carroll (Biometrika. 2005;92:399-418), the proposed method does not require a model for the association of a polytomous or continuous exposure with the disease outcome, and, therefore, it is agnostic to the functional form of such a model and completely robust to its possible misspecification.

Shame and Guilt-Proneness in Adolescents: Gene-Environment Interactions.

PubMed

Szentágotai-Tătar, Aurora; Chiș, Adina; Vulturar, Romana; Dobrean, Anca; Cândea, Diana Mirela; Miu, Andrei C

2015-01-01

Rooted in people's preoccupation with how they are perceived and evaluated, shame and guilt are self-conscious emotions that play adaptive roles in social behavior, but can also contribute to psychopathology when dysregulated. Shame and guilt-proneness develop during childhood and adolescence, and are influenced by genetic and environmental factors that are little known to date. This study investigated the effects of early traumatic events and functional polymorphisms in the brain-derived neurotrophic factor (BDNF) gene and the serotonin transporter gene promoter (5-HTTLPR) on shame and guilt in adolescents. A sample of N = 271 healthy adolescents between 14 and 17 years of age filled in measures of early traumatic events and proneness to shame and guilt, and were genotyped for the BDNF Val66Met and 5-HTTLPR polymorphisms. Results of moderator analyses indicated that trauma intensity was positively associated with guilt-proneness only in carriers of the low-expressing Met allele of BDNF Val66Met. This is the first study that identifies a gene-environment interaction that significantly contributes to guilt proneness in adolescents, with potential implications for developmental psychopathology.

Testing in semiparametric models with interaction, with applications to gene-environment interactions.

PubMed

Maity, Arnab; Carroll, Raymond J; Mammen, Enno; Chatterjee, Nilanjan

2009-01-01

Motivated from the problem of testing for genetic effects on complex traits in the presence of gene-environment interaction, we develop score tests in general semiparametric regression problems that involves Tukey style 1 degree-of-freedom form of interaction between parametrically and non-parametrically modelled covariates. We find that the score test in this type of model, as recently developed by Chatterjee and co-workers in the fully parametric setting, is biased and requires undersmoothing to be valid in the presence of non-parametric components. Moreover, in the presence of repeated outcomes, the asymptotic distribution of the score test depends on the estimation of functions which are defined as solutions of integral equations, making implementation difficult and computationally taxing. We develop profiled score statistics which are unbiased and asymptotically efficient and can be performed by using standard bandwidth selection methods. In addition, to overcome the difficulty of solving functional equations, we give easy interpretations of the target functions, which in turn allow us to develop estimation procedures that can be easily implemented by using standard computational methods. We present simulation studies to evaluate type I error and power of the method proposed compared with a naive test that does not consider interaction. Finally, we illustrate our methodology by analysing data from a case-control study of colorectal adenoma that was designed to investigate the association between colorectal adenoma and the candidate gene NAT2 in relation to smoking history.

Review of the Gene-Environment Interaction Literature in Cancer: What Do We Know?

PubMed

Simonds, Naoko I; Ghazarian, Armen A; Pimentel, Camilla B; Schully, Sheri D; Ellison, Gary L; Gillanders, Elizabeth M; Mechanic, Leah E

2016-07-01

Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature. To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed. A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., <0.05). In addition, the magnitude of interactions reported was modest. Observations of published

Review of the Gene-Environment Interaction Literature in Cancer: What do we know?

PubMed Central

Simonds, Naoko I.; Ghazarian, Armen A.; Pimentel, Camilla B.; Schully, Sheri D.; Ellison, Gary L.; Gillanders, Elizabeth M.; Mechanic, Leah E.

2016-01-01

Background Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment (GxE) interactions has been an active area of research, little is reported about the known findings in the literature. Methods To examine the state of the science in GxE research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed. Results A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined GxE in colon, rectal, or colorectal cancer types; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index (BMI), diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction p-value, of which a sizable number of p-values were considered statistically significant (i.e., < 0.05). In addition, the magnitudes of interactions reported

Genetics of Addiction: Future Focus on Gene × Environment Interaction?

PubMed

Vink, Jacqueline M

2016-09-01

The heritability of substance use is moderate to high. Successful efforts to find genetic variants associated with substance use (smoking, alcohol, cannabis) have been undertaken by large consortia. However, the proportion of phenotypic variance explained by the identified genetic variants is small. Interestingly, there is overlap between the genetic variants that influence different substances. Moreover, there are sets of "substance-specific" genes and sets of genes contributing to a "vulnerability for addictive behavior" in general. It is important to recognize that genes alone do not determine addiction phenotypes: Environmental factors such as parental monitoring, peer pressure, or socioeconomic status also play an important role. Despite a rich epidemiologic literature focused on the social determinants of substance use, few studies have examined the moderation of genetic influences like gene-environment (G × E) interactions. Understanding this balance may hold the key to understanding the individual differences in substance use, abuse, and addictive behavior. Recommendations for future research are described in this commentary and include increasing the power of G × E studies by using state-of-the-art methods such as polygenic risk scores instead of single genetic variants and taking genetic overlap between substances into account. Future genetic studies should also investigate environmental risk factors for addictive behavior more extensively to unravel the interaction between nature and nurture. Focusing on G × E interactions not only will give insight into the underlying biological mechanism but will also characterize subgroups (based on environmental factors) at high risk for addictive behaviors. With this information, we could bridge the gap between fundamental research and applications for society.

Gene-obesogenic environment interactions in the UK Biobank study.

PubMed

Tyrrell, Jessica; Wood, Andrew R; Ames, Ryan M; Yaghootkar, Hanieh; Beaumont, Robin N; Jones, Samuel E; Tuke, Marcus A; Ruth, Katherine S; Freathy, Rachel M; Davey Smith, George; Joost, Stéphane; Guessous, Idris; Murray, Anna; Strachan, David P; Kutalik, Zoltán; Weedon, Michael N; Frayling, Timothy M

2017-04-01

Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI). We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment. We found gene-environment interactions with TDI (Pinteraction = 3 × 10 -10 ), self-reported TV watching (Pinteraction = 7 × 10 -5 ) and self-reported physical activity (Pinteraction = 5 × 10 -6 ). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely. Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible. © The Author 2017. Published by Oxford University Press on behalf of the

How Gene-Environment Interaction Affects Children's Anxious and Fearful Behavior. Science Briefs

ERIC Educational Resources Information Center

National Scientific Council on the Developing Child, 2007

2007-01-01

"Science Briefs" summarize the findings and implications of a recent study in basic science or clinical research. This brief reports on the study "Evidence for a Gene-Environment Interaction in Predicting Behavioral Inhibition in Middle Childhood" (N. A. Fox, K E. Nichols, H. A. Henderson, K. Rubin, L. Schmidt, D. Hamer, M. Ernst, and D. S.…

The Genetic Architecture of Noise-Induced Hearing Loss: Evidence for a Gene-by-Environment Interaction.

PubMed

Lavinsky, Joel; Ge, Marshall; Crow, Amanda L; Pan, Calvin; Wang, Juemei; Salehi, Pezhman; Myint, Anthony; Eskin, Eleazar; Allayee, Hooman; Lusis, Aldons J; Friedman, Rick A

2016-10-13

The discovery of environmentally specific genetic effects is crucial to the understanding of complex traits, such as susceptibility to noise-induced hearing loss (NIHL). We describe the first genome-wide association study (GWAS) for NIHL in a large and well-characterized population of inbred mouse strains, known as the Hybrid Mouse Diversity Panel (HMDP). We recorded auditory brainstem response (ABR) thresholds both pre and post 2-hr exposure to 10-kHz octave band noise at 108 dB sound pressure level in 5-6-wk-old female mice from the HMDP (4-5 mice/strain). From the observation that NIHL susceptibility varied among the strains, we performed a GWAS with correction for population structure and mapped a locus on chromosome 6 that was statistically significantly associated with two adjacent frequencies. We then used a "genetical genomics" approach that included the analysis of cochlear eQTLs to identify candidate genes within the GWAS QTL. In order to validate the gene-by-environment interaction, we compared the effects of the postnoise exposure locus with that from the same unexposed strains. The most significant SNP at chromosome 6 (rs37517079) was associated with noise susceptibility, but was not significant at the same frequencies in our unexposed study. These findings demonstrate that the genetic architecture of NIHL is distinct from that of unexposed hearing levels and provide strong evidence for gene-by-environment interactions in NIHL. Copyright © 2016 Lavinsky et al.

Linking Genes to Cardiovascular Diseases: Gene Action and Gene–Environment Interactions

PubMed Central

2016-01-01

A unique myocardial characteristic is its ability to grow/remodel in order to adapt; this is determined partly by genes and partly by the environment and the milieu intérieur. In the “post-genomic” era, a need is emerging to elucidate the physiologic functions of myocardial genes, as well as potential adaptive and maladaptive modulations induced by environmental/epigenetic factors. Genome sequencing and analysis advances have become exponential lately, with escalation of our knowledge concerning sometimes controversial genetic underpinnings of cardiovascular diseases. Current technologies can identify candidate genes variously involved in diverse normal/abnormal morphomechanical phenotypes, and offer insights into multiple genetic factors implicated in complex cardiovascular syndromes. The expression profiles of thousands of genes are regularly ascertained under diverse conditions. Global analyses of gene expression levels are useful for cataloging genes and correlated phenotypes, and for elucidating the role of genes in maladies. Comparative expression of gene networks coupled to complex disorders can contribute insights as to how “modifier genes” influence the expressed phenotypes. Increasingly, a more comprehensive and detailed systematic understanding of genetic abnormalities underlying, for example, various genetic cardiomyopathies is emerging. Implementing genomic findings in cardiology practice may well lead directly to better diagnosing and therapeutics. There is currently evolving a strong appreciation for the value of studying gene anomalies, and doing so in a non-disjointed, cohesive manner. However, it is challenging for many—practitioners and investigators—to comprehend, interpret, and utilize the clinically increasingly accessible and affordable cardiovascular genomics studies. This survey addresses the need for fundamental understanding in this vital area. PMID:26545598

Interactions between genetic variation and cellular environment in skeletal muscle gene expression.

PubMed

Taylor, D Leland; Knowles, David A; Scott, Laura J; Ramirez, Andrea H; Casale, Francesco Paolo; Wolford, Brooke N; Guan, Li; Varshney, Arushi; Albanus, Ricardo D'Oliveira; Parker, Stephen C J; Narisu, Narisu; Chines, Peter S; Erdos, Michael R; Welch, Ryan P; Kinnunen, Leena; Saramies, Jouko; Sundvall, Jouko; Lakka, Timo A; Laakso, Markku; Tuomilehto, Jaakko; Koistinen, Heikki A; Stegle, Oliver; Boehnke, Michael; Birney, Ewan; Collins, Francis S

2018-01-01

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis-acting genotype-environment interactions (GxE)-genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate environmental response expression quantitative trait loci (reQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.

Gene-environment interaction in major depression: focus on experience-dependent biological systems.

PubMed

Lopizzo, Nicola; Bocchio Chiavetto, Luisella; Cattane, Nadia; Plazzotta, Giona; Tarazi, Frank I; Pariante, Carmine M; Riva, Marco A; Cattaneo, Annamaria

2015-01-01

Major depressive disorder (MDD) is a multifactorial and polygenic disorder, where multiple and partially overlapping sets of susceptibility genes interact each other and with the environment, predisposing individuals to the development of the illness. Thus, MDD results from a complex interplay of vulnerability genes and environmental factors that act cumulatively throughout individual's lifetime. Among these environmental factors, stressful life experiences, especially those occurring early in life, have been suggested to exert a crucial impact on brain development, leading to permanent functional changes that may contribute to lifelong risk for mental health outcomes. In this review, we will discuss how genetic variants (polymorphisms, SNPs) within genes operating in neurobiological systems that mediate stress response and synaptic plasticity, can impact, by themselves, the vulnerability risk for MDD; we will also consider how this MDD risk can be further modulated when gene × environment interaction is taken into account. Finally, we will discuss the role of epigenetic mechanisms, and in particular of DNA methylation and miRNAs expression changes, in mediating the effect of the stress on the vulnerability risk to develop MDD. Taken together, we aim to underlie the role of genetic and epigenetic processes involved in stress- and neuroplasticity-related biological systems on the development of MDD after exposure to early life stress, thereby building the basis for future research and clinical interventions.

Sleep Duration and Depressive Symptoms: A Gene-Environment Interaction

PubMed Central

Watson, Nathaniel F.; Harden, Kathryn Paige; Buchwald, Dedra; Vitiello, Michael V.; Pack, Allan I.; Strachan, Eric; Goldberg, Jack

2014-01-01

Objective: We used quantitative genetic models to assess whether sleep duration modifies genetic and environmental influences on depressive symptoms. Method: Participants were 1,788 adult twins from 894 same-sex twin pairs (192 male and 412 female monozygotic [MZ] pairs, and 81 male and 209 female dizygotic [DZ] pairs] from the University of Washington Twin Registry. Participants self-reported habitual sleep duration and depressive symptoms. Data were analyzed using quantitative genetic interaction models, which allowed the magnitude of additive genetic, shared environmental, and non-shared environmental influences on depressive symptoms to vary with sleep duration. Results: Within MZ twin pairs, the twin who reported longer sleep duration reported fewer depressive symptoms (ec = -0.17, SE = 0.06, P < 0.05). There was a significant gene × sleep duration interaction effect on depressive symptoms (a'c = 0.23, SE = 0.08, P < 0.05), with the interaction occurring on genetic influences that are common to both sleep duration and depressive symptoms. Among individuals with sleep duration within the normal range (7-8.9 h/night), the total heritability (h2) of depressive symptoms was approximately 27%. However, among individuals with sleep duration within the low (< 7 h/night) or high (≥ 9 h/night) range, increased genetic influence on depressive symptoms was observed, particularly at sleep duration extremes (5 h/night: h2 = 53%; 10 h/night: h2 = 49%). Conclusion: Genetic contributions to depressive symptoms increase at both short and long sleep durations. Citation: Watson NF; Harden KP; Buchwald D; Vitiello MV; Pack AI; Stachan E; Goldberg J. Sleep duration and depressive symptoms: a gene-environment interaction. SLEEP 2014;37(2):351-358. PMID:24497663

Gene-environment interaction in posttraumatic stress disorder

PubMed Central

Nugent, Nicole R.; Amstadter, Ananda B.

2009-01-01

The purpose of this article is to encourage research investigating the role of measured gene-environment interaction (G × E) in the etiology of posttraumatic stress disorder (PTSD). PTSD is uniquely suited to the study of G × E as the diagnosis requires exposure to a potentially-traumatic life event. PTSD is also moderately heritable; however, the role of genetic factors in PTSD etiology has been largely neglected both by trauma researchers and psychiatric geneticists. First, we summarize evidence for genetic influences on PTSD from family, twin, and molecular genetic studies. Second, we discuss the key challenges in G × E studies of PTSD and offer practical strategies for addressing these challenges and for discovering replicable G × E for PTSD. Finally, we propose some promising new directions for PTSD G × E research. We suggest that G × E research in PTSD is essential to understanding vulnerability and resilience following exposure to a traumatic event. PMID:18297420

Animal models of gene-environment interaction in schizophrenia: a dimensional perspective

PubMed Central

Ayhan, Yavuz; McFarland, Ross; Pletnikov, Mikhail V.

2015-01-01

Schizophrenia has long been considered as a disorder with multifactorial origins. Recent discoveries have advanced our understanding of the genetic architecture of the disease. However, even with the increase of identified risk variants, heritability estimates suggest an important contribution of non-genetic factors. Various environmental risk factors have been proposed to play a role in the etiopathogenesis of schizophrenia. These include season of birth, maternal infections, obstetric complications, adverse events at early childhood, and drug abuse. Despite the progress in identification of genetic and environmental risk factors, we still have a limited understanding of the mechanisms whereby gene-environment interactions (GxE) operate in schizophrenia and psychoses at large. In this review we provide a critical analysis of current animal models of GxE relevant to psychotic disorders and propose that dimensional perspective will advance our understanding of the complex mechanisms of these disorders. PMID:26510407

The Oxytocin Receptor Gene (OXTR) in Relation to State Levels of Loneliness in Adolescence: Evidence for Micro-Level Gene-Environment Interactions

PubMed Central

van Roekel, Eeske; Verhagen, Maaike; Scholte, Ron H. J.; Kleinjan, Marloes; Goossens, Luc; Engels, Rutger C. M. E.

2013-01-01

Previous research has shown that the rs53576 variant of the oxytocin receptor gene (OXTR) is associated with trait levels of loneliness, but results are inconsistent. The aim of the present study is to examine micro-level effects of the OXTR rs53576 variant on state levels of loneliness in early adolescents. In addition, gene-environment interactions are examined between this OXTR variant and positive and negative perceptions of company. Data were collected in 278 adolescents (58% girls), by means of the Experience Sampling Method (ESM). Sampling periods consisted of six days with nine assessments per day. A relation was found between the OXTR rs53576 variant and state loneliness, in girls only. Girls carrying an A allele had higher levels of state loneliness than girls carrying the GG genotype. In addition, adolescents with an A allele were more affected by negative perceptions of company than GG carriers, on weekend days only. No significant gene-environment interactions were found with positive company. Adolescents carrying an A allele were more susceptible to negative environments during weekend days than GG carriers. Our findings emphasize the importance of operationalizing the phenotype and the environment accurately. PMID:24223720

The oxytocin receptor gene (OXTR) in relation to state levels of loneliness in adolescence: evidence for micro-level gene-environment interactions.

PubMed

van Roekel, Eeske; Verhagen, Maaike; Scholte, Ron H J; Kleinjan, Marloes; Goossens, Luc; Engels, Rutger C M E

2013-01-01

Previous research has shown that the rs53576 variant of the oxytocin receptor gene (OXTR) is associated with trait levels of loneliness, but results are inconsistent. The aim of the present study is to examine micro-level effects of the OXTR rs53576 variant on state levels of loneliness in early adolescents. In addition, gene-environment interactions are examined between this OXTR variant and positive and negative perceptions of company. Data were collected in 278 adolescents (58% girls), by means of the Experience Sampling Method (ESM). Sampling periods consisted of six days with nine assessments per day. A relation was found between the OXTR rs53576 variant and state loneliness, in girls only. Girls carrying an A allele had higher levels of state loneliness than girls carrying the GG genotype. In addition, adolescents with an A allele were more affected by negative perceptions of company than GG carriers, on weekend days only. No significant gene-environment interactions were found with positive company. Adolescents carrying an A allele were more susceptible to negative environments during weekend days than GG carriers. Our findings emphasize the importance of operationalizing the phenotype and the environment accurately.

Microsatellite polymorphisms associated with human behavioural and psychological phenotypes including a gene-environment interaction.

PubMed

Bagshaw, Andrew T M; Horwood, L John; Fergusson, David M; Gemmell, Neil J; Kennedy, Martin A

2017-02-03

The genetic and environmental influences on human personality and behaviour are a complex matter of ongoing debate. Accumulating evidence indicates that short tandem repeats (STRs) in regulatory regions are good candidates to explain heritability not accessed by genome-wide association studies. We tested for associations between the genotypes of four selected repeats and 18 traits relating to personality, behaviour, cognitive ability and mental health in a well-studied longitudinal birth cohort (n = 458-589) using one way analysis of variance. The repeats were a highly conserved poly-AC microsatellite in the upstream promoter region of the T-box brain 1 (TBR1) gene and three previously studied STRs in the activating enhancer-binding protein 2-beta (AP2-β) and androgen receptor (AR) genes. Where significance was found we used multiple regression to assess the influence of confounding factors. Carriers of the shorter, most common, allele of the AR gene's GGN microsatellite polymorphism had fewer anxiety-related symptoms, which was consistent with previous studies, but in our study this was not significant following Bonferroni correction. No associations with two repeats in the AP2-β gene withstood this correction. A novel finding was that carriers of the minor allele of the TBR1 AC microsatellite were at higher risk of conduct problems in childhood at age 7-9 (p = 0.0007, which did pass Bonferroni correction). Including maternal smoking during pregnancy (MSDP) in models controlling for potentially confounding influences showed that an interaction between TBR1 genotype and MSDP was a significant predictor of conduct problems in childhood and adolescence (p < 0.001), and of self-reported criminal behaviour up to age 25 years (p ≤ 0.02). This interaction remained significant after controlling for possible confounders including maternal age at birth, socio-economic status and education, and offspring birth weight. The potential functional importance

Shame and Guilt-Proneness in Adolescents: Gene-Environment Interactions

PubMed Central

Szentágotai-Tătar, Aurora; Chiș, Adina; Vulturar, Romana; Dobrean, Anca; Cândea, Diana Mirela; Miu, Andrei C.

2015-01-01

Rooted in people’s preoccupation with how they are perceived and evaluated, shame and guilt are self-conscious emotions that play adaptive roles in social behavior, but can also contribute to psychopathology when dysregulated. Shame and guilt-proneness develop during childhood and adolescence, and are influenced by genetic and environmental factors that are little known to date. This study investigated the effects of early traumatic events and functional polymorphisms in the brain-derived neurotrophic factor (BDNF) gene and the serotonin transporter gene promoter (5-HTTLPR) on shame and guilt in adolescents. A sample of N = 271 healthy adolescents between 14 and 17 years of age filled in measures of early traumatic events and proneness to shame and guilt, and were genotyped for the BDNF Val66Met and 5-HTTLPR polymorphisms. Results of moderator analyses indicated that trauma intensity was positively associated with guilt-proneness only in carriers of the low-expressing Met allele of BDNF Val66Met. This is the first study that identifies a gene-environment interaction that significantly contributes to guilt proneness in adolescents, with potential implications for developmental psychopathology. PMID:26230319

Topology association analysis in weighted protein interaction network for gene prioritization

NASA Astrophysics Data System (ADS)

Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

2016-11-01

Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

Genome-wide gene by lead exposure interaction analysis identifies UNC5D as a candidate gene for neurodevelopment.

PubMed

Wang, Zhaoxi; Claus Henn, Birgit; Wang, Chaolong; Wei, Yongyue; Su, Li; Sun, Ryan; Chen, Han; Wagner, Peter J; Lu, Quan; Lin, Xihong; Wright, Robert; Bellinger, David; Kile, Molly; Mazumdar, Maitreyi; Tellez-Rojo, Martha Maria; Schnaas, Lourdes; Christiani, David C

2017-07-28

Neurodevelopment is a complex process involving both genetic and environmental factors. Prenatal exposure to lead (Pb) has been associated with lower performance on neurodevelopmental tests. Adverse neurodevelopmental outcomes are more frequent and/or more severe when toxic exposures interact with genetic susceptibility. To explore possible loci associated with increased susceptibility to prenatal Pb exposure, we performed a genome-wide gene-environment interaction study (GWIS) in young children from Mexico (n = 390) and Bangladesh (n = 497). Prenatal Pb exposure was estimated by cord blood Pb concentration. Neurodevelopment was assessed using the Bayley Scales of Infant Development. We identified a locus on chromosome 8, containing UNC5D, and demonstrated evidence of its genome-wide significance with mental composite scores (rs9642758, p meta  = 4.35 × 10 -6 ). Within this locus, the joint effects of two independent single nucleotide polymorphisms (SNPs, rs9642758 and rs10503970) had a p-value of 4.38 × 10 -9 for mental composite scores. Correlating GWIS results with in vitro transcriptomic profiles identified one common gene, SLC1A5, which is involved in synaptic function, neuronal development, and excitotoxicity. Further analysis revealed interconnected interactions that formed a large network of 52 genes enriched with oxidative stress genes and neurodevelopmental genes. Our findings suggest that certain genetic polymorphisms within/near genes relevant to neurodevelopment might modify the toxic effects of Pb exposure via oxidative stress.

Combinatory approaches prevent preterm birth profoundly exacerbated by gene-environment interactions

PubMed Central

Cha, Jeeyeon; Bartos, Amanda; Egashira, Mahiro; Haraguchi, Hirofumi; Saito-Fujita, Tomoko; Leishman, Emma; Bradshaw, Heather; Dey, Sudhansu K.; Hirota, Yasushi

2013-01-01

There are currently more than 15 million preterm births each year. We propose that gene-environment interaction is a major contributor to preterm birth. To address this experimentally, we generated a mouse model with uterine deletion of Trp53, which exhibits approximately 50% incidence of spontaneous preterm birth due to premature decidual senescence with increased mTORC1 activity and COX2 signaling. Here we provide evidence that this predisposition provoked preterm birth in 100% of females exposed to a mild inflammatory insult with LPS, revealing the high significance of gene-environment interactions in preterm birth. More intriguingly, preterm birth was rescued in LPS-treated Trp53-deficient mice when they were treated with a combination of rapamycin (mTORC1 inhibitor) and progesterone (P4), without adverse effects on maternal or fetal health. These results provide evidence for the cooperative contributions of two sites of action (decidua and ovary) toward preterm birth. Moreover, a similar signature of decidual senescence with increased mTORC1 and COX2 signaling was observed in women undergoing preterm birth. Collectively, our findings show that superimposition of inflammation on genetic predisposition results in high incidence of preterm birth and suggest that combined treatment with low doses of rapamycin and P4 may help reduce the incidence of preterm birth in high-risk women. PMID:23979163

Gene-environment interactions in geriatric depression.

PubMed

Lotrich, Francis E

2011-06-01

In older adults, several environmental challenges can potentially trigger the onset of an episode of major depression. Vulnerability to these challenges can be influenced by genetics. There is accumulating evidence for an interaction between stress and a serotonin transporter polymorphism, though there is also heterogeneity among studies. Other relevant genes include those encoding for the neuroendocrine stress axis, growth factors, and other monoaminergic systems. Each of these may interact with either predisposing traumas in early childhood or precipitating events later in life. Copyright © 2011 Elsevier Inc. All rights reserved.

Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.

PubMed

Wu, Cen; Jiang, Yu; Ren, Jie; Cui, Yuehua; Ma, Shuangge

2018-02-10

Identification of gene-environment (G × E) interactions associated with disease phenotypes has posed a great challenge in high-throughput cancer studies. The existing marginal identification methods have suffered from not being able to accommodate the joint effects of a large number of genetic variants, while some of the joint-effect methods have been limited by failing to respect the "main effects, interactions" hierarchy, by ignoring data contamination, and by using inefficient selection techniques under complex structural sparsity. In this article, we develop an effective penalization approach to identify important G × E interactions and main effects, which can account for the hierarchical structures of the 2 types of effects. Possible data contamination is accommodated by adopting the least absolute deviation loss function. The advantage of the proposed approach over the alternatives is convincingly demonstrated in both simulation and a case study on lung cancer prognosis with gene expression measurements and clinical covariates under the accelerated failure time model. Copyright © 2017 John Wiley & Sons, Ltd.

Enhancing the gene-environment interaction framework through a quasi-experimental research design: evidence from differential responses to September 11.

PubMed

Fletcher, Jason M

2014-01-01

This article uses a gene-environment interaction framework to examine the differential responses to an objective external stressor based on genetic variation in the production of depressive symptoms. This article advances the literature by utilizing a quasi-experimental environmental exposure design, as well as a regression discontinuity design, to control for seasonal trends, which limit the potential for gene-environment correlation and allow stronger causal claims. Replications are attempted for two prominent genes (5-HTT and MAOA), and three additional genes are explored (DRD2, DRD4, and DAT1). This article provides evidence of a main effect of 9/11 on reports of feelings of sadness and fails to replicate a common finding of interaction using 5-HTT but does show support for interaction with MAOA in men. It also provides new evidence that variation in the DRD4 gene modifies an individual's response to the exposure, with individuals with no 7-repeats found to have a muted response.

Environmental and gene-environment interactions and risk of rheumatoid arthritis

PubMed Central

Karlson, Elizabeth W.; Deane, Kevin

2012-01-01

Multiple environmental factors including hormones, dietary factors, infections and exposure to tobacco smoke as well as gene-environment interactions have been associated with increased risk for rheumatoid arthritis (RA). Importantly, the growing understanding of the prolonged period prior to the first onset of symptoms of RA suggests that these environmental and genetic factors are likely acting to drive the development of RA-related autoimmunity long before the appearance of the first joint symptoms and clinical findings that are characteristic of RA. Herein we will review these factors and interactions, especially those that have been investigated in a prospective fashion prior to the symptomatic onset of RA. We will also discuss how these factors may be explored in future study to further the understanding of the pathogenesis of RA, and ultimately perhaps develop preventive measures for this disease. PMID:22819092

MAOA genotype, social exclusion and aggression: an experimental test of a gene-environment interaction.

PubMed

Gallardo-Pujol, D; Andrés-Pueyo, A; Maydeu-Olivares, A

2013-02-01

In 2002, Caspi and colleagues provided the first epidemiological evidence that genotype may moderate individuals' responses to environmental determinants. However, in a correlational study great care must be taken to ensure the proper estimation of the causal relationship. Here, a randomized experiment was performed to test the hypothesis that the MAOA gene promoter polymorphism (MAOA-LPR) interacts with environmental adversity in determining aggressive behavior using laboratory analogs of real-life conditions. A sample of 57 Caucasian male students of Catalan and Spanish origin was recruited at the University of Barcelona. Ostracism, or social exclusion, was induced as environmental adversity using the Cyberball software. Laboratory aggression was assessed with the Point Subtraction Aggression Paradigm (PSAP), which was used as an analog of antisocial behavior. We also measured aggressiveness by means of the reduced version of the Aggression Questionnaire. The MAOA-LPR polymorphism showed a significant effect on the number of aggressive responses in the PSAP (F(1,53) = 4.63, P = 0.03, partial η(2) = 0.08), as well as social exclusion (F(1,53) = 8.03, P = 0.01, partial η(2) = 0.13). Most notably, however, we found that the MAOA-LPR polymorphism interacts significantly with social exclusion in order to provoke aggressive behavior (F(1,53) = 4.42, P = 0.04, partial η(2) = 0.08), remarkably, the low-activity allele of the MAOA-LPR polymorphism carriers in the ostracized group show significantly higher aggression scores than the rest. Our results support the notion that gene-environment interactions can be successfully reproduced within a laboratory using analogs and an appropriate design. We provide guidelines to test gene-environment interactions hypotheses under controlled, experimental settings. © 2012 The Authors. Genes, Brain and Behavior © 2012 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.

GeneXplorer: an interactive web application for microarray data visualization and analysis.

PubMed

Rees, Christian A; Demeter, Janos; Matese, John C; Botstein, David; Sherlock, Gavin

2004-10-01

When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/.

Gene-Environment Correlation and Interaction in Peer Effects on Adolescent Alcohol and Tobacco Use

PubMed Central

Harden, K. Paige; Hill, Jennifer E.; Turkheimer, Eric; Emery, Robert E.

2010-01-01

Peer relationships are commonly thought to be critical for adolescent socialization, including the development of negative health behaviors such as alcohol and tobacco use. The interplay between genetic liability and peer influences on the development of adolescent alcohol and tobacco use was examined using a nationally-representative sample of adolescent sibling pairs and their best friends. Genetic factors, some of them related to an adolescent's own substance use and some of them independent of use, were associated with increased exposure to best friends with heavy substance use—a gene-environment correlation. Moreover, adolescents who were genetically liable to substance use were more vulnerable to the adverse influences of their best friends—a gene-environment interaction. PMID:18368474

Gene-Environment Interactions across Development: Exploring DRD2 Genotype and Prenatal Smoking Effects on Self-Regulation

ERIC Educational Resources Information Center

Wiebe, Sandra A.; Espy, Kimberly Andrews; Stopp, Christian; Respass, Jennifer; Stewart, Peter; Jameson, Travis R.; Gilbert, David G.; Huggenvik, Jodi I.

2009-01-01

Genetic factors dynamically interact with both pre- and postnatal environmental influences to shape development. Considerable attention has been devoted to gene-environment interactions (G x E) on important outcomes (A. Caspi & T. E. Moffitt, 2006). It is also important to consider the possibility that these G x E effects may vary across…

Gene × environment interaction on intergroup bias: the role of 5-HTTLPR and perceived outgroup threat.

PubMed

Cheon, Bobby K; Livingston, Robert W; Hong, Ying-Yi; Chiao, Joan Y

2014-09-01

Perceived threat from outgroups is a consistent social-environmental antecedent of intergroup bias (i.e. prejudice, ingroup favoritism). The serotonin transporter gene polymorphism (5-HTTLPR) has been associated with individual variations in sensitivity to context, particularly stressful and threatening situations. Here, we examined how 5-HTTLPR and environmental factors signaling potential outgroup threat dynamically interact to shape intergroup bias. Across two studies, we provide novel evidence for a gene-environment interaction on the acquisition of intergroup bias and prejudice. Greater exposure to signals of outgroup threat, such as negative prior contact with outgroups and perceived danger from the social environment, were more predictive of intergroup bias among participants possessing at least one short allele (vs two long alleles) of 5-HTTLPR. Furthermore, this gene x environment interaction was observed for biases directed at diverse ethnic and arbitrarily-defined outgroups across measures reflecting intergroup biases in evaluation and discriminatory behavior. These findings reveal a candidate genetic mechanism for the acquisition of intergroup bias, and suggest that intergroup bias is dually inherited and transmitted through the interplay of social (i.e. contextual cues of outgroup threat) and biological mechanisms (i.e. genetic sensitivity toward threatening contexts) that regulate perceived intergroup threats. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Brain-derived neurotrophic factor as a model system for examining gene by environment interactions across development.

PubMed

Casey, B J; Glatt, C E; Tottenham, N; Soliman, F; Bath, K; Amso, D; Altemus, M; Pattwell, S; Jones, R; Levita, L; McEwen, B; Magariños, A M; Gunnar, M; Thomas, K M; Mezey, J; Clark, A G; Hempstead, B L; Lee, F S

2009-11-24

There has been a dramatic rise in gene x environment studies of human behavior over the past decade that have moved the field beyond simple nature versus nurture debates. These studies offer promise in accounting for more variability in behavioral and biological phenotypes than studies that focus on genetic or experiential factors alone. They also provide clues into mechanisms of modifying genetic risk or resilience in neurodevelopmental disorders. Yet, it is rare that these studies consider how these interactions change over the course of development. In this paper, we describe research that focuses on the impact of a polymorphism in a brain-derived neurotrophic factor (BDNF) gene, known to be involved in learning and development. Specifically we present findings that assess the effects of genotypic and environmental loadings on neuroanatomic and behavioral phenotypes across development. The findings illustrate the use of a genetic mouse model that mimics the human polymorphism, to constrain the interpretation of gene-environment interactions across development in humans.

The genetics of music accomplishment: evidence for gene-environment correlation and interaction.

PubMed

Hambrick, David Z; Tucker-Drob, Elliot M

2015-02-01

Theories of skilled performance that emphasize training history, such as K. Anders Ericsson and colleagues' deliberate-practice theory, have received a great deal of recent attention in both the scientific literature and the popular press. Twin studies, however, have demonstrated evidence for moderate-to-strong genetic influences on skilled performance. Focusing on musical accomplishment in a sample of over 800 pairs of twins, we found evidence for gene-environment correlation, in the form of a genetic effect on music practice. However, only about one quarter of the genetic effect on music accomplishment was explained by this genetic effect on music practice, suggesting that genetically influenced factors other than practice contribute to individual differences in music accomplishment. We also found evidence for gene-environment interaction, such that genetic effects on music accomplishment were most pronounced among those engaging in music practice, suggesting that genetic potentials for skilled performance are most fully expressed and fostered by practice.

Sample size requirements for indirect association studies of gene-environment interactions (G x E).

PubMed

Hein, Rebecca; Beckmann, Lars; Chang-Claude, Jenny

2008-04-01

Association studies accounting for gene-environment interactions (G x E) may be useful for detecting genetic effects. Although current technology enables very dense marker spacing in genetic association studies, the true disease variants may not be genotyped. Thus, causal genes are searched for by indirect association using genetic markers in linkage disequilibrium (LD) with the true disease variants. Sample sizes needed to detect G x E effects in indirect case-control association studies depend on the true genetic main effects, disease allele frequencies, whether marker and disease allele frequencies match, LD between loci, main effects and prevalence of environmental exposures, and the magnitude of interactions. We explored variables influencing sample sizes needed to detect G x E, compared these sample sizes with those required to detect genetic marginal effects, and provide an algorithm for power and sample size estimations. Required sample sizes may be heavily inflated if LD between marker and disease loci decreases. More than 10,000 case-control pairs may be required to detect G x E. However, given weak true genetic main effects, moderate prevalence of environmental exposures, as well as strong interactions, G x E effects may be detected with smaller sample sizes than those needed for the detection of genetic marginal effects. Moreover, in this scenario, rare disease variants may only be detectable when G x E is included in the analyses. Thus, the analysis of G x E appears to be an attractive option for the detection of weak genetic main effects of rare variants that may not be detectable in the analysis of genetic marginal effects only.

Vulnerability or Sensitivity to the Environment? Methodological Issues, Trends, and Recommendations in Gene-Environment Interactions Research in Human Behavior.

PubMed

Leighton, Caroline; Botto, Alberto; Silva, Jaime R; Jiménez, Juan Pablo; Luyten, Patrick

2017-01-01

Research on the potential role of gene-environment interactions (GxE) in explaining vulnerability to psychopathology in humans has witnessed a shift from a diathesis-stress perspective to differential susceptibility approaches. This paper critically reviews methodological issues and trends in this body of research. Databases were screened for studies of GxE in the prediction of personality traits, behavior, and mental health disorders in humans published between January 2002 and January 2015. In total, 315 papers were included. Results showed that 34 candidate genes have been included in GxE studies. Independent of the type of environment studied (early or recent life events, positive or negative environments), about 67-83% of studies have reported significant GxE interactions, which is consistent with a social susceptibility model. The percentage of positive results does not seem to differ depending on the gene studied, although publication bias might be involved. However, the number of positive findings differs depending on the population studied (i.e., young adults vs. older adults). Methodological considerations limit the ability to draw strong conclusions, particularly as almost 90% ( n  = 283/315) of published papers are based on samples from North America and Europe, and about 70% of published studies (219/315) are based on samples that were also used in other reports. At the same time, there are clear indications of methodological improvements over time, as is shown by a significant increase in longitudinal and experimental studies as well as in improved minimum genotyping. Recommendations for future research, such as minimum quality assessment of genes and environmental factors, specifying theoretical models guiding the study, and taking into account of cultural, ethnic, and lifetime perspectives, are formulated.

Commentary: Fundamental problems with candidate gene-by-environment interaction studies - reflections on Moore and Thoemmes (2016).

PubMed

Border, Richard; Keller, Matthew C

2017-03-01

Moore and Thoemmes elaborate on one particular source of difficulty in the study of candidate gene-by-environment interactions (cG × E): how different biologically plausible configurations of gene-environment covariation can bias estimates of cG × E when not explicitly modeled. However, even if cG × E investigators were able to account for the sources of bias Moore and Thoemmes elaborate, it is unlikely that conventional approaches would yield reliable results. Published cG × E findings to date have generally employed inadequate analytic procedures, have relied on samples orders of magnitude too small to detect plausible effects, and have relied on a particular candidate gene approach that has been unfruitful and largely jettisoned in mainstream genetic analyses of complex traits. Analytic procedures for the study of gene-environment interplay must evolve to meet the challenges that the genetic architecture of complex traits presents, and investigators must collaborate on grander scales if we hope to begin to understand how specific genes and environments combine to affect behavior. © 2017 Association for Child and Adolescent Mental Health.

A database of gene-environment interactions pertaining to blood lipid traits, cardiovascular disease and type 2 diabetes

USDA-ARS?s Scientific Manuscript database

As the role of the environment – diet, exercise, alcohol and tobacco use and sleep among others – is accorded a more prominent role in modifying the relationship between genetic variants and clinical measures of disease, consideration of gene-environment (GxE) interactions is a must. To facilitate i...

Autism risk factors: genes, environment, and gene-environment interactions

PubMed Central

Chaste, Pauline; Leboyer, Marion

2012-01-01

The aim of this review is to summarize the key findings from genetic and epidemiological research, which show that autism is a complex disorder resulting from the combination of genetic and environmental factors. Remarkable advances in the knowledge of genetic causes of autism have resulted from the great efforts made in the field of genetics. The identification of specific alleles contributing to the autism spectrum has supplied important pieces for the autism puzzle. However, many questions remain unanswered, and new questions are raised by recent results. Moreover, given the amount of evidence supporting a significant contribution of environmental factors to autism risk, it is now clear that the search for environmental factors should be reinforced. One aspect of this search that has been neglected so far is the study of interactions between genes and environmental factors. PMID:23226953

Genes and environment in neonatal intraventricular hemorrhage.

PubMed

Ment, Laura R; Ådén, Ulrika; Bauer, Charles R; Bada, Henrietta S; Carlo, Waldemar A; Kaiser, Jeffrey R; Lin, Aiping; Cotten, Charles Michael; Murray, Jeffrey; Page, Grier; Hallman, Mikko; Lifton, Richard P; Zhang, Heping

2015-12-01

Emerging data suggest intraventricular hemorrhage (IVH) of the preterm neonate is a complex disorder with contributions from both the environment and the genome. Environmental analyses suggest factors mediating both cerebral blood flow and angiogenesis contribute to IVH, while candidate gene studies report variants in angiogenesis, inflammation, and vascular pathways. Gene-by-environment interactions demonstrate the interaction between the environment and the genome, and a non-replicated genome-wide association study suggests that both environmental and genetic factors contribute to the risk for severe IVH in very low-birth weight preterm neonates. Copyright © 2015 Elsevier Inc. All rights reserved.

Oh, Behave! Behavior as an Interaction between Genes & the Environment

ERIC Educational Resources Information Center

Weigel, Emily G.; DeNieu, Michael; Gall, Andrew J.

2014-01-01

This lesson is designed to teach students that behavior is a trait shaped by both genes and the environment. Students will read a scientific paper, discuss and generate predictions based on the ideas and data therein, and model the relationships between genes, the environment, and behavior. The lesson is targeted to meet the educational goals of…

Information-Theoretic Metrics for Visualizing Gene-Environment Interactions

PubMed Central

Chanda, Pritam ; Zhang, Aidong ; Brazeau, Daniel ; Sucheston, Lara ; Freudenheim, Jo L. ; Ambrosone, Christine ; Ramanathan, Murali 

2007-01-01

The purpose of our work was to develop heuristics for visualizing and interpreting gene-environment interactions (GEIs) and to assess the dependence of candidate visualization metrics on biological and study-design factors. Two information-theoretic metrics, the k-way interaction information (KWII) and the total correlation information (TCI), were investigated. The effectiveness of the KWII and TCI to detect GEIs in a diverse range of simulated data sets and a Crohn disease data set was assessed. The sensitivity of the KWII and TCI spectra to biological and study-design variables was determined. Head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and the pedigree disequilibrium test (PDT) methods were obtained. The KWII and TCI spectra, which are graphical summaries of the KWII and TCI for each subset of environmental and genotype variables, were found to detect each known GEI in the simulated data sets. The patterns in the KWII and TCI spectra were informative for factors such as case-control misassignment, locus heterogeneity, allele frequencies, and linkage disequilibrium. The KWII and TCI spectra were found to have excellent sensitivity for identifying the key disease-associated genetic variations in the Crohn disease data set. In head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, the results from visual interpretation of the KWII and TCI spectra performed satisfactorily. The KWII and TCI are promising metrics for visualizing GEIs. They are capable of detecting interactions among numerous single-nucleotide polymorphisms and environmental variables for a diverse range of GEI models. PMID:17924337

Identification of New Genetic Susceptibility Loci for Breast Cancer Through Consideration of Gene-Environment Interactions

PubMed Central

Schoeps, Anja; Rudolph, Anja; Seibold, Petra; Dunning, Alison M.; Milne, Roger L.; Bojesen, Stig E.; Swerdlow, Anthony; Andrulis, Irene; Brenner, Hermann; Behrens, Sabine; Orr, Nicholas; Jones, Michael; Ashworth, Alan; Li, Jingmei; Cramp, Helen; Connley, Dan; Czene, Kamila; Darabi, Hatef; Chanock, Stephen J.; Lissowska, Jolanta; Figueroa, Jonine D.; Knight, Julia; Glendon, Gord; Mulligan, Anna M.; Dumont, Martine; Severi, Gianluca; Baglietto, Laura; Olson, Janet; Vachon, Celine; Purrington, Kristen; Moisse, Matthieu; Neven, Patrick; Wildiers, Hans; Spurdle, Amanda; Kosma, Veli-Matti; Kataja, Vesa; Hartikainen, Jaana M.; Hamann, Ute; Ko, Yon-Dschun; Dieffenbach, Aida K.; Arndt, Volker; Stegmaier, Christa; Malats, Núria; Arias Perez, JoséI.; Benítez, Javier; Flyger, Henrik; Nordestgaard, Børge G.; Truong, Théresè; Cordina-Duverger, Emilie; Menegaux, Florence; Silva, Isabel dos Santos; Fletcher, Olivia; Johnson, Nichola; Häberle, Lothar; Beckmann, Matthias W.; Ekici, Arif B.; Braaf, Linde; Atsma, Femke; van den Broek, Alexandra J.; Makalic, Enes; Schmidt, Daniel F.; Southey, Melissa C.; Cox, Angela; Simard, Jacques; Giles, Graham G.; Lambrechts, Diether; Mannermaa, Arto; Brauch, Hiltrud; Guénel, Pascal; Peto, Julian; Fasching, Peter A.; Hopper, John; Flesch-Janys, Dieter; Couch, Fergus; Chenevix-Trench, Georgia; Pharoah, Paul D. P.; Garcia-Closas, Montserrat; Schmidt, Marjanka K.; Hall, Per; Easton, Douglas F.; Chang-Claude, Jenny

2014-01-01

Genes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene-environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P-values = 3 × 10−07), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m2 (OR = 1.26, 95% CI 1.15–1.38) but not in women with a BMI of 30 kg/m2 or higher (OR = 0.89, 95% CI 0.72–1.11, P for interaction = 3.2 × 10−05). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci. PMID:24248812

Childhood quality influences genetic sensitivity to environmental influences across adulthood: A life-course Gene × Environment interaction study.

PubMed

Keers, Robert; Pluess, Michael

2017-12-01

While environmental adversity has been shown to increase risk for psychopathology, individuals differ in their sensitivity to these effects. Both genes and childhood experiences are thought to influence sensitivity to the environment, and these factors may operate synergistically such that the effects of childhood experiences on later sensitivity are greater in individuals who are more genetically sensitive. In line with this hypothesis, several recent studies have reported a significant three-way interaction (Gene × Environment × Environment) between two candidate genes and childhood and adult environment on adult psychopathology. We aimed to replicate and extend these findings in a large, prospective multiwave longitudinal study using a polygenic score of environmental sensitivity and objectively measured childhood and adult material environmental quality. We found evidence for both Environment × Environment and Gene × Environment × Environment effects on psychological distress. Children with a poor-quality material environment were more sensitive to the negative effects of a poor environment as adults, reporting significantly higher psychological distress scores. These effects were further moderated by a polygenic score of environmental sensitivity. Genetically sensitive children were more vulnerable to adversity as adults, if they had experienced a poor childhood environment but were significantly less vulnerable if their childhood environment was positive. These findings are in line with the differential susceptibility hypothesis and suggest that a life course approach is necessary to elucidate the role of Gene × Environment in the development of mental illnesses.

Gene × environment interaction on intergroup bias: the role of 5-HTTLPR and perceived outgroup threat

PubMed Central

Livingston, Robert W.; Hong, Ying-Yi; Chiao, Joan Y.

2014-01-01

Perceived threat from outgroups is a consistent social-environmental antecedent of intergroup bias (i.e. prejudice, ingroup favoritism). The serotonin transporter gene polymorphism (5-HTTLPR) has been associated with individual variations in sensitivity to context, particularly stressful and threatening situations. Here, we examined how 5-HTTLPR and environmental factors signaling potential outgroup threat dynamically interact to shape intergroup bias. Across two studies, we provide novel evidence for a gene–environment interaction on the acquisition of intergroup bias and prejudice. Greater exposure to signals of outgroup threat, such as negative prior contact with outgroups and perceived danger from the social environment, were more predictive of intergroup bias among participants possessing at least one short allele (vs two long alleles) of 5-HTTLPR. Furthermore, this gene x environment interaction was observed for biases directed at diverse ethnic and arbitrarily-defined outgroups across measures reflecting intergroup biases in evaluation and discriminatory behavior. These findings reveal a candidate genetic mechanism for the acquisition of intergroup bias, and suggest that intergroup bias is dually inherited and transmitted through the interplay of social (i.e. contextual cues of outgroup threat) and biological mechanisms (i.e. genetic sensitivity toward threatening contexts) that regulate perceived intergroup threats. PMID:23887814

Epistasis × environment interactions among Arabidopsis thaliana glucosinolate genes impact complex traits and fitness in the field.

PubMed

Kerwin, Rachel E; Feusier, Julie; Muok, Alise; Lin, Catherine; Larson, Brandon; Copeland, Daniel; Corwin, Jason A; Rubin, Matthew J; Francisco, Marta; Li, Baohua; Joseph, Bindu; Weinig, Cynthia; Kliebenstein, Daniel J

2017-08-01

Despite the growing number of studies showing that genotype × environment and epistatic interactions control fitness, the influences of epistasis × environment interactions on adaptive trait evolution remain largely uncharacterized. Across three field trials, we quantified aliphatic glucosinolate (GSL) defense chemistry, leaf damage, and relative fitness using mutant lines of Arabidopsis thaliana varying at pairs of causal aliphatic GSL defense genes to test the impact of epistatic and epistasis × environment interactions on adaptive trait variation. We found that aliphatic GSL accumulation was primarily influenced by additive and epistatic genetic variation, leaf damage was primarily influenced by environmental variation and relative fitness was primarily influenced by epistasis and epistasis × environment interactions. Epistasis × environment interactions accounted for up to 48% of the relative fitness variation in the field. At a single field site, the impact of epistasis on relative fitness varied significantly over 2 yr, showing that epistasis × environment interactions within a location can be temporally dynamic. These results suggest that the environmental dependency of epistasis can profoundly influence the response to selection, shaping the adaptive trajectories of natural populations in complex ways, and deserves further consideration in future evolutionary studies. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Operating Characteristics of Statistical Methods for Detecting Gene-by-Measured Environment Interaction in the Presence of Gene-Environment Correlation under Violations of Distributional Assumptions.

PubMed

Van Hulle, Carol A; Rathouz, Paul J

2015-02-01

Accurately identifying interactions between genetic vulnerabilities and environmental factors is of critical importance for genetic research on health and behavior. In the previous work of Van Hulle et al. (Behavior Genetics, Vol. 43, 2013, pp. 71-84), we explored the operating characteristics for a set of biometric (e.g., twin) models of Rathouz et al. (Behavior Genetics, Vol. 38, 2008, pp. 301-315), for testing gene-by-measured environment interaction (GxM) in the presence of gene-by-measured environment correlation (rGM) where data followed the assumed distributional structure. Here we explore the effects that violating distributional assumptions have on the operating characteristics of these same models even when structural model assumptions are correct. We simulated N = 2,000 replicates of n = 1,000 twin pairs under a number of conditions. Non-normality was imposed on either the putative moderator or on the ultimate outcome by ordinalizing or censoring the data. We examined the empirical Type I error rates and compared Bayesian information criterion (BIC) values. In general, non-normality in the putative moderator had little impact on the Type I error rates or BIC comparisons. In contrast, non-normality in the outcome was often mistaken for or masked GxM, especially when the outcome data were censored.

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures.

PubMed

Sánchez, Brisa N; Kang, Shan; Mukherjee, Bhramar

2012-06-01

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron

Blood lead levels, iron metabolism gene polymorphisms and homocysteine: a gene-environment interaction study.

PubMed

Kim, Kyoung-Nam; Lee, Mee-Ri; Lim, Youn-Hee; Hong, Yun-Chul

2017-12-01

Homocysteine has been causally associated with various adverse health outcomes. Evidence supporting the relationship between lead and homocysteine levels has been accumulating, but most prior studies have not focused on the interaction with genetic polymorphisms. From a community-based prospective cohort, we analysed 386 participants (aged 41-71 years) with information regarding blood lead and plasma homocysteine levels. Blood lead levels were measured between 2001 and 2003, and plasma homocysteine levels were measured in 2007. Interactions of lead levels with 42 genotyped single-nucleotide polymorphisms (SNPs) in five genes ( TF , HFE , CBS , BHMT and MTR ) were assessed via a 2-degree of freedom (df) joint test and a 1-df interaction test. In secondary analyses using imputation, we further assessed 58 imputed SNPs in the TF and MTHFR genes. Blood lead concentrations were positively associated with plasma homocysteine levels (p=0.0276). Six SNPs in the TF and MTR genes were screened using the 2-df joint test, and among them, three SNPs in the TF gene showed interactions with lead with respect to homocysteine levels through the 1-df interaction test (p<0.0083). Seven SNPs in the MTHFR gene were associated with homocysteine levels at an α-level of 0.05, but the associations did not persist after Bonferroni correction. These SNPs did not show interactions with lead levels. Blood lead levels were positively associated with plasma homocysteine levels measured 4-6 years later, and three SNPs in the TF gene modified the association. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Classification and Clustering Methods for Multiple Environmental Factors in Gene-Environment Interaction: Application to the Multi-Ethnic Study of Atherosclerosis.

PubMed

Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A; Kardia, Sharon L R; Allison, Matthew; Diez Roux, Ana V

2016-11-01

There has been an increased interest in identifying gene-environment interaction (G × E) in the context of multiple environmental exposures. Most G × E studies analyze one exposure at a time, but we are exposed to multiple exposures in reality. Efficient analysis strategies for complex G × E with multiple environmental factors in a single model are still lacking. Using the data from the Multiethnic Study of Atherosclerosis, we illustrate a two-step approach for modeling G × E with multiple environmental factors. First, we utilize common clustering and classification strategies (e.g., k-means, latent class analysis, classification and regression trees, Bayesian clustering using Dirichlet Process) to define subgroups corresponding to distinct environmental exposure profiles. Second, we illustrate the use of an additive main effects and multiplicative interaction model, instead of the conventional saturated interaction model using product terms of factors, to study G × E with the data-driven exposure subgroups defined in the first step. We demonstrate useful analytical approaches to translate multiple environmental exposures into one summary class. These tools not only allow researchers to consider several environmental exposures in G × E analysis but also provide some insight into how genes modify the effect of a comprehensive exposure profile instead of examining effect modification for each exposure in isolation.

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies

PubMed Central

Kurbasic, Azra; Poveda, Alaitz; Chen, Yan; Ågren, Åsa; Engberg, Elisabeth; Hu, Frank B.; Johansson, Ingegerd; Barroso, Ines; Brändström, Anders; Hallmans, Göran; Renström, Frida; Franks, Paul W.

2014-01-01

Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics. PMID:25396097

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies.

PubMed

Kurbasic, Azra; Poveda, Alaitz; Chen, Yan; Agren, Asa; Engberg, Elisabeth; Hu, Frank B; Johansson, Ingegerd; Barroso, Ines; Brändström, Anders; Hallmans, Göran; Renström, Frida; Franks, Paul W

2014-12-01

Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics.

Enacting the molecular imperative: How gene-environment interaction research links bodies and environments in the post-genomic age.

PubMed

Darling, Katherine Weatherford; Ackerman, Sara L; Hiatt, Robert H; Lee, Sandra Soo-Jin; Shim, Janet K

2016-04-01

Despite a proclaimed shift from 'nature versus nurture' to 'genes and environment' paradigms within biomedical and genomic science, capturing the environment and identifying gene-environment interactions (GEIs) has remained a challenge. What does 'the environment' mean in the post-genomic age? In this paper, we present qualitative data from a study of 33 principal investigators funded by the U.S. National Institutes of Health to conduct etiological research on three complex diseases (cancer, cardiovascular disease and diabetes). We examine their research practices and perspectives on the environment through the concept of molecularization: the social processes and transformations through which phenomena (diseases, identities, pollution, food, racial/ethnic classifications) are re-defined in terms of their molecular components and described in the language of molecular biology. We show how GEI researchers' expansive conceptualizations of the environment ultimately yield to the imperative to molecularize and personalize the environment. They seek to 'go into the body' and re-work the boundaries between bodies and environments. In the process, they create epistemic hinges to facilitate a turn from efforts to understand social and environmental exposures outside the body, to quantifying their effects inside the body. GEI researchers respond to these emergent imperatives with a mixture of excitement, ambivalence and frustration. We reflect on how GEI researchers struggle to make meaning of molecules in their work, and how they grapple with molecularization as a methodological and rhetorical imperative as well as a process transforming biomedical research practices. Copyright © 2016 Elsevier Ltd. All rights reserved.

Enacting the molecular imperative: How gene-environment interaction research links bodies and environments in the Post-Genomic Age

PubMed Central

Darling, Katherine Weatherford; Ackerman, Sara L.; Hiatt, Robert H.; Lee, Sandra Soo-Jin; Shim, Janet K.

2016-01-01

Despite a proclaimed shift from ‘nature versus nurture’ to ‘genes and environment’ paradigms within biomedical and genomic science, capturing the environment and identifying gene-environment interactions (GEIs) has remained a challenge. What does ‘the environment’ mean in the post-genomic age? In this paper, we present qualitative data from a study of 33 principal investigators funded by the U.S. National Institutes of Health to conduct etiological research on three complex diseases (cancer, cardiovascular disease and diabetes). We examine their research practices and perspectives on the environment through the concept of molecularization: the social processes and transformations through which phenomena (diseases, identities, pollution, food, racial/ethnic classifications) are re-defined in terms of their molecular components and described in the language of molecular biology. We show how GEI researchers’ expansive conceptualizations of the environment ultimately yield to the imperative to molecularize and personalize the environment. They seek to ‘go into the body’ and re-work the boundaries between bodies and environments. In the process, they create epistemic hinges to facilitate a turn from efforts to understand social and environmental exposures outside the body, to quantifying their effects inside the body. GEI researchers respond to these emergent imperatives with a mixture of excitement, ambivalence and frustration. We reflect on how GEI researchers struggle to make meaning of molecules in their work, and how they grapple with molecularization as a methodological and rhetorical imperative as well as a process transforming biomedical research practices. PMID:26994357

Gene-environment interactions linking air pollution and inflammation in Parkinson's disease.

PubMed

Lee, Pei-Chen; Raaschou-Nielsen, Ole; Lill, Christina M; Bertram, Lars; Sinsheimer, Janet S; Hansen, Johnni; Ritz, Beate

2016-11-01

Both air pollution exposure and systemic inflammation have been linked to Parkinson's disease (PD). In the PASIDA study, 408 incident cases of PD diagnosed in 2006-2009 and their 495 population controls were interviewed and provided DNA samples. Markers of long term traffic related air pollution measures were derived from geographic information systems (GIS)-based modeling. Furthermore, we genotyped functional polymorphisms in genes encoding proinflammatory cytokines, namely rs1800629 in TNFα (tumor necrosis factor alpha) and rs16944 in IL1B (interleukin-1β). In logistic regression models, long-term exposure to NO 2 increased PD risk overall (odds ratio (OR)=1.06 per 2.94μg/m 3 increase, 95% CI=1.00-1.13). The OR for PD in individuals with high NO 2 exposure (≧75th percentile) and the AA genotype of IL1B rs16944 was 3.10 (95% CI=1.14-8.38) compared with individuals with lower NO 2 exposure (<75th percentile) and the GG genotype. The interaction term was nominally significant on the multiplicative scale (p=0.01). We did not find significant gene-environment interactions with TNF rs1800629. Our finds may provide suggestive evidence that a combination of traffic-related air pollution and genetic variation in the proinflammatory cytokine gene IL1B contribute to risk of developing PD. However, as statistical evidence was only modest in this large sample we cannot rule out that these results represent a chance finding, and additional replication efforts are warranted. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has

Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior.

PubMed

Rohde, Palle Duun; Gaertner, Bryn; Ward, Kirsty; Sørensen, Peter; Mackay, Trudy F C

2017-08-01

Human psychiatric disorders such as schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype-by-social environmental interaction (GSEI); i.e. , variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments that turned out to be enriched for genome-wide associated variants. A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice. Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders. Copyright © 2017 by the Genetics Society of America.

Gene-environment interaction in the etiology of mathematical ability using SNP sets.

PubMed

Docherty, Sophia J; Kovas, Yulia; Plomin, Robert

2011-01-01

Mathematics ability and disability is as heritable as other cognitive abilities and disabilities, however its genetic etiology has received relatively little attention. In our recent genome-wide association study of mathematical ability in 10-year-old children, 10 SNP associations were nominated from scans of pooled DNA and validated in an individually genotyped sample. In this paper, we use a 'SNP set' composite of these 10 SNPs to investigate gene-environment (GE) interaction, examining whether the association between the 10-SNP set and mathematical ability differs as a function of ten environmental measures in the home and school in a sample of 1888 children with complete data. We found two significant GE interactions for environmental measures in the home and the school both in the direction of the diathesis-stress type of GE interaction: The 10-SNP set was more strongly associated with mathematical ability in chaotic homes and when parents are negative.

Gene-Environment Interaction in the Etiology of Mathematical Ability Using SNP Sets

PubMed Central

Kovas, Yulia; Plomin, Robert

2010-01-01

Mathematics ability and disability is as heritable as other cognitive abilities and disabilities, however its genetic etiology has received relatively little attention. In our recent genome-wide association study of mathematical ability in 10-year-old children, 10 SNP associations were nominated from scans of pooled DNA and validated in an individually genotyped sample. In this paper, we use a ‘SNP set’ composite of these 10 SNPs to investigate gene-environment (GE) interaction, examining whether the association between the 10-SNP set and mathematical ability differs as a function of ten environmental measures in the home and school in a sample of 1888 children with complete data. We found two significant GE interactions for environmental measures in the home and the school both in the direction of the diathesis-stress type of GE interaction: The 10-SNP set was more strongly associated with mathematical ability in chaotic homes and when parents are negative. PMID:20978832

A gene-environment interaction analysis of plasma selenium with prevalent and incident diabetes: The Hortega study.

PubMed

Galan-Chilet, Inmaculada; Grau-Perez, Maria; De Marco, Griselda; Guallar, Eliseo; Martin-Escudero, Juan Carlos; Dominguez-Lucas, Alejandro; Gonzalez-Manzano, Isabel; Lopez-Izquierdo, Raul; Briongos-Figuero, Laisa Socorro; Redon, Josep; Chaves, Felipe Javier; Tellez-Plaza, Maria

2017-08-01

Selenium and single-nucleotide-polymorphisms in selenoprotein genes have been associated to diabetes. However, the interaction of selenium with genetic variation in diabetes and oxidative stress-related genes has not been evaluated as a potential determinant of diabetes risk. We evaluated the cross-sectional and prospective associations of plasma selenium concentrations with type 2 diabetes, and the interaction of selenium concentrations with genetic variation in candidate polymorphisms, in a representative sample of 1452 men and women aged 18-85 years from Spain. The geometric mean of plasma selenium levels in the study sample was 84.2µg/L. 120 participants had diabetes at baseline. Among diabetes-free participants who were not lost during the follow-up (N=1234), 75 developed diabetes over time. The multivariable adjusted odds ratios (95% confidence interval) for diabetes prevalence comparing the second and third to the first tertiles of plasma selenium levels were 1.80 (1.03, 3.14) and 1.97 (1.14, 3.41), respectively. The corresponding hazard ratios (95% CI) for diabetes incidence were 1.76 (0.96, 3.22) and 1.80 (0.98, 3.31), respectively. In addition, we observed significant interactions between selenium and polymorphisms in PPARGC1A, and in genes encoding mitochondrial proteins, such as BCS1L and SDHA, and suggestive interactions of selenium with other genes related to selenoproteins and redox metabolism. Plasma selenium was positively associated with prevalent and incident diabetes. While the statistical interactions of selenium with polymorphisms involved in regulation of redox and insulin signaling pathways provide biological plausibility to the positive associations of selenium with diabetes, further research is needed to elucidate the causal pathways underlying these associations. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Genetic interaction analysis of point mutations enables interrogation of gene function at a residue-level resolution

PubMed Central

Braberg, Hannes; Moehle, Erica A.; Shales, Michael; Guthrie, Christine; Krogan, Nevan J.

2014-01-01

We have achieved a residue-level resolution of genetic interaction mapping – a technique that measures how the function of one gene is affected by the alteration of a second gene – by analyzing point mutations. Here, we describe how to interpret point mutant genetic interactions, and outline key applications for the approach, including interrogation of protein interaction interfaces and active sites, and examination of post-translational modifications. Genetic interaction analysis has proven effective for characterizing cellular processes; however, to date, systematic high-throughput genetic interaction screens have relied on gene deletions or knockdowns, which limits the resolution of gene function analysis and poses problems for multifunctional genes. Our point mutant approach addresses these issues, and further provides a tool for in vivo structure-function analysis that complements traditional biophysical methods. We also discuss the potential for genetic interaction mapping of point mutations in human cells and its application to personalized medicine. PMID:24842270

Computational analysis of gene-gene interactions using multifactor dimensionality reduction.

PubMed

Moore, Jason H

2004-11-01

Understanding the relationship between DNA sequence variations and biologic traits is expected to improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearities in the genotype-to-phenotype mapping relationship as a result of gene-gene interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known as multifactor dimensionality reduction that was specifically designed for the identification of multilocus genetic effects is presented. Several case studies that demonstrate the detection of gene-gene interactions in common diseases such as atrial fibrillation, Type II diabetes and essential hypertension are also discussed.

Media portrayals and health inequalities: a case study of characterizations of Gene x Environment interactions.

PubMed

Horwitz, Allan V

2005-10-01

This article examines how genetic and environmental interactions associated with health inequalities are constructed and framed in the presentation of scientific research. It uses the example of a major article about depression in a longitudinal study of young adults that appeared in Science in 2003. This portrayal of findings related to health inequalities uses a genetic lens that privileges genetic influences and diminishes environmental ones. The emphasis on the genetic side of Gene x Environment interactions can serve to deflect attention away from the important impact of social inequalities on health.

Genome-wide assessment of gene-by-smoking interactions in COPD.

PubMed

Park, Boram; Koo, So-My; An, Jaehoon; Lee, MoonGyu; Kang, Hae Yeon; Qiao, Dandi; Cho, Michael H; Sung, Joohon; Silverman, Edwin K; Yang, Hyeon-Jong; Won, Sungho

2018-06-18

Cigarette smoke exposure is a major risk factor in chronic obstructive pulmonary disease (COPD) and its interactions with genetic variants could affect lung function. However, few gene-smoking interactions have been reported. In this report, we evaluated the effects of gene-smoking interactions on lung function using Korea Associated Resource (KARE) data with the spirometric variables-forced expiratory volume in 1 s (FEV 1 ). We found that variations in FEV 1 were different among smoking status. Thus, we considered a linear mixed model for association analysis under heteroscedasticity according to smoking status. We found a previously identified locus near SOX9 on chromosome 17 to be the most significant based on a joint test of the main and interaction effects of smoking. Smoking interactions were replicated with Gene-Environment of Interaction and phenotype (GENIE), Multi-Ethnic Study of Atherosclerosis-Lung (MESA-Lung), and COPDGene studies. We found that individuals with minor alleles, rs17765644, rs17178251, rs11870732, and rs4793541, tended to have lower FEV 1 values, and lung function decreased much faster with age for smokers. There have been very few reports to replicate a common variant gene-smoking interaction, and our results revealed that statistical models for gene-smoking interaction analyses should be carefully selected.

Explaining human uniqueness: genome interactions with environment, behaviour and culture.

PubMed

Varki, Ajit; Geschwind, Daniel H; Eichler, Evan E

2008-10-01

What makes us human? Specialists in each discipline respond through the lens of their own expertise. In fact, 'anthropogeny' (explaining the origin of humans) requires a transdisciplinary approach that eschews such barriers. Here we take a genomic and genetic perspective towards molecular variation, explore systems analysis of gene expression and discuss an organ-systems approach. Rejecting any 'genes versus environment' dichotomy, we then consider genome interactions with environment, behaviour and culture, finally speculating that aspects of human uniqueness arose because of a primate evolutionary trend towards increasing and irreversible dependence on learned behaviours and culture - perhaps relaxing allowable thresholds for large-scale genomic diversity.

Identifying candidate genes affecting developmental time in Drosophila melanogaster: pervasive pleiotropy and gene-by-environment interaction

PubMed Central

Mensch, Julián; Lavagnino, Nicolás; Carreira, Valeria Paula; Massaldi, Ana; Hasson, Esteban; Fanara, Juan José

2008-01-01

Background Understanding the genetic architecture of ecologically relevant adaptive traits requires the contribution of developmental and evolutionary biology. The time to reach the age of reproduction is a complex life history trait commonly known as developmental time. In particular, in holometabolous insects that occupy ephemeral habitats, like fruit flies, the impact of developmental time on fitness is further exaggerated. The present work is one of the first systematic studies of the genetic basis of developmental time, in which we also evaluate the impact of environmental variation on the expression of the trait. Results We analyzed 179 co-isogenic single P[GT1]-element insertion lines of Drosophila melanogaster to identify novel genes affecting developmental time in flies reared at 25°C. Sixty percent of the lines showed a heterochronic phenotype, suggesting that a large number of genes affect this trait. Mutant lines for the genes Merlin and Karl showed the most extreme phenotypes exhibiting a developmental time reduction and increase, respectively, of over 2 days and 4 days relative to the control (a co-isogenic P-element insertion free line). In addition, a subset of 42 lines selected at random from the initial set of 179 lines was screened at 17°C. Interestingly, the gene-by-environment interaction accounted for 52% of total phenotypic variance. Plastic reaction norms were found for a large number of developmental time candidate genes. Conclusion We identified components of several integrated time-dependent pathways affecting egg-to-adult developmental time in Drosophila. At the same time, we also show that many heterochronic phenotypes may arise from changes in genes involved in several developmental mechanisms that do not explicitly control the timing of specific events. We also demonstrate that many developmental time genes have pleiotropic effects on several adult traits and that the action of most of them is sensitive to temperature during

Gene-environment interaction between the oxytocin receptor (OXTR) gene and parenting behaviour on children's theory of mind.

PubMed

Wade, Mark; Hoffmann, Thomas J; Jenkins, Jennifer M

2015-12-01

Theory of mind (ToM) is the ability to interpret and understand human behaviour by representing the mental states of others. Like many human capacities, ToM is thought to develop through both complex biological and socialization mechanisms. However, no study has examined the joint effect of genetic and environmental influences on ToM. This study examined how variability in the oxytocin receptor gene (OXTR) and parenting behavior--two widely studied factors in ToM development-interacted to predict ToM in pre-school-aged children. Participants were 301 children who were part of an ongoing longitudinal birth cohort study. ToM was assessed at age 4.5 using a previously validated scale. Parenting was assessed through observations of mothers' cognitively sensitive behaviours. Using a family-based association design, it was suggestive that a particular variant (rs11131149) interacted with maternal cognitive sensitivity on children's ToM (P = 0.019). More copies of the major allele were associated with higher ToM as a function of increasing cognitive sensitivity. A sizeable 26% of the variability in ToM was accounted for by this interaction. This study provides the first empirical evidence of gene-environment interactions on ToM, supporting the notion that genetic factors may be modulated by potent environmental influences early in development. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Amphetamine Self-Administration and Dopamine Function: Assessment of Gene x Environment Interactions in Lewis and Fischer 344 Rats

PubMed Central

Meyer, Andrew C.; Bardo, Michael T.

2015-01-01

Rationale Previous research suggests both genetic and environmental influences on substance abuse vulnerability. Objectives The current work sought to investigate the interaction of genes and environment on the acquisition of amphetamine self-administration, as well as amphetamine-stimulated dopamine (DA) release in nucleus accumbens shell using in vivo microdialysis. Methods Inbred Lewis (LEW) and Fischer (F344) rat strains were raised in either an enriched condition (EC), social condition (SC), or isolated condition (IC). Acquisition of amphetamine self-administration (0.1 mg/kg/infusion) was determined across an incrementing daily fixed ratio (FR) schedule. In a separate cohort of rats, extracellular DA and the metabolite dihydroxyphenylacetic acid (DOPAC) were measured in the nucleus accumbens shell following an acute amphetamine injection (1 mg/kg). Results “Addiction-prone” LEW had greater acquisition of amphetamine self-administration on a FR1 schedule compared to “addiction-resistant” F344 when raised in the SC environment. These genetic differences were negated in both the EC and IC environments, with enrichment buffering against self-administration and isolation enhancing self-administration in both strains. On a FR5 schedule, the isolation-induced increase in amphetamine self-administration was greater in F344 than LEW. While no group differences were obtained in extracellular DA, gene x environment differences were obtained in extracellular levels of the metabolite DOPAC. In IC rats only, LEW showed an attenuation in the amphetamine-induced decrease in DOPAC compared to F344. IC LEW rats also had an attenuated DOPAC response to amphetamine compared to EC LEW. Conclusions The current results demonstrate gene x environment interactions in amphetamine self-administration and amphetamine-induced changes in extracellular DOPAC in NAc shell. However, the behavioral and neurochemical differences were not related directly, indicating that mechanisms

Gene–environment interaction in tobacco-related cancers

PubMed Central

Taioli, Emanuela

2008-01-01

This review summarizes the carcinogenic effects of tobacco smoke and the basis for interaction between tobacco smoke and genetic factors. Examples of published papers on gene–tobacco interaction and cancer risk are presented. The assessment of gene–environment interaction in tobacco-related cancers has been more complex than originally expected for several reasons, including the multiplicity of genes involved in tobacco metabolism, the numerous substrates metabolized by the relevant genes and the interaction of smoking with other metabolic pathways. Future studies on gene–environment interaction and cancer risk should include biomarkers of smoking dose, along with markers of quantitative historical exposure to tobacco. Epigenetic studies should be added to classic genetic analyses, in order to better understand gene–environmental interaction and individual susceptibility. Other metabolic pathways in competition with tobacco genetic metabolism/repair should be incorporated in epidemiological studies to generate a more complete picture of individual cancer risk associated with environmental exposure to carcinogens. PMID:18550573

Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution.

PubMed

Keller, Matthew C

2014-01-01

Candidate gene × environment (G × E) interaction research tests the hypothesis that the effects of some environmental variable (e.g., childhood maltreatment) on some outcome measure (e.g., depression) depend on a particular genetic polymorphism. Because this research is inherently nonexperimental, investigators have been rightly concerned that detected interactions could be driven by confounders (e.g., ethnicity, gender, age, socioeconomic status) rather than by the specified genetic or environmental variables per se. In an attempt to eliminate such alternative explanations for detected G × E interactions, investigators routinely enter the potential confounders as covariates in general linear models. However, this practice does not control for the effects these variables might have on the G × E interaction. Rather, to properly control for confounders, researchers need to enter the covariate × environment and the covariate × gene interaction terms in the same model that tests the G × E term. In this manuscript, I demonstrate this point analytically and show that the practice of improperly controlling for covariates is the norm in the G × E interaction literature to date. Thus, many alternative explanations for G × E findings that investigators had thought were eliminated have not been. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.

Gene-Environment Interaction in Externalizing Problems among Adolescents: Evidence from the Pelotas 1993 Birth Cohort Study

ERIC Educational Resources Information Center

Kieling, Christian; Hutz, Mara H.; Genro, Julia P.; Polanczyk, Guilherme V.; Anselmi, Luciana; Camey, Suzi; Hallal, Pedro C.; Barros, Fernando C.; Victora, Cesar G.; Menezes, Ana M. B.; Rohde, Luis Augusto

2013-01-01

Background: The study of gene-environment interactions (G by E) is one of the most promising strategies to uncover the origins of mental disorders. Replication of initial findings, however, is essential because there is a strong possibility of publication bias in the literature. In addition, there is a scarcity of research on the topic originated…

Gene by Environment Interactions Influencing Reading Disability and the Inattentive Symptom Dimension of Attention Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Rosenberg, Jenni; Pennington, Bruce F.; Willcutt, Erik G.; Olson, Richard K.

2012-01-01

Background: Reading disability (RD) and attention deficit/hyperactivity disorder (ADHD) are comorbid and genetically correlated, especially the inattentive dimension of ADHD (ADHD-I). However, previous research indicates that RD and ADHD enter into opposite gene by environment (G x E) interactions. Methods: This study used behavioral genetic…

Gene by environment interactions influencing reading disability and the inattentive symptom dimension of attention deficit/hyperactivity disorder.

PubMed

Rosenberg, Jenni; Pennington, Bruce F; Willcutt, Erik G; Olson, Richard K

2012-03-01

Reading disability (RD) and attention deficit/hyperactivity disorder (ADHD) are comorbid and genetically correlated, especially the inattentive dimension of ADHD (ADHD-I). However, previous research indicates that RD and ADHD enter into opposite gene by environment (G × E) interactions. This study used behavioral genetic methods to replicate these opposite G × E interactions in a sample of same-sex monozygotic and dizygotic twin pairs from the Colorado Learning Disabilities Research Center (CLDRC; DeFries et al., 1997) and to test a genetic hypothesis for why these opposite interactions occur. We replicated opposite G × E interactions for RD (bioecological) and ADHD-I (diathesis-stress) with parental education in the same sample of participants. The genetic hypothesis for this opposite pattern of interactions is that only genes specific to each disorder enter into these opposite interactions, not the shared genes underlying their comorbidity. To test this hypothesis, we used single models with an exploratory three-way interaction, in which the G × E interactions for each disorder were moderated by comorbidity. Neither three-way interaction was significant. The heritability of RD did not vary as a function of parental education and ADHD-I. Similarly, the heritability of ADHD-I did not vary as a function of parental education and RD. We documented opposite G × E interactions in RD and ADHD-I in the same overall twin sample, but the explanation for this apparent paradox remains unclear. Examining specific genes and more specific environmental factors may help resolve the paradox. © 2011 The Authors. Journal of Child Psychology and Psychiatry © 2011 Association for Child and Adolescent Mental Health.

Abundant Gene-by-Environment Interactions in Gene Expression Reaction Norms to Copper within Saccharomyces cerevisiae

PubMed Central

Hodgins-Davis, Andrea; Adomas, Aleksandra B.; Warringer, Jonas; Townsend, Jeffrey P.

2012-01-01

Genetic variation for plastic phenotypes potentially contributes phenotypic variation to populations that can be selected during adaptation to novel ecological contexts. However, the basis and extent of plastic variation that manifests in diverse environments remains elusive. Here, we characterize copper reaction norms for mRNA abundance among five Saccharomyces cerevisiae strains to 1) describe population variation across the full range of ecologically relevant copper concentrations, from starvation to toxicity, and 2) to test the hypothesis that plastic networks exhibit increased population variation for gene expression. We find that although the vast majority of the variation is small in magnitude (considerably <2-fold), not just some, but most genes demonstrate variable expression across environments, across genetic backgrounds, or both. Plastically expressed genes included both genes regulated directly by copper-binding transcription factors Mac1 and Ace1 and genes indirectly responding to the downstream metabolic consequences of the copper gradient, particularly genes involved in copper, iron, and sulfur homeostasis. Copper-regulated gene networks exhibited more similar behavior within the population in environments where those networks have a large impact on fitness. Nevertheless, expression variation in genes like Cup1, important to surviving copper stress, was linked with variation in mitotic fitness and in the breadth of differential expression across the genome. By revealing a broader and deeper range of population variation, our results provide further evidence for the interconnectedness of genome-wide mRNA levels, their dependence on environmental context and genetic background, and the abundance of variation in gene expression that can contribute to future evolution. PMID:23019066

Does parental divorce moderate the heritability of body dissatisfaction? An extension of previous gene-environment interaction effects.

PubMed

O'Connor, Shannon M; Klump, Kelly L; VanHuysse, Jessica L; McGue, Matt; Iacono, William

2016-02-01

Previous research suggests that parental divorce moderates genetic influences on body dissatisfaction. Specifically, the heritability of body dissatisfaction is higher in children of divorced versus intact families, suggesting possible gene-environment interaction effects. However, prior research is limited to a single, self-reported measure of body dissatisfaction. The primary aim of this study was to examine whether these findings extend to a different dimension of body dissatisfaction: body image perceptions. Participants were 1,534 female twins from the Minnesota Twin Family Study, aged 16-20 years. The Body Rating Scale (BRS) was used to assess body image perceptions. Although BRS scores were heritable in twins from divorced and intact families, the heritability estimates in the divorced group were not significantly greater than estimates in the intact group. However, there were differences in nonshared environmental effects, where the magnitude of these environmental influences was larger in the divorced as compared with the intact families. Different dimensions of body dissatisfaction (i.e., negative self-evaluation versus body image perceptions) may interact with environmental risk, such as parental divorce, in discrete ways. Future research should examine this possibility and explore differential gene-environment interactions using diverse measures. © 2015 Wiley Periodicals, Inc.

Genetic risk for violent behavior and environmental exposure to disadvantage and violent crime: the case for gene-environment interaction.

PubMed

Barnes, J C; Jacobs, Bruce A

2013-01-01

Despite mounds of evidence to suggest that neighborhood structural factors predict violent behavior, almost no attention has been given to how these influences work synergistically (i.e., interact) with an individual's genetic propensity toward violent behavior. Indeed, two streams of research have, heretofore, flowed independently of one another. On one hand, criminologists have underscored the importance of neighborhood context in the etiology of violence. On the other hand, behavioral geneticists have argued that individual-level genetic propensities are important for understanding violence. The current study seeks to integrate these two compatible frameworks by exploring gene-environment interactions (GxE). Two GxEs were examined and supported by the data (i.e., the National Longitudinal Study of Adolescent Health). Using a scale of genetic risk based on three dopamine genes, the analysis revealed that genetic risk had a greater influence on violent behavior when the individual was also exposed to neighborhood disadvantage or when the individual was exposed to higher violent crime rates. The relevance of these findings for criminological theorizing was considered.

Gene-environment interaction between adiponectin gene polymorphisms and environmental factors on the risk of diabetic retinopathy.

PubMed

Li, Yuan; Wu, Qun Hong; Jiao, Ming Li; Fan, Xiao Hong; Hu, Quan; Hao, Yan Hua; Liu, Ruo Hong; Zhang, Wei; Cui, Yu; Han, Li Yuan

2015-01-01

To evaluate whether the adiponectin gene is associated with diabetic retinopathy (DR) risk and interaction with environmental factors modifies the DR risk, and to investigate the relationship between serum adiponectin levels and DR. Four adiponectin polymorphisms were evaluated in 372 DR cases and 145 controls. Differences in environmental factors between cases and controls were evaluated by unconditional logistic regression analysis. The model-free multifactor dimensionality reduction method and traditional multiple regression models were applied to explore interactions between the polymorphisms and environmental factors. Using the Bonferroni method, we found no significant associations between four adiponectin polymorphisms and DR susceptibility. Multivariate logistic regression found that physical activity played a protective role in the progress of DR, whereas family history of diabetes (odds ratio 1.75) and insulin therapy (odds ratio 1.78) were associated with an increased risk for DR. The interaction between the C-11377 G (rs266729) polymorphism and insulin therapy might be associated with DR risk. Family history of diabetes combined with insulin therapy also increased the risk of DR. No adiponectin gene polymorphisms influenced the serum adiponectin levels. Serum adiponectin levels did not differ between the DR group and non-DR group. No significant association was identified between four adiponectin polymorphisms and DR susceptibility after stringent Bonferroni correction. The interaction between C-11377G (rs266729) polymorphism and insulin therapy, as well as the interaction between family history of diabetes and insulin therapy, might be associated with DR susceptibility.

Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data.

PubMed

Lobach, Iryna; Mallick, Bani; Carroll, Raymond J

2011-01-01

Case-control studies are widely used to detect gene-environment interactions in the etiology of complex diseases. Many variables that are of interest to biomedical researchers are difficult to measure on an individual level, e.g. nutrient intake, cigarette smoking exposure, long-term toxic exposure. Measurement error causes bias in parameter estimates, thus masking key features of data and leading to loss of power and spurious/masked associations. We develop a Bayesian methodology for analysis of case-control studies for the case when measurement error is present in an environmental covariate and the genetic variable has missing data. This approach offers several advantages. It allows prior information to enter the model to make estimation and inference more precise. The environmental covariates measured exactly are modeled completely nonparametrically. Further, information about the probability of disease can be incorporated in the estimation procedure to improve quality of parameter estimates, what cannot be done in conventional case-control studies. A unique feature of the procedure under investigation is that the analysis is based on a pseudo-likelihood function therefore conventional Bayesian techniques may not be technically correct. We propose an approach using Markov Chain Monte Carlo sampling as well as a computationally simple method based on an asymptotic posterior distribution. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a population-based case-control study of the association between calcium intake with the risk of colorectal adenoma development.

RNA-seq Analysis of Host and Viral Gene Expression Highlights Interaction between Varicella Zoster Virus and Keratinocyte Differentiation

PubMed Central

Singh, Manuraj; Kanda, Ravinder K.; Yee, Michael B.; Kellam, Paul; Hollinshead, Michael; Kinchington, Paul R.; O'Toole, Edel A.; Breuer, Judith

2014-01-01

Varicella zoster virus (VZV) is the etiological agent of chickenpox and shingles, diseases characterized by epidermal skin blistering. Using a calcium-induced keratinocyte differentiation model we investigated the interaction between epidermal differentiation and VZV infection. RNA-seq analysis showed that VZV infection has a profound effect on differentiating keratinocytes, altering the normal process of epidermal gene expression to generate a signature that resembles patterns of gene expression seen in both heritable and acquired skin-blistering disorders. Further investigation by real-time PCR, protein analysis and electron microscopy revealed that VZV specifically reduced expression of specific suprabasal cytokeratins and desmosomal proteins, leading to disruption of epidermal structure and function. These changes were accompanied by an upregulation of kallikreins and serine proteases. Taken together VZV infection promotes blistering and desquamation of the epidermis, both of which are necessary to the viral spread and pathogenesis. At the same time, analysis of the viral transcriptome provided evidence that VZV gene expression was significantly increased following calcium treatment of keratinocytes. Using reporter viruses and immunohistochemistry we confirmed that VZV gene and protein expression in skin is linked with cellular differentiation. These studies highlight the intimate host-pathogen interaction following VZV infection of skin and provide insight into the mechanisms by which VZV remodels the epidermal environment to promote its own replication and spread. PMID:24497829

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

PubMed

Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.

PubMed

Tao, Yebin; Sánchez, Brisa N; Mukherjee, Bhramar

2015-03-30

Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association. Copyright © 2014 John Wiley & Sons, Ltd.

Sleep Duration and Body Mass Index in Twins: A Gene-Environment Interaction

PubMed Central

Watson, Nathaniel F.; Harden, Kathryn Paige; Buchwald, Dedra; Vitiello, Michael V.; Pack, Allan I.; Weigle, David S.; Goldberg, Jack

2012-01-01

Study Objectives: To examine whether sleep duration modifies genetic and environmental influences on body mass index (BMI). Design: Genotype-environment interaction twin study. Setting: University of Washington Twin Registry. Patients or Participants: A population-based sample of US twins (1,088 pairs, 604 monozygotic, 484 dizygotic; 66% female; mean age = 36.6 yr, standard deviation (SD) = 15.9 yr). Interventions: N/A. Measurements and Results: Participants self-reported information on height, weight, and sleep. Mean BMI was calculated as 25.3 kg/m2 (SD = 5.4) and mean habitual sleep duration was 7.2 hr/night (SD = 1.2). Data were analyzed using biometric genetic interaction models. Overall the heritability of sleep duration was 34%. Longer sleep duration was associated with decreased BMI (P < 0.05). The heritability of BMI when sleep duration was < 7 hr (h2 = 70%) was more than twice as large as the heritability of BMI when sleep duration was ≥ 9 hr (h2 = 32%); this interaction was significant (P < 0.05). Conclusions: Shorter sleep duration is associated with increased BMI and increased genetic influences on BMI, suggesting that shorter sleep duration increases expression of genetic risks for high body weight. At the same time, longer sleep duration may suppress genetic influences on body weight. Future research aiming to identify specific genotypes for BMI may benefit by considering the moderating role of sleep duration. Citation: Watson NF; Harden KP; Buchwald D; Vitiello MV; Pack AI; Weigle DS; Goldberg J. Sleep duration and body mass index in twins: a gene-environment interaction. SLEEP 2012;35(5):597-603. PMID:22547885

Gene-Diet Interactions in Childhood Obesity

PubMed Central

Garver, William S

2011-01-01

Childhood overweight and obesity have reached epidemic proportions worldwide, and the increase in weight-associated co-morbidities including premature type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease will soon become major healthcare and economic problems. A number of studies now indicate that the childhood obesity epidemic which has emerged during the past 30 years is a complex multi-factorial disease resulting from interaction of susceptibility genes with an obesogenic environment. This review will focus on gene-diet interactions suspected of having a prominent role in promoting childhood obesity. In particular, the specific genes that will be presented (FTO, MC4R, and NPC1) have recently been associated with childhood obesity through a genome-wide association study (GWAS) and were shown to interact with nutritional components to increase weight gain. Although a fourth gene (APOA2) has not yet been associated with childhood obesity, this review will also present information on what now represents the best characterized gene-diet interaction in promoting weight gain. PMID:22043166

The challenge of causal inference in gene-environment interaction research: leveraging research designs from the social sciences.

PubMed

Fletcher, Jason M; Conley, Dalton

2013-10-01

The integration of genetics and the social sciences will lead to a more complex understanding of the articulation between social and biological processes, although the empirical difficulties inherent in this integration are large. One key challenge is the implications of moving "outside the lab" and away from the experimental tools available for research with model organisms. Social science research methods used to examine human behavior in nonexperimental, real-world settings to date have not been fully taken advantage of during this disciplinary integration, especially in the form of gene-environment interaction research. This article outlines and provides examples of several prominent research designs that should be used in gene-environment research and highlights a key benefit to geneticists of working with social scientists.

Local area disadvantage and gambling involvement and disorder: Evidence for gene-environment correlation and interaction.

PubMed

Slutske, Wendy S; Deutsch, Arielle R; Statham, Dixie J; Martin, Nicholas G

2015-08-01

Previous research has demonstrated that local area characteristics (such as disadvantage and gambling outlet density) and genetic risk factors are associated with gambling involvement and disordered gambling. These 2 lines of research were brought together in the present study by examining the extent to which genetic contributions to individual differences in gambling involvement and disorder contributed to being exposed to, and were also accentuated by, local area disadvantage. Participants were members of the national community-based Australian Twin Registry who completed a telephone interview in which the past-year frequency of gambling and symptoms of disordered gambling were assessed. Indicators of local area disadvantage were based on census data matched to the participants' postal codes. Univariate biometric model-fitting revealed that exposure to area disadvantage was partially explained by genetic factors. Bivariate biometric model-fitting was conducted to examine the evidence for gene-environment interaction while accounting for gene-environment correlation. These analyses demonstrated that: (a) a small portion of the genetic propensity to gamble was explained by moving to or remaining in a disadvantaged area, and (b) the remaining genetic and unique environmental variation in the frequency of participating in electronic machine gambling (among men and women) and symptoms of disordered gambling (among women) was greater in more disadvantaged localities. As the gambling industry continues to grow, it will be important to take into account the multiple contexts in which problematic gambling behavior can emerge-from genes to geography-as well as the ways in which such contexts may interact with each other. (c) 2015 APA, all rights reserved).

Local Area Disadvantage and Gambling Involvement and Disorder: Evidence for Gene-Environment Correlation and Interaction

PubMed Central

Slutske, Wendy S.; Deutsch, Arielle R.; Statham, Dixie B.; Martin, Nicholas G.

2015-01-01

Previous research has demonstrated that local area characteristics (such as disadvantage and gambling outlet density) and genetic risk factors are associated with gambling involvement and disordered gambling. These two lines of research were brought together in the present study by examining the extent to which genetic contributions to individual differences in gambling involvement and disorder contributed to being exposed to, and were also accentuated by, local area disadvantage. Participants were members of the national community-based Australian Twin Registry who completed a telephone interview in which the past-year frequency of gambling and symptoms of disordered gambling were assessed. Indicators of local area disadvantage were based on census data matched to the participants' postal codes. Univariate biometric model-fitting revealed that exposure to area disadvantage was partially explained by genetic factors. Bivariate biometric model-fitting was conducted to examine the evidence for gene-environment interaction while accounting for gene-environment correlation. These analyses demonstrated that: (a) a small portion of the genetic propensity to gamble was explained by moving to or remaining in a disadvantaged area, and (b) the remaining genetic and unique environmental variation in the frequency of participating in electronic machine gambling (among men and women) and symptoms of disordered gambling (among women) was greater in more disadvantaged localities. As the gambling industry continues to grow, it will be important to take into account the multiple contexts in which problematic gambling behavior can emerge -- from genes to geography -- as well as the ways in which such contexts may interact with each other. PMID:26147321

Does Parental Divorce Moderate the Heritability of Body Dissatisfaction? An Extension of Previous Gene-Environment Interaction Effects

PubMed Central

O’Connor, Shannon M.; Klump, Kelly L.; VanHuysse, Jessica L.; McGue, Matt; Iacono, William

2015-01-01

Objective Previous research suggests that parental divorce moderates genetic influences on body dissatisfaction. Specifically, the heritability of body dissatisfaction is higher in children of divorced versus intact families, suggesting possible gene-environment interaction effects. However, prior research is limited to a single, self-report measure of body dissatisfaction. The primary aim of the present study was to examine whether these findings extend to a different dimension of body dissatisfaction, body image perceptions. Method Participants were 1,534 female twins from the Minnesota Twin Family Study, ages 16–20 years. The Body Rating Scale (BRS) was used to assess body image perceptions. Results Although BRS scores were heritable in twins from divorced and intact families, the heritability estimates in the divorced group were not significantly greater than estimates in the intact group. However, there were differences in nonshared environmental effects, where the magnitude of these environmental influences was larger in the divorced as compared to the intact families. Discussion Different dimensions of body dissatisfaction (i.e., negative self-evaluation versus body image perceptions) may interact with environmental risk, such as parental divorce, in discrete ways. Future research should examine this possibility and explore differential gene x environment interactions using diverse measures. PMID:26314278

Analysis of Behavioral and Emotional Problems in Children Highlights the Role of Genotype × Environment Interaction.

PubMed

Molenaar, Dylan; Middeldorp, Christel; van Beijsterveldt, Toos; Boomsma, Dorret I

2015-01-01

This study tested for Genotype × Environment (G × E) interaction on behavioral and emotional problems in children using new methods that do not require identification of candidate genes or environments, can distinguish between interaction with shared and unique environment, and are insensitive to scale effects. Parental ratings of problem behavior from 14,755 twin pairs (5.3 years, SD = 0.22) indicated G × E interaction on emotional liability, social isolation, aggression, attention problems, dependency, anxiety, and physical coordination. Environmental influences increased in children who were genetically more predisposed to problem behavior, with ~20% of the variance due to G × E interaction (8% for anxiety to 37% for attention problems). Ignoring G × E interaction does not greatly bias heritability estimates, but it does offer a comprehensive model of the etiology for childhood problems. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

Evidence for Gender-Dependent Genotype by Environment Interaction in Adult Depression.

PubMed

Molenaar, Dylan; Middeldorp, Christel M; Willemsen, Gonneke; Ligthart, Lannie; Nivard, Michel G; Boomsma, Dorret I

2015-10-14

Depression in adults is heritable with about 40 % of the phenotypic variance due to additive genetic effects and the remaining phenotypic variance due to unique (unshared) environmental effects. Common environmental effects shared by family members are rarely found in adults. One possible explanation for this finding is that there is an interaction between genes and the environment which may mask effects of the common environment. To test this hypothesis, we investigated genotype by environment interaction in a large sample of female and male adult twins aged 18-70 years. The anxious depression subscale of the Adult Self Report from the Achenbach System of Empirically Based Assessment (Achenbach and Rescorla in Manual for the ASEBA adult: forms and profiles, 2003) was completed by 13,022 twins who participate in longitudinal studies of the Netherlands Twin Register. In a single group analysis, we found genotype by unique environment interaction, but no genotype by common environment interaction. However, when conditioning on gender, we observed genotype by common environment interaction in men, with larger common environmental variance in men who are genetically less at risk to develop depression. Although the effect size of the interaction is characterized by large uncertainty, the results show that there is at least some variance due to the common environment in adult depression in men.

Comparative genomics of free-living Gammaproteobacteria: pathogenesis-related genes or interaction-related genes?

PubMed

Vázquez-Rosas-Landa, Mirna; Ponce-Soto, Gabriel Yaxal; Eguiarte, Luis E; Souza, V

2017-07-31

Bacteria have numerous strategies to interact with themselves and with their environment, but genes associated with these interactions are usually cataloged as pathogenic. To understand the role that these genes have not only in pathogenesis but also in bacterial interactions, we compared the genomes of eight bacteria from human-impacted environments with those of free-living bacteria from the Cuatro Ciénegas Basin (CCB), a relatively pristine oligotrophic site. Fifty-one genomes from CCB bacteria, including Pseudomonas, Vibrio, Photobacterium and Aeromonas, were analyzed. We found that the CCB strains had several virulence-related genes, 15 of which were common to all strains and were related to flagella and chemotaxis. We also identified the presence of Type III and VI secretion systems, which leads us to propose that these systems play an important role in interactions among bacterial communities beyond pathogenesis. None of the CCB strains had pathogenicity islands, despite having genes associated with antibiotics. Integrons were rare, while CRISPR elements were common. The idea that pathogenicity-related genes in many cases form part of a wider strategy used by bacteria to interact with other organisms could help us to understand the role of pathogenicity-related elements in an ecological and evolutionary framework leading toward a more inclusive One Health concept. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies.

PubMed

Smith, Jennifer A; Zhao, Wei; Yasutake, Kalyn; August, Carmella; Ratliff, Scott M; Faul, Jessica D; Boerwinkle, Eric; Chakravarti, Aravinda; Diez Roux, Ana V; Gao, Yan; Griswold, Michael E; Heiss, Gerardo; Kardia, Sharon L R; Morrison, Alanna C; Musani, Solomon K; Mwasongwe, Stanford; North, Kari E; Rose, Kathryn M; Sims, Mario; Sun, Yan V; Weir, David R; Needham, Belinda L

2017-12-18

Inter-individual variability in blood pressure (BP) is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific social contexts. In this study, we used a genomic region-based method for longitudinal analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS), to evaluate the effects of interactions between known socioeconomic/psychosocial and genetic risk factors on systolic and diastolic BP in four large epidemiologic cohorts of European and/or African ancestry. After correction for multiple testing, two interactions were significantly associated with diastolic BP. In European ancestry participants, outward/trait anger score had a significant interaction with the C10orf107 genomic region ( p = 0.0019). In African ancestry participants, depressive symptom score had a significant interaction with the HFE genomic region ( p = 0.0048). This study provides a foundation for using genomic region-based longitudinal analysis to identify subgroups of the population that may be at greater risk of elevated BP due to the combined influence of genetic and socioeconomic/psychosocial risk factors.

Conversation analysis as a method for investigating interaction in care home environments.

PubMed

Chatwin, John

2014-11-01

This article gives an outline of how the socio-linguistic approach of conversation analysis can be applied to the analysis of carer-patient interaction in care homes. A single case study from a routine encounter in a residential care home is presented. This is used to show how the conversation analysis method works, the kinds of interactional and communication features it can expose, and what specific contribution this kind of micro-interactional approach may make to improving quality of care in these environments. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Behavioral science and the study of gene-nutrition and gene-physical activity interactions in obesity research.

PubMed

Faith, Myles S

2008-12-01

This report summarizes emerging opportunities for behavioral science to help advance the field of gene-environment and gene-behavior interactions, based on presentations at The National Cancer Institute (NCI) Workshop, "Gene-Nutrition and Gene-Physical Activity Interactions in the Etiology of Obesity." Three opportunities are highlighted: (i) designing potent behavioral "challenges" in experiments, (ii) determining viable behavioral phenotypes for genetics studies, and (iii) identifying specific measures of the environment or environmental exposures. Additional points are underscored, including the need to incorporate novel findings from neuroimaging studies regarding motivation and drive for eating and physical activity. Advances in behavioral science theory and methods can play an important role in advancing understanding of gene-brain-behavior relationships in obesity onset.

Commentary: Gene-Environment Interplay in the Context of Genetics, Epigenetics, and Gene Expression.

ERIC Educational Resources Information Center

Kramer, Douglas A.

2005-01-01

Objective: To comment on the article in this issue of the Journal by Professor Michael Rutter, "Environmentally Mediated Risks for Psychopathology: Research Strategies and Findings," in the context of current research findings on gene-environment interaction, epigenetics, and gene expression. Method: Animal and human studies are reviewed that…

Analysis of Multiple Association Studies Provides Evidence of an Expression QTL Hub in Gene-Gene Interaction Network Affecting HDL Cholesterol Levels

PubMed Central

Ma, Li; Ballantyne, Christie; Brautbar, Ariel; Keinan, Alon

2014-01-01

Epistasis has been suggested to underlie part of the missing heritability in genome-wide association studies. In this study, we first report an analysis of gene-gene interactions affecting HDL cholesterol (HDL-C) levels in a candidate gene study of 2,091 individuals with mixed dyslipidemia from a clinical trial. Two additional studies, the Atherosclerosis Risk in Communities study (ARIC; n = 9,713) and the Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,685), were considered for replication. We identified a gene-gene interaction between rs1532085 and rs12980554 (P = 7.1×10−7) in their effect on HDL-C levels, which is significant after Bonferroni correction (P c = 0.017) for the number of SNP pairs tested. The interaction successfully replicated in the ARIC study (P = 7.0×10−4; P c = 0.02). Rs1532085, an expression QTL (eQTL) of LIPC, is one of the two SNPs involved in another, well-replicated gene-gene interaction underlying HDL-C levels. To further investigate the role of this eQTL SNP in gene-gene interactions affecting HDL-C, we tested in the ARIC study for interaction between this SNP and any other SNP genome-wide. We found the eQTL to be involved in a few suggestive interactions, one of which significantly replicated in MESA. Importantly, these gene-gene interactions, involving only rs1532085, explain an additional 1.4% variation of HDL-C, on top of the 0.65% explained by rs1532085 alone. LIPC plays a key role in the lipid metabolism pathway and it, and rs1532085 in particular, has been associated with HDL-C and other lipid levels. Collectively, we discovered several novel gene-gene interactions, all involving an eQTL of LIPC, thus suggesting a hub role of LIPC in the gene-gene interaction network that regulates HDL-C levels, which in turn raises the hypothesis that LIPC's contribution is largely via interactions with other lipid metabolism related genes. PMID:24651390

A kernel regression approach to gene-gene interaction detection for case-control studies.

PubMed

Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

Cross-fostering: Elucidating the effects of gene×environment interactions on phenotypic development.

PubMed

McCarty, Richard

2017-02-01

Cross-fostering of litters from soon after birth until weaning is a valuable tool to study the ways in which gene×environment interactions program the development of neural, physiological and behavioral characteristics of mammalian species. In laboratory mice and rats, the primary focus of this review, cross-fostering of litters between mothers of different strains or treatment groups (intraspecific) or between mothers of different species (interspecific) has been conducted over the past 9 decades. Areas of particular interest have included maternal effects on emotionality, social preferences, responses to stressful stimulation, nutrition and growth, blood pressure regulation, and epigenetic effects on brain development and behavior. Results from these areas of research highlight the critical role of the postnatal maternal environment in programming the development of offspring phenotypic characteristics. In addition, experimental paradigms that have included cross-fostering have permitted investigators to tease apart prenatal versus postnatal effects of various treatments on offspring development and behavior. Copyright © 2016 Elsevier Ltd. All rights reserved.

Understanding oral stereotypies in calves: alternative strategies, hypothalamic-pituitary-adrenal axis (re)activity and gene by environment interactions.

PubMed

Webb, L E; van Reenen, C G; Engel, B; Berends, H; Gerrits, W J J; Bokkers, E A M

2017-06-01

Stereotypies are used as indicators of poor animal welfare and it is, therefore, important to understand underlying factors mediating their development. In calves, two oral stereotypies, that is, tongue playing and object manipulation, result mostly from insufficient structure in the diet. Three hypotheses were studied: (1) oral stereotypies in calves are one of two alternative strategies, the alternative being hypo-activity; (2) stereotyping and non-stereotyping calves differ in terms of cortisol secretion; (3) oral stereotypy development in calves rests on a gene by environment interaction. Eight-week-old bull calves (n=48) were assigned to one of four solid feed allowances (0, 9, 18 or 27 g dry matter/kg metabolic weight per day) with the following composition: 50% concentrate, 25% maize silage and 25% straw on dry matter basis. The calves received milk replacer in buckets, the provision of which was adjusted to achieve equal growth rates. At 14 to 18 weeks of age, calves were exposed to a challenge, that is, tethering inside cages. Oral stereotypies and inactivity were recorded in the home pens in the 4 weeks before the challenge using instantaneous scan sampling. Salivary cortisol levels were measured at -120, +40, +80, +120 min and +48 h relative to the challenge. Individual differences in behaviour were recorded in the first 30 min after challenge implementation using focal animal sampling and continuous recording, and these elements were entered into a principal component (PC) analysis to extract PCs. Regression analyses were performed to find relationships between stereotypies and inactivity, stereotypies and cortisol, and stereotypies and PCs (individual differences, genes) and solid feed (environment). Relationships between PCs and cortisol were also investigated to help with the interpretation of PCs. Hypotheses 1 and 2 were rejected. Hypothesis 3, however, was supported: calves with a zero solid feed allowance, that is, in the most barren environment

ExAtlas: An interactive online tool for meta-analysis of gene expression data.

PubMed

Sharov, Alexei A; Schlessinger, David; Ko, Minoru S H

2015-12-01

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

The association of interacting neighborhood gene-environment risk with cortisol and blood pressure in African-American adults

PubMed Central

Coulon, Sandra M.; Wilson, Dawn K.; Van Horn, M. L.; Hand, Gregory A.; Kresovich, Stephen

2016-01-01

Background African-American adults are disproportionately affected by stress-related chronic conditions like high blood pressure (BP), and both environmental stress and genetic risk may play a role in its development. Purpose This study tested whether the dual risk of low neighborhood socioeconomic status (SES) and glucocorticoid genetic sensitivity interacted to predict waking cortisol and BP. Methods Cross-sectional waking cortisol and BP were collected from 208 African-American adults who were participating in a follow-up visit as part of the Positive Action for Today’s Health trial. Three single nucleotide polymorphisms were genotyped, salivary cortisol samples were collected, and neighborhood SES was calculated using 2010 Census data. Results The sample was mostly female (65%), with weight classified as overweight or obese (MBMI=32.74, SD=8.88), and a mean age of 55.64 (SD=15.21). The gene-by-neighborhood SES interaction predicted cortisol (B=0.235, p=.001, r2=.036), but not BP. For adults with high genetic risk, waking cortisol was lower with lower SES but higher with higher SES (B=0.87). Lower neighborhood SES was also related to higher systolic BP (B=−0.794, p=.028). Conclusions Findings demonstrated an interaction whereby African-American adults with high genetic sensitivity had high levels of waking cortisol with higher neighborhood SES, and low levels with lower neighborhood SES. This moderation effect is consistent with a differential susceptibility gene-environment pattern, rather than a dual-risk pattern. These findings contribute to a growing body of evidence that demonstrates the importance of investigating complex gene-environment relations in order to better understand stress-related health disparities. PMID:26685668

The developmental origins of externalizing behavioral problems: parental disengagement and the role of gene-environment interplay.

PubMed

Boutwell, Brian B; Beaver, Kevin M; Barnes, James C; Vaske, Jamie

2012-05-30

A line of research has revealed that the influence of genes on behavioral development is closely tied to environmental experiences. Known as gene-environment interaction, research in this area is beginning to reveal that variation in parenting behaviors may moderate genetic influences on antisocial behaviors in children. Despite growing interest in gene-environment interaction research, little evidence exists concerning the role of maternal disengagement in the conditioning of genetic influences on childhood behavioral problems. The current study is intended to address this gap in the literature by analyzing a sample of twin pairs drawn from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B). Analysis of the ECLS-B provided evidence that maternal disengagement moderates genetic influences on the development of externalizing problems. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Gene-gene interactions and gene polymorphisms of VEGFA and EG-VEGF gene systems in recurrent pregnancy loss.

PubMed

Su, Mei-Tsz; Lin, Sheng-Hsiang; Chen, Yi-Chi; Kuo, Pao-Lin

2014-06-01

Both vascular endothelial growth factor A (VEGFA) and endocrine gland-derived vascular endothelial growth factor (EG-VEGF) systems play major roles in angiogenesis. A body of evidence suggests VEGFs regulate critical processes during pregnancy and have been associated with recurrent pregnancy loss (RPL). However, little information is available regarding the interaction of these two major major angiogenesis-related systems in early human pregnancy. This study was conducted to investigate the association of gene polymorphisms and gene-gene interaction among genes in VEGFA and EG-VEGF systems and idiopathic RPL. A total of 98 women with history of idiopathic RPL and 142 controls were included, and 5 functional SNPs selected from VEGFA, KDR, EG-VEGF (PROK1), PROKR1 and PROKR2 were genotyped. We used multifactor dimensionality reduction (MDR) analysis to choose a best model and evaluate gene-gene interactions. Ingenuity pathways analysis (IPA) was introduced to explore possible complex interactions. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL (P<0.01). The MDR test revealed that the KDR (Q472H) polymorphism was the best loci to be associated with RPL (P=0.02). IPA revealed EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3 signaling pathways. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL. EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3.

Understanding the Molecular Mechanisms Underpinning Gene by Environment Interactions in Psychiatric Disorders: The FKBP5 Model.

PubMed

Matosin, Natalie; Halldorsdottir, Thorhildur; Binder, Elisabeth B

2018-05-15

Epidemiologic and genetic studies suggest common environmental and genetic risk factors for a number of psychiatric disorders, including depression, bipolar disorder, and schizophrenia. Genetic and environmental factors, especially adverse life events, not only have main effects on disease development but also may interact to shape risk and resilience. Such gene by adversity interactions have been described for FKBP5, an endogenous regulator of the stress-neuroendocrine system, conferring risk for a number of psychiatric disorders. In this review, we present a molecular and cellular model of the consequences of FKBP5 by early adversity interactions. We illustrate how altered genetic and epigenetic regulation of FKBP5 may contribute to disease risk by covering evidence from clinical and preclinical studies of FKBP5 dysregulation, known cell-type and tissue-type expression patterns of FKBP5 in humans and animals, and the role of FKBP5 as a stress-responsive molecular hub modulating many cellular pathways. FKBP5 presents the possibility to better understand the molecular and cellular factors contributing to a disease-relevant gene by environment interaction, with implications for the development of biomarkers and interventions for psychiatric disorders. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Gene network biological validity based on gene-gene interaction relevance.

PubMed

Gómez-Vela, Francisco; Díaz-Díaz, Norberto

2014-01-01

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

Gene-by-Environment Interactions in Pancreatic Cancer: Implications for Prevention

PubMed Central

Jansen, Rick J.; Tan, Xiang-Lin; Petersen, Gloria M.

2015-01-01

Pancreatic cancer (PC) has been estimated to have higher incidence and correspondingly higher mortality rates in more developed regions worldwide. Overall, the age-adjusted incidence rate is 4.9/105 and age-adjusted mortality rate is at 4.8/105. We review here our current knowledge of modifiable risk factors (cigarette smoking, obesity, diet, and alcohol) for PC, genetic variants implicated by genome-wide association studies, possible genetic interactions with risk factors, and prevention strategies to provide future research directions that may further our understanding of this complex disease. Cigarette smoking is consistently associated with a two-fold increased PC risk. PC associations with dietary intake have been largely inconsistent, with the potential exception of certain unsaturated fatty acids decreasing risk and well-done red meat or meat mutagens increasing risk. There is strong evidence to support that obesity (and related measures) increase risk of PC. Only the heaviest alcohol drinkers seem to be at an increased risk of PC. Currently, key prevention strategies include avoiding tobacco and excessive alcohol consumption and adopting a healthy lifestyle. Screening technologies and PC chemoprevention are likely to become more sophisticated, but may only apply to those at high risk. Risk stratification may be improved by taking into account gene environment interactions. Research on these modifiable risk factors is key to reducing the incidence of PC and understanding who in the population can be considered high risk. PMID:26029010

Explaining human uniqueness: genome interactions with environment, behaviour and culture

PubMed Central

Varki, Ajit; Geschwind, Daniel H.; Eichler, Evan E.

2009-01-01

What makes us human? Specialists in each discipline respond through the lens of their own expertise. In fact, ‘anthropogeny’ (explaining the origin of humans) requires a transdisciplinary approach that eschews such barriers. Here we take a genomic and genetic perspective towards molecular variation, explore systems analysis of gene expression and discuss an organ-systems approach. Rejecting any ‘genes versus environment’ dichotomy, we then consider genome interactions with environment, behaviour and culture, finally speculating that aspects of human uniqueness arose because of a primate evolutionary trend towards increasing and irreversible dependence on learned behaviours and culture — perhaps relaxing allowable thresholds for large-scale genomic diversity. PMID:18802414

A CRISPR Cas9-based gene drive platform for genetic interaction analysis in Candida albicans

PubMed Central

Shapiro, Rebecca S.; Chavez, Alejandro; Porter, Caroline B. M.; Hamblin, Meagan; Kaas, Christian S.; DiCarlo, James E.; Zeng, Guisheng; Xu, Xiaoli; Revtovich, Alexey V.; Kirienko, Natalia V.; Wang, Yue; Church, George M.; Collins, James J.

2018-01-01

Candida albicans is the leading cause of fungal infections; yet, complex genetic interaction analysis remains cumbersome in this diploid pathogen. Here, we developed a CRISPR-Cas9-based ‘gene drive array’ (GDA) platform to facilitate efficient genetic analysis in C. albicans. In our system, a modified DNA donor molecule acts as a selfish genetic element, replaces the targeted site, and propagates to replace additional wild-type loci. Using mating-competent C. albicans haploids, each carrying a different gene drive disabling a gene of interest, we are able to create diploid strains that are homozygous double-deletion mutants. We generate double-gene deletion libraries to demonstrate this technology, targeting antifungal efflux and biofilm adhesion factors. We screen these libraries to identify virulence regulators and determine how genetic networks shift under diverse conditions. This platform transforms our ability to perform genetic interaction analysis in C. albicans and is readily extended to other fungal pathogens. PMID:29062088

Gene-Environment Interplay between Number of Friends and Prosocial Leadership Behavior in Children

ERIC Educational Resources Information Center

Rivizzigno, Alessandra S.; Brendgen, Mara; Feng, Bei; Vitaro, Frank; Dionne, Ginette; Tremblay, Richard E.; Boivin, Michel

2014-01-01

Enriched environments may moderate the effect of genetic factors on prosocial leadership (gene-environment interaction, G × E). However, positive environmental experiences may also themselves be influenced by a genetic disposition for prosocial leadership (gene-environment correlation, rGE). Relating these processes to friendships, the present…

SLC6A1 gene involvement in susceptibility to attention-deficit/hyperactivity disorder: A case-control study and gene-environment interaction.

PubMed

Yuan, Fang-Fen; Gu, Xue; Huang, Xin; Zhong, Yan; Wu, Jing

2017-07-03

Attention-deficit/hyperactivity disorder (ADHD) is an early onset childhood neurodevelopmental disorder with an estimated heritability of approximately 76%. We conducted a case-control study to explore the role of the SLC6A1 gene in ADHD. The genotypes of eight variants were determined using Sequenom MassARRAY technology. The participants in the study were 302 children with ADHD and 411 controls. ADHD symptoms were assessed using the Conners Parent Symptom Questionnaire. In our study, rs2944366 was consistently shown to be associated with the ADHD risk in the dominant model (odds ratio [OR]=0.554, 95% confidence interval [CI]=0.404-0.760), and nominally associated with Hyperactive index score (P=0.027). In addition, rs1170695 has been found to be associated with the ADHD risk in the addictive model (OR=1.457, 95%CI=1.173-1.809), while rs9990174 was associated with the Hyperactive index score (P=0.010). Intriguingly, gene-environmental interactions analysis consistently revealed the potential interactions of rs1170695 with blood lead (P mul =0.044) to modify the ADHD risk. Expression quantitative trait loci analysis suggested that these positive single nucleotide polymorphisms (SNPs) may mediate SLC6A1 gene expression. Therefore, our results suggest that selected SLC6A1 gene variants may have a significant effect on the ADHD risk. Copyright © 2017 Elsevier Inc. All rights reserved.

Gene-Environment Interaction in Adults’ IQ Scores: Measures of Past and Present Environment

PubMed Central

Willemsen, Gonneke; de Geus, Eco J. C.; Boomsma, Dorret I.; Posthuma, Danielle

2008-01-01

Gene-environment interaction was studied in a sample of young (mean age 26 years, N = 385) and older (mean age 49 years, N = 370) adult males and females. Full scale IQ scores (FSIQ) were analyzed using biometric models in which additive genetic (A), common environmental (C), and unique environmental (E) effects were allowed to depend on environmental measures. Moderators under study were parental and partner educational level, as well as urbanization level and mean real estate price of the participants’ residential area. Mean effects were observed for parental education, partner education and urbanization level. On average, FSIQ scores were roughly 5 points higher in participants with highly educated parents, compared to participants whose parents were less well educated. In older participants, IQ scores were about 2 points higher when their partners were highly educated. In younger males, higher urbanization levels were associated with slightly higher FSIQ scores. Our analyses also showed increased common environmental variation in older males whose parents were more highly educated, and increased unique environmental effects in older males living in more affluent areas. Contrary to studies in children, however, the variance attributable to additive genetic effects was stable across all levels of the moderators under study. Most results were replicated for VIQ and PIQ. PMID:18535898

Changes in the striatal proteome of YAC128Q mice exhibit gene-environment interactions between mutant huntingtin and manganese.

PubMed

Wegrzynowicz, Michal; Holt, Hunter K; Friedman, David B; Bowman, Aaron B

2012-02-03

Huntington's disease (HD) is a neurodegenerative disorder caused by expansion of a CAG repeat within the Huntingtin (HTT) gene, though the clinical presentation of disease and age-of-onset are strongly influenced by ill-defined environmental factors. We recently reported a gene-environment interaction wherein expression of mutant HTT is associated with neuroprotection against manganese (Mn) toxicity. Here, we are testing the hypothesis that this interaction may be manifested by altered protein expression patterns in striatum, a primary target of both neurodegeneration in HD and neurotoxicity of Mn. To this end, we compared striatal proteomes of wild-type and HD (YAC128Q) mice exposed to vehicle or Mn. Principal component analysis of proteomic data revealed that Mn exposure disrupted a segregation of WT versus mutant proteomes by the major principal component observed in vehicle-exposed mice. Identification of altered proteins revealed novel markers of Mn toxicity, particularly proteins involved in glycolysis, excitotoxicity, and cytoskeletal dynamics. In addition, YAC128Q-dependent changes suggest that axonal pathology may be an early feature in HD pathogenesis. Finally, for several proteins, genotype-specific responses to Mn were observed. These differences include increased sensitivity to exposure in YAC128Q mice (UBQLN1) and amelioration of some mutant HTT-induced alterations (SAE1, ENO1). We conclude that the interaction of Mn and mutant HTT may suppress proteomic phenotypes of YAC128Q mice, which could reveal potential targets in novel treatment strategies for HD.

Gene-Environment Interplay in Twin Models

PubMed Central

Hatemi, Peter K.

2013-01-01

In this article, we respond to Shultziner’s critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism’s mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise. PMID:24808718

GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores.

PubMed

Chikkagoudar, Satish; Wang, Kai; Li, Mingyao

2011-05-26

Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/.

GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores

PubMed Central

2011-01-01

Background Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Findings Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. Conclusions GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/. PMID:21615923

The Interacting Effect of the BDNF Val66Met Polymorphism and Stressful Life Events on Adolescent Depression Is Not an Artifact of Gene-Environment Correlation: Evidence from a Longitudinal Twin Study

ERIC Educational Resources Information Center

Chen, Jie; Li, Xinying; McGue, Matt

2013-01-01

Background: Confounding introduced by gene-environment correlation (rGE) may prevent one from observing a true gene-environment interaction (G × E) effect on psychopathology. The present study investigated the interacting effect of the BDNF Val66Met polymorphism and stressful life events (SLEs) on adolescent depression while controlling for the…

Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data.

PubMed

Tang, Hongwei; Wei, Peng; Duell, Eric J; Risch, Harvey A; Olson, Sara H; Bueno-de-Mesquita, H Bas; Gallinger, Steven; Holly, Elizabeth A; Petersen, Gloria; Bracci, Paige M; McWilliams, Robert R; Jenab, Mazda; Riboli, Elio; Tjønneland, Anne; Boutron-Ruault, Marie Christine; Kaaks, Rudolph; Trichopoulos, Dimitrios; Panico, Salvatore; Sund, Malin; Peeters, Petra H M; Khaw, Kay-Tee; Amos, Christopher I; Li, Donghui

2014-05-01

Cigarette smoking is the best established modifiable risk factor for pancreatic cancer. Genetic factors that underlie smoking-related pancreatic cancer have previously not been examined at the genome-wide level. Taking advantage of the existing Genome-wide association study (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study in 2028 cases and 2109 controls to examine gene-smoking interactions at pathway/gene/single nucleotide polymorphism (SNP) level. Using the likelihood ratio test nested in logistic regression models and ingenuity pathway analysis (IPA), we examined 172 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, 3 manually curated gene sets, 3 nicotine dependency gene ontology pathways, 17 912 genes and 468 114 SNPs. None of the individual pathway/gene/SNP showed significant interaction with smoking after adjusting for multiple comparisons. Six KEGG pathways showed nominal interactions (P < 0.05) with smoking, and the top two are the pancreatic secretion and salivary secretion pathways (major contributing genes: RAB8A, PLCB and CTRB1). Nine genes, i.e. ZBED2, EXO1, PSG2, SLC36A1, CLSTN1, MTHFSD, FAT2, IL10RB and ATXN2 had P interaction < 0.0005. Five intergenic region SNPs and two SNPs of the EVC and KCNIP4 genes had P interaction < 0.00003. In IPA analysis of genes with nominal interactions with smoking, axonal guidance signaling $$\\left(P=2.12\\times 1{0}^{-7}\\right)$$ and α-adrenergic signaling $$\\left(P=2.52\\times 1{0}^{-5}\\right)$$ genes were significantly overrepresented canonical pathways. Genes contributing to the axon guidance signaling pathway included the SLIT/ROBO signaling genes that were frequently altered in pancreatic cancer. These observations need to be confirmed in additional data set. Once confirmed, it will open a new avenue to unveiling the etiology of smoking-associated pancreatic cancer.

Gene × Smoking Interactions on Human Brain Gene Expression: Finding Common Mechanisms in Adolescents and Adults

ERIC Educational Resources Information Center

Wolock, Samuel L.; Yates, Andrew; Petrill, Stephen A.; Bohland, Jason W.; Blair, Clancy; Li, Ning; Machiraju, Raghu; Huang, Kun; Bartlett, Christopher W.

2013-01-01

Background: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets…

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models

USDA-ARS?s Scientific Manuscript database

Leaf shape traits have long been a focus of many disciplines, but searching for complex genetic and environmental interactive mechanisms regulating leaf shape variation has not yet been well developed. The question of the respective roles of gene and environment and how they interplay to modulate l...

Integrative Analysis of GWASs, Human Protein Interaction, and Gene Expression Identified Gene Modules Associated With BMDs

PubMed Central

He, Hao; Zhang, Lei; Li, Jian; Wang, Yu-Ping; Zhang, Ji-Gang; Shen, Jie; Guo, Yan-Fang

2014-01-01

Context: To date, few systems genetics studies in the bone field have been performed. We designed our study from a systems-level perspective by integrating genome-wide association studies (GWASs), human protein-protein interaction (PPI) network, and gene expression to identify gene modules contributing to osteoporosis risk. Methods: First we searched for modules significantly enriched with bone mineral density (BMD)-associated genes in human PPI network by using 2 large meta-analysis GWAS datasets through a dense module search algorithm. One included 7 individual GWAS samples (Meta7). The other was from the Genetic Factors for Osteoporosis Consortium (GEFOS2). One was assigned as a discovery dataset and the other as an evaluation dataset, and vice versa. Results: In total, 42 modules and 129 modules were identified significantly in both Meta7 and GEFOS2 datasets for femoral neck and spine BMD, respectively. There were 3340 modules identified for hip BMD only in Meta7. As candidate modules, they were assessed for the biological relevance to BMD by gene set enrichment analysis in 2 expression profiles generated from circulating monocytes in subjects with low versus high BMD values. Interestingly, there were 2 modules significantly enriched in monocytes from the low BMD group in both gene expression datasets (nominal P value <.05). Two modules had 16 nonredundant genes. Functional enrichment analysis revealed that both modules were enriched for genes involved in Wnt receptor signaling and osteoblast differentiation. Conclusion: We highlighted 2 modules and novel genes playing important roles in the regulation of bone mass, providing important clues for therapeutic approaches for osteoporosis. PMID:25119315

Genetic Interactions with Prenatal Social Environment: Effects on Academic and Behavioral Outcomes

ERIC Educational Resources Information Center

Conley, Dalton; Rauscher, Emily

2013-01-01

Numerous studies report gene-environment interactions, suggesting that specific alleles have different effects on social outcomes depending on environment. In all these studies, however, environmental conditions are potentially endogenous to unmeasured genetic characteristics. That is, it could be that the observed interaction effects actually…

Interaction between early-life stress and FKBP5 gene variants in major depressive disorder and post-traumatic stress disorder: A systematic review and meta-analysis.

PubMed

Wang, Qingzhong; Shelton, Richard C; Dwivedi, Yogesh

2018-01-01

Gene-environment interaction contributes to the risks of psychiatric disorders. Interactions between FKBP5 gene variants and early-life stress may enhance the risk not only for mood disorder, but also for a number of other behavioral phenotypes. The aim of the present study was to review and conduct a meta-analysis on the results from published studies examining interaction between FKBP5 gene variants and early-life stress and their associations with stress-related disorders such as major depression and PTSD. A literature search was conducted using PsychINFO and PubMed databases until May 2017. A total of 14 studies with a pooled total of 15109 participants met the inclusion criteria, the results of which were combined and a meta-analysis was performed using the differences in correlations as the effect measure. Based on literature, rs1360780, rs3800373, and rs9470080 SNPs were selected within the FKBP5 gene and systematic review was conducted. Based on the Comprehensive Meta-Analysis software, no publication bias was detected. Sensitivity analysis and credibility of meta-analysis results also indicated that the analyses were stable. The meta-analysis showed that individuals who carry T allele of rs1360780, C-allele of rs3800373 or T-allele of rs9470080 exposed to early-life trauma had higher risks for depression or PTSD. The effects of ethnicity, age, sex, and different stress measures were not examined due to limited sample size. These results provide strong evidence of interactions between FKBP5 genotypes and early-life stress, which could pose a significant risk factor for stress-associated disorders such as major depression and PTSD. Copyright © 2017 Elsevier B.V. All rights reserved.

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions

PubMed Central

Luan, Jian'an; Mihailov, Evelin; Metspalu, Andres; Forouhi, Nita G.; Magnusson, Patrik K. E.; Pedersen, Nancy L.; Hallmans, Göran; Chu, Audrey Y.; Justice, Anne E.; Graff, Mariaelisa; Rose, Lynda M.; Langenberg, Claudia; Cupples, L. Adrienne; Ridker, Paul M.; Ong, Ken K.; Loos, Ruth J. F.; Chasman, Daniel I.; Ingelsson, Erik; Kilpeläinen, Tuomas O.; Scott, Robert A.; Mägi, Reedik

2017-01-01

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (Pv), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (Pm). Correlations between Pv and Pm were stronger for SNPs with established marginal effects (Spearman’s ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When Pv and Pm were compared for all pruned SNPs, only BMI was statistically significant (Spearman’s ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the Pv distribution (Pbinomial <0.05). SNPs from the top 1% of the Pm distribution for BMI had more significant Pv values (PMann–Whitney = 1.46×10−5), and the odds ratio of SNPs with nominally significant (<0.05) Pm and Pv was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (Pint<0.05) were enriched with nominally significant Pv values (Pbinomial = 8.63×10−9 and 8.52×10−7 for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them. PMID:28614350

How Genes and the Social Environment Moderate Each Other

PubMed Central

Leve, Leslie D.; Neiderhiser, Jenae M.

2013-01-01

Recent research has suggested that the social environment can moderate the expression of genetic influences on health and that genetic influences can shape an individual’s sensitivity to the social environment. Evidence supports 4 major mechanisms: genes can influence an individual’s response to environmental stress, genes may enhance an individual’s sensitivity to both favorable and adverse environments, inherited characteristics may better fit with some environments than with others, and inherited capabilities may only become manifest in challenging or responsive environments. Further progress depends on better recognition of patterns of gene–environment interaction, improved methods of assessing the environment and its impact on genetic mechanisms, the use of appropriately designed laboratory studies, identification of heritable differences in an individual before environmental moderation occurs, and clarification of the timing of the impact of social and genetic moderation. PMID:23927504

Gene-Environment Interaction in Parkinson's Disease: Coffee, ADORA2A, and CYP1A2.

PubMed

Chuang, Yu-Hsuan; Lill, Christina M; Lee, Pei-Chen; Hansen, Johnni; Lassen, Christina F; Bertram, Lars; Greene, Naomi; Sinsheimer, Janet S; Ritz, Beate

2016-01-01

Drinking caffeinated coffee has been reported to provide protection against Parkinson's disease (PD). Caffeine is an adenosine A2A receptor (encoded by the gene ADORA2A) antagonist that increases dopaminergic neurotransmission and Cytochrome P450 1A2 (gene: CYP1A2) metabolizes caffeine; thus, gene polymorphisms in ADORA2A and CYP1A2 may influence the effect coffee consumption has on PD risk. In a population-based case-control study (PASIDA) in Denmark (1,556 PD patients and 1,606 birth year- and gender-matched controls), we assessed interactions between lifetime coffee consumption and 3 polymorphisms in ADORA2A and CYP1A2 for all subjects, and incident and prevalent PD cases separately using logistic regression models. We also conducted a meta-analysis combining our results with those from previous studies. We estimated statistically significant interactions for ADORA2A rs5760423 and heavy vs. light coffee consumption in incident (OR interaction = 0.66 [95% CI 0.46-0.94], p = 0.02) but not prevalent PD. We did not observe interactions for CYP1A2 rs762551 and rs2472304 in incident or prevalent PD. In meta-analyses, PD associations with daily coffee consumption were strongest among carriers of variant alleles in both ADORA2A and CYP1A2. We corroborated results from a previous report that described interactions between ADORA2A and CYP1A2 polymorphisms and coffee consumption. Our results also suggest that survivor bias may affect results of studies that enroll prevalent PD cases. © 2017 S. Karger AG, Basel.

Beyond the single gene: How epistasis and gene-by-environment effects influence crop domestication.

PubMed

Doust, Andrew N; Lukens, Lewis; Olsen, Kenneth M; Mauro-Herrera, Margarita; Meyer, Ann; Rogers, Kimberly

2014-04-29

Domestication is a multifaceted evolutionary process, involving changes in individual genes, genetic interactions, and emergent phenotypes. There has been extensive discussion of the phenotypic characteristics of plant domestication, and recent research has started to identify the specific genes and mutational mechanisms that control domestication traits. However, there is an apparent disconnect between the simple genetic architecture described for many crop domestication traits, which should facilitate rapid phenotypic change under selection, and the slow rate of change reported from the archeobotanical record. A possible explanation involves the middle ground between individual genetic changes and their expression during development, where gene-by-gene (epistatic) and gene-by-environment interactions can modify the expression of phenotypes and opportunities for selection. These aspects of genetic architecture have the potential to significantly slow the speed of phenotypic evolution during crop domestication and improvement. Here we examine whether epistatic and gene-by-environment interactions have shaped how domestication traits have evolved. We review available evidence from the literature, and we analyze two domestication-related traits, shattering and flowering time, in a mapping population derived from a cross between domesticated foxtail millet and its wild progenitor. We find that compared with wild progenitor alleles, those favored during domestication often have large phenotypic effects and are relatively insensitive to genetic background and environmental effects. Consistent selection should thus be able to rapidly change traits during domestication. We conclude that if phenotypic evolution was slow during crop domestication, this is more likely due to cultural or historical factors than epistatic or environmental constraints.

Gene-environment interplay in the etiology of psychosis.

PubMed

Zwicker, Alyson; Denovan-Wright, Eileen M; Uher, Rudolf

2018-01-15

Schizophrenia and other types of psychosis incur suffering, high health care costs and loss of human potential, due to the combination of early onset and poor response to treatment. Our ability to prevent or cure psychosis depends on knowledge of causal mechanisms. Molecular genetic studies show that thousands of common and rare variants contribute to the genetic risk for psychosis. Epidemiological studies have identified many environmental factors associated with increased risk of psychosis. However, no single genetic or environmental factor is sufficient to cause psychosis on its own. The risk of developing psychosis increases with the accumulation of many genetic risk variants and exposures to multiple adverse environmental factors. Additionally, the impact of environmental exposures likely depends on genetic factors, through gene-environment interactions. Only a few specific gene-environment combinations that lead to increased risk of psychosis have been identified to date. An example of replicable gene-environment interaction is a common polymorphism in the AKT1 gene that makes its carriers sensitive to developing psychosis with regular cannabis use. A synthesis of results from twin studies, molecular genetics, and epidemiological research outlines the many genetic and environmental factors contributing to psychosis. The interplay between these factors needs to be considered to draw a complete picture of etiology. To reach a more complete explanation of psychosis that can inform preventive strategies, future research should focus on longitudinal assessments of multiple environmental exposures within large, genotyped cohorts beginning early in life.

Linking the serotonin transporter gene, family environments, hippocampal volume and depression onset: A prospective imaging gene × environment analysis.

PubMed

Little, Keriann; Olsson, Craig A; Youssef, George J; Whittle, Sarah; Simmons, Julian G; Yücel, Murat; Sheeber, Lisa B; Foley, Debra L; Allen, Nicholas B

2015-11-01

A single imaging gene-environment (IGxE) framework that is able to simultaneously model genetic, neurobiological, and environmental influences on psychopathology outcomes is needed to improve understanding of how complex interrelationships between allelic variation, differences in neuroanatomy or neuroactivity, and environmental experience affect risk for psychiatric disorder. In a longitudinal study of adolescent development we demonstrate the utility of such an IGxE framework by testing whether variation in parental behavior at age 12 altered the strength of an imaging genetics pathway, involving an indirect association between allelic variation in the serotonin transporter gene to variation in hippocampal volume and consequent onset of major depressive disorder by age 18. Results were consistent with the presence of an indirect effect of the serotonin transporter S-allele on depression onset via smaller left and right hippocampal volumes that was significant only in family environments involving either higher levels of parental aggression or lower levels of positive parenting. The previously reported finding of S-allele carriers' increased risk of depression in adverse environments may, therefore, be partly because of the effects of these environments on a neurobiological pathway from the serotonin transporter gene to depression onset that proceeds through variation in hippocampal volume. (c) 2015 APA, all rights reserved).

Microarray analysis of differential gene expression elicited in Trametes versicolor during interspecific mycelial interactions.

PubMed

Eyre, Catherine; Muftah, Wafa; Hiscox, Jennifer; Hunt, Julie; Kille, Peter; Boddy, Lynne; Rogers, Hilary J

2010-08-01

Trametes versicolor is an important white rot fungus of both industrial and ecological interest. Saprotrophic basidiomycetes are the major decomposition agents in woodland ecosystems, and rarely form monospecific populations, therefore interspecific mycelial interactions continually occur. Interactions have different outcomes including replacement of one species by the other or deadlock. We have made subtractive cDNA libraries to enrich for genes that are expressed when T. versicolor interacts with another saprotrophic basidiomycete, Stereum gausapatum, an interaction that results in the replacement of the latter. Expressed sequence tags (ESTs) (1920) were used for microarray analysis, and their expression compared during interaction with three different fungi: S. gausapatum (replaced by T. versicolor), Bjerkandera adusta (deadlock) and Hypholoma fasciculare (replaced T. versicolor). Expression of significantly more probes changed in the interaction between T. versicolor and S. gausapatum or B. adusta compared to H. fasciculare, suggesting a relationship between interaction outcome and changes in gene expression. Copyright © 2010 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

PubMed Central

2013-01-01

Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr. PMID:23586463

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

PubMed

Chen, Edward Y; Tan, Christopher M; Kou, Yan; Duan, Qiaonan; Wang, Zichen; Meirelles, Gabriela Vaz; Clark, Neil R; Ma'ayan, Avi

2013-04-15

System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

Parental Divorce and Disordered Eating: An Investigation of a Gene-Environment Interaction

PubMed Central

Suisman, Jessica Lynn; Burt, S. Alexandra; McGue, Matt; Iacono, William G.; Klump, Kelly L.

2010-01-01

Objective We investigated gene-environment interactions (G×E) for associations between parental divorce and disordered eating (DE). Method Participants were 1,810 female twins from the Michigan State University Twin Registry and the Minnesota Twin Family Study. The Minnesota Eating Behaviors Survey was used to assess DE. We tested for G×E by comparing the heritability of DE in twins from divorced versus intact families. It was hypothesized that divorce would moderate the heritability of DE, in that heritability would be higher in twins from divorced than twins from intact families. Results As expected, the heritability of body dissatisfaction was significantly higher in twins from divorced than intact families. However, genetic influences were equal in twins from divorced and intact families for all other forms of DE. Discussion Although divorce did not moderate heritability of most DE symptoms, future research should replicate G×Es for body dissatisfaction and identify factors underlying this unique relationship. PMID:21312202

Parental divorce and disordered eating: an investigation of a gene-environment interaction.

PubMed

Suisman, Jessica L; Burt, S Alexandra; McGue, Matt; Iacono, William G; Klump, Kelly L

2011-03-01

We investigated gene-environment interactions (GxE) for associations between parental divorce and disordered eating (DE). Participants were 1,810 female twins from the Michigan State University Twin Registry and the Minnesota Twin Family Study. The Minnesota Eating Behaviors Survey was used to assess DE. We tested for GxE by comparing the heritability of DE in twins from divorced versus intact families. It was hypothesized that divorce would moderate the heritability of DE, in that heritability would be higher in twins from divorced than twins from intact families. As expected, the heritability of body dissatisfaction was significantly higher in twins from divorced than intact families. However, genetic influences were equal in twins from divorced and intact families for all other forms of DE. Although divorce did not moderate heritability of most DE symptoms, future research should replicate GxEs for body dissatisfaction and identify factors underlying this unique relationship. Copyright © 2010 Wiley Periodicals, Inc.

Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error.

PubMed

Fan, Qiao; Verhoeven, Virginie J M; Wojciechowski, Robert; Barathi, Veluchamy A; Hysi, Pirro G; Guggenheim, Jeremy A; Höhn, René; Vitart, Veronique; Khawaja, Anthony P; Yamashiro, Kenji; Hosseini, S Mohsen; Lehtimäki, Terho; Lu, Yi; Haller, Toomas; Xie, Jing; Delcourt, Cécile; Pirastu, Mario; Wedenoja, Juho; Gharahkhani, Puya; Venturini, Cristina; Miyake, Masahiro; Hewitt, Alex W; Guo, Xiaobo; Mazur, Johanna; Huffman, Jenifer E; Williams, Katie M; Polasek, Ozren; Campbell, Harry; Rudan, Igor; Vatavuk, Zoran; Wilson, James F; Joshi, Peter K; McMahon, George; St Pourcain, Beate; Evans, David M; Simpson, Claire L; Schwantes-An, Tae-Hwi; Igo, Robert P; Mirshahi, Alireza; Cougnard-Gregoire, Audrey; Bellenguez, Céline; Blettner, Maria; Raitakari, Olli; Kähönen, Mika; Seppala, Ilkka; Zeller, Tanja; Meitinger, Thomas; Ried, Janina S; Gieger, Christian; Portas, Laura; van Leeuwen, Elisabeth M; Amin, Najaf; Uitterlinden, André G; Rivadeneira, Fernando; Hofman, Albert; Vingerling, Johannes R; Wang, Ya Xing; Wang, Xu; Tai-Hui Boh, Eileen; Ikram, M Kamran; Sabanayagam, Charumathi; Gupta, Preeti; Tan, Vincent; Zhou, Lei; Ho, Candice E H; Lim, Wan'e; Beuerman, Roger W; Siantar, Rosalynn; Tai, E-Shyong; Vithana, Eranga; Mihailov, Evelin; Khor, Chiea-Chuen; Hayward, Caroline; Luben, Robert N; Foster, Paul J; Klein, Barbara E K; Klein, Ronald; Wong, Hoi-Suen; Mitchell, Paul; Metspalu, Andres; Aung, Tin; Young, Terri L; He, Mingguang; Pärssinen, Olavi; van Duijn, Cornelia M; Jin Wang, Jie; Williams, Cathy; Jonas, Jost B; Teo, Yik-Ying; Mackey, David A; Oexle, Konrad; Yoshimura, Nagahisa; Paterson, Andrew D; Pfeiffer, Norbert; Wong, Tien-Yin; Baird, Paul N; Stambolian, Dwight; Wilson, Joan E Bailey; Cheng, Ching-Yu; Hammond, Christopher J; Klaver, Caroline C W; Saw, Seang-Mei; Rahi, Jugnoo S; Korobelnik, Jean-François; Kemp, John P; Timpson, Nicholas J; Smith, George Davey; Craig, Jamie E; Burdon, Kathryn P; Fogarty, Rhys D; Iyengar, Sudha K; Chew, Emily; Janmahasatian, Sarayut; Martin, Nicholas G; MacGregor, Stuart; Xu, Liang; Schache, Maria; Nangia, Vinay; Panda-Jonas, Songhomitra; Wright, Alan F; Fondran, Jeremy R; Lass, Jonathan H; Feng, Sheng; Zhao, Jing Hua; Khaw, Kay-Tee; Wareham, Nick J; Rantanen, Taina; Kaprio, Jaakko; Pang, Chi Pui; Chen, Li Jia; Tam, Pancy O; Jhanji, Vishal; Young, Alvin L; Döring, Angela; Raffel, Leslie J; Cotch, Mary-Frances; Li, Xiaohui; Yip, Shea Ping; Yap, Maurice K H; Biino, Ginevra; Vaccargiu, Simona; Fossarello, Maurizio; Fleck, Brian; Yazar, Seyhan; Tideman, Jan Willem L; Tedja, Milly; Deangelis, Margaret M; Morrison, Margaux; Farrer, Lindsay; Zhou, Xiangtian; Chen, Wei; Mizuki, Nobuhisa; Meguro, Akira; Mäkelä, Kari Matti

2016-03-29

Myopia is the most common human eye disorder and it results from complex genetic and environmental causes. The rapidly increasing prevalence of myopia poses a major public health challenge. Here, the CREAM consortium performs a joint meta-analysis to test single-nucleotide polymorphism (SNP) main effects and SNP × education interaction effects on refractive error in 40,036 adults from 25 studies of European ancestry and 10,315 adults from 9 studies of Asian ancestry. In European ancestry individuals, we identify six novel loci (FAM150B-ACP1, LINC00340, FBN1, DIS3L-MAP2K1, ARID2-SNAT1 and SLC14A2) associated with refractive error. In Asian populations, three genome-wide significant loci AREG, GABRR1 and PDE10A also exhibit strong interactions with education (P<8.5 × 10(-5)), whereas the interactions are less evident in Europeans. The discovery of these loci represents an important advance in understanding how gene and environment interactions contribute to the heterogeneity of myopia.

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

PubMed Central

Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng

2013-01-01

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984

Gene-environment interaction: Does fluoride influence the reproductive hormones in male farmers modified by ERα gene polymorphisms?

PubMed

Ma, Qiang; Huang, Hui; Sun, Long; Zhou, Tong; Zhu, Jingyuan; Cheng, Xuemin; Duan, Lijv; Li, Zhiyuan; Cui, Liuxin; Ba, Yue

2017-12-01

The occurrence of endemic fluorosis is derived from high fluoride levels in drinking water and industrial fumes or dust. Reproductive disruption is also a major harm caused by fluoride exposure besides dental and skeletal lesions. However, few studies focus on the mechanism of fluoride exposure on male reproductive function, especially the possible interaction of fluoride exposure and gene polymorphism on male reproductive hormones. Therefore, we conducted a cross-sectional study in rural areas of Henan province in China to explore the interaction between the estrogen receptor alpha (ERα) gene and fluoride exposure on reproductive hormone levels in male farmers living in the endemic fluorosis villages. The results showed that fluoride exposure significantly increased the serum level of estradiol in the hypothalamic-pituitary-testicular (HPT) axis in male farmers. Moreover, the observations indicated that fluoride exposure and genetic markers had an interaction on serum concentration of follicle-stimulating hormone and estradiol, and the interaction among different loci of the ERα gene could impact the serum testosterone level. Findings in the present work suggest that chronic fluoride exposure in drinking water could modulate the levels of reproductive hormones in males living in endemic fluorosis areas, and the interaction between fluoride exposure and ERα polymorphisms might affect the serum levels of hormones in the HPT axis in male farmers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies.

PubMed

Hüls, Anke; Krämer, Ursula; Carlsten, Christopher; Schikowski, Tamara; Ickstadt, Katja; Schwender, Holger

2017-12-16

Weighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study. However, appropriate external weights are not always available. In such situations and in the presence of predominant marginal genetic effects, we have shown in a previous study that GRS with internal weights from marginal genetic effects ("GRS-marginal-internal") are a powerful and reliable alternative to single SNP approaches or the use of unweighted GRS. However, this approach might not be appropriate for detecting predominant interactions, i.e. interactions showing an effect stronger than the marginal genetic effect. In this paper, we present a weighting approach for such predominant interactions ("GRS-interaction-training") in which parts of the data are used to estimate the weights from the interaction terms and the remaining data are used to determine the GRS. We conducted a simulation study for the detection of GxE interactions in which we evaluated power, type I error and sign-misspecification. We compared this new weighting approach to the GRS-marginal-internal approach and to GRS with external weights. Our simulation study showed that in the absence of external weights and with predominant interaction effects, the highest power was reached with the GRS-interaction-training approach. If marginal genetic effects were predominant, the GRS-marginal-internal approach was more appropriate. Furthermore, the power to detect interactions reached by the GRS-interaction-training approach was only slightly lower than the power achieved by GRS with external weights. The power of the GRS-interaction-training approach was confirmed in a real data application to the Traffic, Asthma and Genetics (TAG) Study (N = 4465 observations). When appropriate external weights are unavailable, we

Gene-Environment Studies and Borderline Personality Disorder: A Review

PubMed Central

Carpenter, Ryan W.; Tomko, Rachel L.; Boomsma, Dorret I.

2014-01-01

We review recent gene-environment studies relevant to borderline personality disorder, including those focusing on impulsivity, emotion sensitivity, suicidal behavior, aggression and anger, and the borderline personality phenotype itself. Almost all the studies reviewed suffered from a number of methodological and statistical problems, limiting the conclusions that currently can be drawn. The best evidence to date supports a gene-environment correlation (rGE) model for borderline personality traits and a range of adverse life events, indicating that those at risk for BPD are also at increased risk for exposure to environments that may trigger BPD. We provide suggestions regarding future research on GxE interaction and rGE effects in borderline personality. PMID:23250817

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

PubMed

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

PubMed

Wei, Qing; Khan, Ishita K; Ding, Ziyun; Yerneni, Satwica; Kihara, Daisuke

2017-03-20

The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression.

PubMed

Hüls, Anke; Ickstadt, Katja; Schikowski, Tamara; Krämer, Ursula

2017-06-12

For the analysis of gene-environment (GxE) interactions commonly single nucleotide polymorphisms (SNPs) are used to characterize genetic susceptibility, an approach that mostly lacks power and has poor reproducibility. One promising approach to overcome this problem might be the use of weighted genetic risk scores (GRS), which are defined as weighted sums of risk alleles of gene variants. The gold-standard is to use external weights from published meta-analyses. In this study, we used internal weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression and thereby provided a method that can be used if there are no external weights available. We conducted a simulation study for the detection of GxE interactions and compared power and type I error of single SNPs analyses with Bonferroni correction and corresponding analysis with unweighted and our weighted GRS approach in scenarios with six risk SNPs and an increasing number of highly correlated (up to 210) and noise SNPs (up to 840). Applying weighted GRS increased the power enormously in comparison to the common single SNPs approach (e.g. 94.2% vs. 35.4%, respectively, to detect a weak interaction with an OR ≈ 1.04 for six uncorrelated risk SNPs and n = 700 with a well-controlled type I error). Furthermore, weighted GRS outperformed the unweighted GRS, in particular in the presence of SNPs without any effect on the phenotype (e.g. 90.1% vs. 43.9%, respectively, when 20 noise SNPs were added to the six risk SNPs). This outperforming of the weighted GRS was confirmed in a real data application on lung inflammation in the SALIA cohort (n = 402). However, in scenarios with a high number of noise SNPs (>200 vs. 6 risk SNPs), larger sample sizes are needed to avoid an increased type I error, whereas a high number of correlated SNPs can be handled even in small samples (e.g. n = 400). In conclusion, weighted GRS with weights from the marginal genetic effects of the

DEIVA: a web application for interactive visual analysis of differential gene expression profiles.

PubMed

Harshbarger, Jayson; Kratz, Anton; Carninci, Piero

2017-01-07

Differential gene expression (DGE) analysis is a technique to identify statistically significant differences in RNA abundance for genes or arbitrary features between different biological states. The result of a DGE test is typically further analyzed using statistical software, spreadsheets or custom ad hoc algorithms. We identified a need for a web-based system to share DGE statistical test results, and locate and identify genes in DGE statistical test results with a very low barrier of entry. We have developed DEIVA, a free and open source, browser-based single page application (SPA) with a strong emphasis on being user friendly that enables locating and identifying single or multiple genes in an immediate, interactive, and intuitive manner. By design, DEIVA scales with very large numbers of users and datasets. Compared to existing software, DEIVA offers a unique combination of design decisions that enable inspection and analysis of DGE statistical test results with an emphasis on ease of use.

Environmental heterogeneity generates opposite gene-by-environment interactions for two fitness-related traits within a population.

PubMed

Culumber, Zachary W; Schumer, Molly; Monks, Scott; Tobler, Michael

2015-02-01

Theory predicts that environmental heterogeneity offers a potential solution to the maintenance of genetic variation within populations, but empirical evidence remains sparse. The live-bearing fish Xiphophorus variatus exhibits polymorphism at a single locus, with different alleles resulting in up to five distinct melanistic "tailspot" patterns within populations. We investigated the effects of heterogeneity in two ubiquitous environmental variables (temperature and food availability) on two fitness-related traits (upper thermal limits and body condition) in two different tailspot types (wild-type and upper cut crescent). We found gene-by-environment (G × E) interactions between tailspot type and food level affecting upper thermal limits (UTL), as well as between tailspot type and thermal environment affecting body condition. Exploring mechanistic bases underlying these G × E patterns, we found no differences between tailspot types in hsp70 gene expression despite significant overall increases in expression under both thermal and food stress. Similarly, there was no difference in routine metabolic rates between the tailspot types. The reversal of relative performance of the two tailspot types under different environmental conditions revealed a mechanism by which environmental heterogeneity can balance polymorphism within populations through selection on different fitness-related traits. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies.

PubMed

Yung, Ling Sing; Yang, Can; Wan, Xiang; Yu, Weichuan

2011-05-01

Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions. We implement the BOOST method based on a GPU framework and name it GBOOST. GBOOST achieves a 40-fold speedup compared with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h on a desktop computer equipped with Nvidia GeForce GTX 285 display card. GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.

Evolutionary dynamics of olfactory receptor genes in chordates: interaction between environments and genomic contents

PubMed Central

2009-01-01

Olfaction is essential for the survival of animals. Versatile odour molecules in the environment are received by olfactory receptors (ORs), which form the largest multigene family in vertebrates. Identification of the entire repertories of OR genes using bioinformatics methods from the whole-genome sequences of diverse organisms revealed that the numbers of OR genes vary enormously, ranging from ~1,200 in rats and ~400 in humans to ~150 in zebrafish and ~15 in pufferfish. Most species have a considerable fraction of pseudogenes. Extensive phylogenetic analyses have suggested that the numbers of gene gains and losses are extremely large in the OR gene family, which is a striking example of the birth-and-death evolution. It appears that OR gene repertoires change dynamically, depending on each organism's living environment. For example, higher primates equipped with a well-developed vision system have lost a large number of OR genes. Moreover, two groups of OR genes for detecting airborne odorants greatly expanded after the time of terrestrial adaption in the tetrapod lineage, whereas fishes retain diverse repertoires of genes that were present in aquatic ancestral species. The origin of vertebrate OR genes can be traced back to the common ancestor of all chordate species, but insects, nematodes and echinoderms utilise distinctive families of chemoreceptors, suggesting that chemoreceptor genes have evolved many times independently in animal evolution. PMID:20038498

Glucose Metabolism and AMPK Signaling Regulate Dopaminergic Cell Death Induced by Gene (α-Synuclein)-Environment (Paraquat) Interactions.

PubMed

Anandhan, Annadurai; Lei, Shulei; Levytskyy, Roman; Pappa, Aglaia; Panayiotidis, Mihalis I; Cerny, Ronald L; Khalimonchuk, Oleh; Powers, Robert; Franco, Rodrigo

2017-07-01

While environmental exposures are not the single cause of Parkinson's disease (PD), their interaction with genetic alterations is thought to contribute to neuronal dopaminergic degeneration. However, the mechanisms involved in dopaminergic cell death induced by gene-environment interactions remain unclear. In this work, we have revealed for the first time the role of central carbon metabolism and metabolic dysfunction in dopaminergic cell death induced by the paraquat (PQ)-α-synuclein interaction. The toxicity of PQ in dopaminergic N27 cells was significantly reduced by glucose deprivation, inhibition of hexokinase with 2-deoxy-D-glucose (2-DG), or equimolar substitution of glucose with galactose, which evidenced the contribution of glucose metabolism to PQ-induced cell death. PQ also stimulated an increase in glucose uptake, and in the levels of glucose transporter type 4 (GLUT4) and Na + -glucose transporters isoform 1 (SGLT1) proteins, but only inhibition of GLUT-like transport with STF-31 or ascorbic acid reduced PQ-induced cell death. Importantly, while autophagy protein 5 (ATG5)/unc-51 like autophagy activating kinase 1 (ULK1)-dependent autophagy protected against PQ toxicity, the inhibitory effect of glucose deprivation on cell death progression was largely independent of autophagy or mammalian target of rapamycin (mTOR) signaling. PQ selectively induced metabolomic alterations and adenosine monophosphate-activated protein kinase (AMPK) activation in the midbrain and striatum of mice chronically treated with PQ. Inhibition of AMPK signaling led to metabolic dysfunction and an enhanced sensitivity of dopaminergic cells to PQ. In addition, activation of AMPK by PQ was prevented by inhibition of the inducible nitric oxide syntase (iNOS) with 1400W, but PQ had no effect on iNOS levels. Overexpression of wild type or A53T mutant α-synuclein stimulated glucose accumulation and PQ toxicity, and this toxic synergism was reduced by inhibition of glucose metabolism

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

PubMed Central

Nayak, Renuka R.; Kearns, Michael; Spielman, Richard S.; Cheung, Vivian G.

2009-01-01

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes. PMID:19797678

Gene-Environment Interplay in Common Complex Diseases: Forging an Integrative Model—Recommendations From an NIH Workshop

PubMed Central

Bookman, Ebony B.; McAllister, Kimberly; Gillanders, Elizabeth; Wanke, Kay; Balshaw, David; Rutter, Joni; Reedy, Jill; Shaughnessy, Daniel; Agurs-Collins, Tanya; Paltoo, Dina; Atienza, Audie; Bierut, Laura; Kraft, Peter; Fallin, M. Daniele; Perera, Frederica; Turkheimer, Eric; Boardman, Jason; Marazita, Mary L.; Rappaport, Stephen M.; Boerwinkle, Eric; Suomi, Stephen J.; Caporaso, Neil E.; Hertz-Picciotto, Irva; Jacobson, Kristen C.; Lowe, William L.; Goldman, Lynn R.; Duggal, Priya; Gunnar, Megan R.; Manolio, Teri A.; Green, Eric D.; Olster, Deborah H.; Birnbaum, Linda S.

2011-01-01

Although it is recognized that many common complex diseases are a result of multiple genetic and environmental risk factors, studies of gene-environment interaction remain a challenge and have had limited success to date. Given the current state-of-the-science, NIH sought input on ways to accelerate investigations of gene-environment interplay in health and disease by inviting experts from a variety of disciplines to give advice about the future direction of gene-environment interaction studies. Participants of the NIH Gene-Environment Interplay Workshop agreed that there is a need for continued emphasis on studies of the interplay between genetic and environmental factors in disease and that studies need to be designed around a multifaceted approach to reflect differences in diseases, exposure attributes, and pertinent stages of human development. The participants indicated that both targeted and agnostic approaches have strengths and weaknesses for evaluating main effects of genetic and environmental factors and their interactions. The unique perspectives represented at the workshop allowed the exploration of diverse study designs and analytical strategies, and conveyed the need for an interdisciplinary approach including data sharing, and data harmonization to fully explore gene-environment interactions. Further, participants also emphasized the continued need for high-quality measures of environmental exposures and new genomic technologies in ongoing and new studies. PMID:21308768

Learning contextual gene set interaction networks of cancer with condition specificity

PubMed Central

2013-01-01

Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

PubMed

Fu, Guifang; Dai, Xiaotian; Symanzik, Jürgen; Bushman, Shaun

2017-01-01

Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Interaction between Calpain 5, Peroxisome proliferator-activated receptor-gamma and Peroxisome proliferator-activated receptor-delta genes: a polygenic approach to obesity

PubMed Central

Sáez, María E; Grilo, Antonio; Morón, Francisco J; Manzano, Luis; Martínez-Larrad, María T; González-Pérez, Antonio; Serrano-Hernando, Javier; Ruiz, Agustín; Ramírez-Lorca, Reposo; Serrano-Ríos, Manuel

2008-01-01

Context Obesity is a multifactorial disorder, that is, a disease determined by the combined effect of genes and environment. In this context, polygenic approaches are needed. Objective To investigate the possibility of the existence of a crosstalk between the CALPAIN 10 homologue CALPAIN 5 and nuclear receptors of the peroxisome proliferator-activated receptors family. Design Cross-sectional, genetic association study and gene-gene interaction analysis. Subjects The study sample comprise 1953 individuals, 725 obese (defined as body mass index ≥ 30) and 1228 non obese subjects. Results In the monogenic analysis, only the peroxisome proliferator-activated receptor delta (PPARD) gene was associated with obesity (OR = 1.43 [1.04–1.97], p = 0.027). In addition, we have found a significant interaction between CAPN5 and PPARD genes (p = 0.038) that reduces the risk for obesity in a 55%. Conclusion Our results suggest that CAPN5 and PPARD gene products may also interact in vivo. PMID:18657264

The influence of gene-environment interactions on GHR and IGF-1 expression and their association with growth in brook charr, Salvelinus fontinalis (Mitchill)

PubMed Central

Côté, Guillaume; Perry, Guy; Blier, Pierre; Bernatchez, Louis

2007-01-01

Background Quantitative reaction norm theory proposes that genotype-by-environment interaction (GxE) results from inter-individual differences of expression in adaptive suites of genes in distinct environments. However, environmental norms for actual gene suites are poorly documented. In this study, we investigated the effects of GxE interactions on levels of gene transcription and growth by documenting the impact of rearing environment (freshwater vs. saltwater), sex and genotypic (low vs. high estimated breeding value EBV) effects on the transcription level of insulin-like growth factor (IGF-1) and growth hormone receptor (GHR) in brook charr (Salvelinus fontinalis). Results Males grew faster than females (μ♀ = 1.20 ± 0.07 g·d-1, μ♂ = 1.46 ± 0.06 g·d-1) and high-EBV fish faster than low-EBV fish (μLOW = 0.97 ± 0.05 g·d-1, μHIGH = 1.58 ± 0.07 g·d-1; p < 0.05). However, growth was markedly lower in saltwater-reared fish than freshwater sibs (μFW = 1.52 ± 0.07 g·d-1, μSW = 1.15 ± 0.06 g·d-1), yet GHR mRNA transcription level was significantly higher in saltwater than in freshwater (μSW = 0.85 ± 0.05, μFW = 0.61 ± 0.05). The ratio of actual growth to units in assayed mRNA ('individual transcript efficiency', iTE; g·d-1·u-1) also differed among EBV groups (μLOW = 2.0 ± 0.24 g·d-1·u-1; μHIGH = 3.7 ± 0.24 g·d-1·u-1) and environments (μSW = 2.0 ± 0.25 g·d-1·u-1; μFW = 3.7 ± 0.25 g·d-1·u-1) for GHR. Males had a lower iTE for GHR than females (μ♂ = 2.4 ± 0.29 g·d-1·u-1; μ♀ = 3.1 ± 0.23 g·d-1·u-1). There was no difference in IGF-1 transcription level between environments (p > 0.7) or EBV groups (p > 0.15) but the level of IGF-1 was four times higher in males than females (μ♂ = 2.4 ± 0.11, μ♀ = 0.58 ± 0.09; p < 0.0001). We detected significant sexual differences in iTE (μ♂ = 1.3 ± 0.59 g·d-1·u-1; μ♀ = 3.9 ± 0.47 g·d-1·u-1), salinities (μSW = 2.3 ± 0.52 g·d-1·u-1; μFW = 3.7 ± 0.53 g·d-1·u-1

Machine Learning for Detecting Gene-Gene Interactions

PubMed Central

McKinney, Brett A.; Reif, David M.; Ritchie, Marylyn D.; Moore, Jason H.

2011-01-01

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are ‘the norm’ and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics. PMID:16722772

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases.

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Bacterial plasmid-mediated quinolone resistance genes in aquatic environments in China

PubMed Central

Yan, Lei; Liu, Dan; Wang, Xin-Hua; Wang, Yunkun; Zhang, Bo; Wang, Mingyu; Xu, Hai

2017-01-01

Emerging antimicrobial resistance is a major threat to human’s health in the 21st century. Understanding and combating this issue requires a full and unbiased assessment of the current status on the prevalence of antimicrobial resistance genes and their correlation with each other and bacterial groups. In aquatic environments that are known reservoirs for antimicrobial resistance genes, we were able to reach this goal on plasmid-mediated quinolone resistance (PMQR) genes that lead to resistance to quinolones and possibly also to the co-emergence of resistance to β-lactams. Novel findings were made that qepA and aac-(6′)-Ib genes that were previously regarded as similarly abundant with qnr genes are now dominant among PMQR genes in aquatic environments. Further statistical analysis suggested that the correlation between PMQR and β-lactam resistance genes in the environment is still weak, that the correlations between antimicrobial resistance genes could be weakened by sufficient wastewater treatment, and that the prevalence of PMQR has been implicated in environmental, pathogenic, predatory, anaerobic, and more importantly, human symbiotic bacteria. This work provides a comprehensive analysis of PMQR genes in aquatic environments in Jinan, China, and provides information with which combat with the antimicrobial resistance problem may be fought. PMID:28094345

Assessment of Gene-by-Sex Interaction Effect on Bone Mineral Density

PubMed Central

Liu, Ching-Ti; Estrada, Karol; Yerges-Armstrong, Laura M.; Amin, Najaf; Evangelou, Evangelos; Li, Guo; Minster, Ryan L.; Carless, Melanie A.; Kammerer, Candace M.; Oei, Ling; Zhou, Yanhua; Alonso, Nerea; Dailiana, Zoe; Eriksson, Joel; García-Giralt, Natalia; Giroux, Sylvie; Husted, Lise Bjerre; Khusainova, Rita I.; Koromila, Theodora; Kung, Annie WaiChee; Lewis, Joshua R.; Masi, Laura; Mencej-Bedrac, Simona; Nogues, Xavier; Patel, Millan S.; Prezelj, Janez; Richards, J Brent; Sham, Pak Chung; Spector, Timothy; Vandenput, Liesbeth; Xiao, Su-Mei; Zheng, Hou-Feng; Zhu, Kun; Balcells, Susana; Brandi, Maria Luisa; Frost, Morten; Goltzman, David; González-Macías, Jesús; Karlsson, Magnus; Khusnutdinova, Elza K.; Kollia, Panagoula; Langdahl, Bente Lomholt; Ljunggren, Östen; Lorentzon, Mattias; Marc, Janja; Mellström, Dan; Ohlsson, Claes; Olmos, José M.; Ralston, Stuart H.; Riancho, José A.; Rousseau, François; Urreizti, Roser; Van Hul, Wim; Zarrabeitia, María T.; Castano-Betancourt, Martha; Demissie, Serkalem; Grundberg, Elin; Herrera, Lizbeth; Kwan, Tony; Medina-Gómez, Carolina; Pastinen, Tomi; Sigurdsson, Gunnar; Thorleifsson, Gudmar; vanMeurs, Joyce B.J.; Blangero, John; Hofman, Albert; Liu, Yongmei; Mitchell, Braxton D.; O’Connell, Jeffrey R.; Oostra, Ben A.; Rotter, Jerome I; Stefansson, Kari; Streeten, Elizabeth A.; Styrkarsdottir, Unnur; Thorsteinsdottir, Unnur; Tylavsky, Frances A.; Uitterlinden, Andre; Cauley, Jane A.; Harris, Tamara B.; Ioannidis, John P.A.; Psaty, Bruce M.; Robbins, John A; Zillikens, M. Carola; vanDuijn, Cornelia M.; Prince, Richard L.; Karasik, David; Rivadeneira, Fernando; Kiel, Douglas P.; Cupples, L. Adrienne; Hsu, Yi-Hsiang

2012-01-01

Background Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and performed eQTL analysis and bioinformatics network analysis. Methods We conducted an autosomal genome-wide meta-analysis of gene-by-sex interaction on lumbar spine (LS-) and femoral neck (FN-) BMD, in 25,353 individuals from eight cohorts. In a second stage, we followed up the 12 top SNPs (P<1×10−5) in an additional set of 24,763 individuals. Gene-by-sex interaction and sex-specific effects were examined in these 12 SNPs. Results We detected one novel genome-wide significant interaction associated with LS-BMD at the Chr3p26.1-p25.1 locus, near the GRM7 gene (male effect = 0.02 & p-value = 3.0×10−5; female effect = −0.007 & p-value=3.3×10−2) and eleven suggestive loci associated with either FN- or LS-BMD in discovery cohorts. However, there was no evidence for genome-wide significant (P<5×10−8) gene-by-sex interaction in the joint analysis of discovery and replication cohorts. Conclusion Despite the large collaborative effort, no genome-wide significant evidence for gene-by-sex interaction was found influencing BMD variation in this screen of autosomal markers. If they exist, gene-by-sex interactions for BMD probably have weak effects, accounting for less than 0.08% of the variation in these traits per implicated SNP. PMID:22692763

Consortium analysis of gene and gene-folate interactions in purine and pyrimidine metabolism pathways with ovarian carcinoma risk

PubMed Central

Kelemen, Linda E.; Terry, Kathryn L.; Goodman, Marc T.; Webb, Penelope M.; Bandera, Elisa V.; McGuire, Valerie; Rossing, Mary Anne; Wang, Qinggang; Dicks, Ed; Tyrer, Jonathan P.; Song, Honglin; Kupryjanczyk, Jolanta; Dansonka-Mieszkowska, Agnieszka; Plisiecka-Halasa, Joanna; Timorek, Agnieszka; Menon, Usha; Gentry-Maharaj, Aleksandra; Gayther, Simon A.; Ramus, Susan J.; Narod, Steven A.; Risch, Harvey A.; McLaughlin, John R.; Siddiqui, Nadeem; Glasspool, Rosalind; Paul, James; Carty, Karen; Gronwald, Jacek; Lubiński, Jan; Jakubowska, Anna; Cybulski, Cezary; Kiemeney, Lambertus A.; Massuger, Leon F. A. G.; van Altena, Anne M.; Aben, Katja K. H.; Olson, Sara H.; Orlow, Irene; Cramer, Daniel W.; Levine, Douglas A.; Bisogna, Maria; Giles, Graham G.; Southey, Melissa C.; Bruinsma, Fiona; Kjær, Susanne Krüger; Høgdall, Estrid; Jensen, Allan; Høgdall, Claus K.; Lundvall, Lene; Engelholm, Svend-Aage; Heitz, Florian; du Bois, Andreas; Harter, Philipp; Schwaab, Ira; Butzow, Ralf; Nevanlinna, Heli; Pelttari, Liisa M.; Leminen, Arto; Thompson, Pamela J.; Lurie, Galina; Wilkens, Lynne R.; Lambrechts, Diether; Van Nieuwenhuysen, Els; Lambrechts, Sandrina; Vergote, Ignace; Beesley, Jonathan; Fasching, Peter A.; Beckmann, Matthias W.; Hein, Alexander; Ekici, Arif B.; Doherty, Jennifer A.; Wu, Anna H.; Pearce, Celeste L.; Pike, Malcolm C.; Stram, Daniel; Chang-Claude, Jenny; Rudolph, Anja; Dörk, Thilo; Dürst, Matthias; Hillemanns, Peter; Runnebaum, Ingo B.; Bogdanova, Natalia; Antonenkova, Natalia; Odunsi, Kunle; Edwards, Robert P.; Kelley, Joseph L.; Modugno, Francesmary; Ness, Roberta B.; Karlan, Beth Y.; Walsh, Christine; Lester, Jenny; Orsulic, Sandra; Fridley, Brooke L.; Vierkant, Robert A.; Cunningham, Julie M.; Wu, Xifeng; Lu, Karen; Liang, Dong; Hildebrandt, Michelle A.T.; Weber, Rachel Palmieri; Iversen, Edwin S.; Tworoger, Shelley S.; Poole, Elizabeth M.; Salvesen, Helga B.; Krakstad, Camilla; Bjorge, Line; Tangen, Ingvild L.; Pejovic, Tanja; Bean, Yukie; Kellar, Melissa; Wentzensen, Nicolas; Brinton, Louise A.; Lissowska, Jolanta; Garcia-Closas, Montserrat; Campbell, Ian G.; Eccles, Diana; Whittemore, Alice S.; Sieh, Weiva; Rothstein, Joseph H.; Anton-Culver, Hoda; Ziogas, Argyrios; Phelan, Catherine M.; Moysich, Kirsten B.; Goode, Ellen L.; Schildkraut, Joellen M.; Berchuck, Andrew; Pharoah, Paul D.P.; Sellers, Thomas A.; Brooks-Wilson, Angela; Cook, Linda S.; Le, Nhu D.

2014-01-01

Scope We re-evaluated previously reported associations between variants in pathways of one-carbon (folate) transfer genes and ovarian carcinoma (OC) risk, and in related pathways of purine and pyrimidine metabolism, and assessed interactions with folate intake. Methods and Results Odds ratios (OR) for 446 genetic variants were estimated among 13,410 OC cases and 22,635 controls and among 2,281 cases and 3,444 controls with folate information. Following multiple testing correction, the most significant main effect associations were for DPYD variants rs11587873 (OR=0.92, P=6x10−5) and rs828054 (OR=1.06, P=1x10−4). Thirteen variants in the pyrimidine metabolism genes, DPYD, DPYS, PPAT and TYMS, also interacted significantly with folate in a multi-variant analysis (corrected P=9.9x10−6) but collectively explained only 0.2% of OC risk. Although no other associations were significant after multiple testing correction, variants in SHMT1 in one-carbon transfer, previously reported with OC, suggested lower risk at higher folate (Pinteraction=0.03-0.006). Conclusions Variation in pyrimidine metabolism genes, particularly DPYD, which was previously reported to be associated with OC, may influence risk; however, stratification by folate intake is unlikely to modify disease risk appreciably in these women. SHMT1 SNP-byfolate interactions are plausible but require further validation. Polymorphisms in selected genes in purine metabolism were not associated with OC. PMID:25066213

System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

PubMed Central

Miao, Yingbo; Zhang, Liangcai; Wang, Yang; Feng, Rennan; Yang, Lei; Zhang, Shihua; Jiang, Yongshuai; Liu, Guiyou

2014-01-01

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM. PMID:25243143

PROP taster status interacts with the built environment to influence children's food acceptance and body weight status.

PubMed

Burd, Carlye; Senerat, Araliya; Chambers, Earle; Keller, Kathleen L

2013-04-01

Eating behaviors and obesity are complex phenotypes influenced by genes and the environment, but few studies have investigated the interaction of these two variables. The purpose of this study was to use a gene-environment interaction model to test for differences in children's food acceptance and body weights. Inherited ability to taste 6-n-propylthiouracil (PROP) was assessed as a marker of oral taste responsiveness. Food environment was classified as "healthy" or "unhealthy" based on proximity to outlets that sell fruits/vegetables and fast foods using Geographic Information Systems (GIS). The cohort consisted of 120 children, ages 4-6 years, recruited from New York City over 2005-2010. Home address and other demographic variables were reported by parents and PROP status, food acceptance, and anthropometrics were assessed in the laboratory. Based on a screening test, children were classified as PROP tasters or non-tasters. Hierarchical linear models analysis of variance was performed to examine differences in food acceptance and body mass index (BMI) z-scores as a function of PROP status, the food environment ("healthy" vs. "unhealthy"), and their interaction. Results showed an interaction between taster status and the food environment on BMI z-score and food acceptance. Non-taster children living in healthy food environments had greater acceptance of vegetables than taster children living in healthy food environments (P ≤ 0.005). Moreover, non-tasters from unhealthy food environments had higher BMI z-scores than all other groups (P ≤ 0.005). Incorporating genetic markers of taste into studies that assess the built environment may improve the ability of these measures to predict risk for obesity and eating behaviors. Copyright © 2012 The Obesity Society.

The dopamine D2 receptor gene and depressive and anxious symptoms in childhood: associations and evidence for gene–environment correlation and gene–environment interaction

PubMed Central

Hayden, Elizabeth P.; Klein, Daniel N.; Dougherty, Lea R.; Olino, Thomas M.; Laptook, Rebecca S.; Dyson, Margaret W.; Bufferd, Sara J.; Durbin, C. Emily; Sheikh, Haroon I.; Singh, Shiva M.

2012-01-01

Objectives Research implicates the A1 allele of the dopamine D2 receptor gene (DRD2) Taq1A polymorphism in the development of depression and anxiety. Furthermore, recent papers suggest that children with A1 allele of this gene may receive less positive parenting, and that the effects of this gene on child symptoms may be moderated by parenting. We sought to replicate and extend these findings using behavioral measures in a nonclinical sample of young children. Methods In a sample of 473 preschool-aged children and their mothers, structured clinical interview measures and maternal reports of child symptoms were collected, and standardized observations of parent–child interactions were conducted. Results An association was detected between the DRD2 A1 allele and symptoms of depression and anxiety indexed using interview and parent report methods. As found in previous reports, children with the DRD2 A1 allele received less supportive parenting and displayed higher levels of negative emotionality during parent–child interactions. Tests of mediation and moderation were conducted. Conclusion We found associations between the DRD2 A1 allele and early-emerging anxious and depressive symptoms in a community sample of preschool-aged children, and evidence of a gene–environment correlation and moderation of the main effect of child genotype on child symptoms by parenting. PMID:20526230

Matrix metalloproteinases and educational attainment in refractive error: evidence of gene-environment interactions in the AREDS study

PubMed Central

Wojciechowski, Robert; Yee, Stephanie S.; Simpson, Claire L.; Bailey-Wilson, Joan E.; Stambolian, Dwight

2012-01-01

suggestive evidence of replication of an association signal for ocular refraction to a marker between MMP1 and MMP10. We also provide evidence of a gene-environment interaction between previously-reported markers and education on refractive error. Variants in MMP1- MMP10 and MMP2 regions appear to affect population variation in ocular refraction in environmental conditions less favorable for myopia development. PMID:23098370

Progress in the epidemiological understanding of gene-environment interactions in major diseases: cancer

PubMed Central

Clavel, Jacqueline

2007-01-01

Cancer epidemiology has undergone marked development since the nineteen-fifties. One of the most spectacular and specific contributions was the demonstration of the massive effect of smoking on the occurrence of lung, larynx and bladder cancer. Major chemical, physical and biological carcinogenic agents have been identified in the working environment and in the overall environment. The chain of events from environmental exposures to cancer requires hundreds of polymorphic genes coding for proteins involved in the transport and metabolism of xenobiotics, or in repair, or in an immune or inflammatory response. The multifactorial and multistage characteristics of cancer create the theoretical conditions for statistical interactions which have been exceptionnally detected. Over the last two decades, a considerable mass of data has been generated, mostly addressing the interactions between smoking and xenobiotic-metabolizing enzymes in smoking-related cancers. They are sometimes considered disappointing but they actually brought a lot of information and raised many methodological issues. In parallel, the number of polymorphisms which can be considered candidate per function increased so much that multiple testing has become a major issue, and genome wide screening approaches have more and more gained in interest. Facing the resulting complexity, some instruments are being set up: our studies are now equipped with carefully sampled biological collections, high-throughput genotyping systems are becoming available, work on statistical methodologies is ongoing, bioinformatics databases are growing larger and access to them is becoming simpler; international consortiums are being organized. The roles of environmental and genetic factors are being jointly elucidated. The basic rules of epidemiology, which are demanding with respect to sampling, with respect to the histological and molecular criteria for cancer classification, with respect to the evaluation of environmental

Genotype-Based Association Mapping of Complex Diseases: Gene-Environment Interactions with Multiple Genetic Markers and Measurement Error in Environmental Exposures

PubMed Central

Lobach, Irvna; Fan, Ruzone; Carroll, Raymond T.

2011-01-01

With the advent of dense single nucleotide polymorphism genotyping, population-based association studies have become the major tools for identifying human disease genes and for fine gene mapping of complex traits. We develop a genotype-based approach for association analysis of case-control studies of gene-environment interactions in the case when environmental factors are measured with error and genotype data are available on multiple genetic markers. To directly use the observed genotype data, we propose two genotype-based models: genotype effect and additive effect models. Our approach offers several advantages. First, the proposed risk functions can directly incorporate the observed genotype data while modeling the linkage disequihbrium information in the regression coefficients, thus eliminating the need to infer haplotype phase. Compared with the haplotype-based approach, an estimating procedure based on the proposed methods can be much simpler and significantly faster. In addition, there is no potential risk due to haplotype phase estimation. Further, by fitting the proposed models, it is possible to analyze the risk alleles/variants of complex diseases, including their dominant or additive effects. To model measurement error, we adopt the pseudo-likelihood method by Lobach et al. [2008]. Performance of the proposed method is examined using simulation experiments. An application of our method is illustrated using a population-based case-control study of association between calcium intake with the risk of colorectal adenoma development. PMID:21031455

Neurobehavioral Integrity of Chimpanzee Newborns: Comparisons across groups and across species reveal gene-environment interaction effects

PubMed Central

Bard, Kim A.; Brent, Linda; Lester, Barry; Worobey, John; Suomi, Stephen J.

2014-01-01

The aims of this article are to describe the neurobehavioral integrity of chimpanzee newborns, to investigate how early experiences affect the neurobehavioral organization of chimpanzees, and to explore species differences by comparing chimpanzee newborns to a group of typically developing human newborns. Neurobehavioral integrity related to orientation, motor performance, arousal, and state regulation of 55 chimpanzee (raised in four different settings) and 42 human newborns was measured with the Neonatal Behavioral Assessment Scale (NBAS) a semi-structured 25-minute interactive assessment. Thirty-eight chimpanzees were tested every other day from birth, and analyses revealed significant developmental changes in 19 of 27 NBAS scores. The cross-group and cross-species comparisons were conducted at 2 and 30 days of age. Among the 4 chimpanzee groups, significant differences were found in 23 of 24 NBAS scores. Surprisingly, the cross-species comparisons revealed that the human group was distinct in only 1 of 25 NBAS scores (the human group had significantly less muscle tone than all the chimpanzee groups). The human group was indistinguishable from at least one of the chimpanzee groups in the remaining 24 of 25 NBAS scores. The results of this study support the conclusion that the interplay between genes and environment, rather than genes alone or environment alone, accounts for phenotypic expressions of newborn neurobehavioral integrity in hominids. PMID:25110465

Assessment of gene-by-sex interaction effect on bone mineral density.

PubMed

Liu, Ching-Ti; Estrada, Karol; Yerges-Armstrong, Laura M; Amin, Najaf; Evangelou, Evangelos; Li, Guo; Minster, Ryan L; Carless, Melanie A; Kammerer, Candace M; Oei, Ling; Zhou, Yanhua; Alonso, Nerea; Dailiana, Zoe; Eriksson, Joel; García-Giralt, Natalia; Giroux, Sylvie; Husted, Lise Bjerre; Khusainova, Rita I; Koromila, Theodora; Kung, Annie Waichee; Lewis, Joshua R; Masi, Laura; Mencej-Bedrac, Simona; Nogues, Xavier; Patel, Millan S; Prezelj, Janez; Richards, J Brent; Sham, Pak Chung; Spector, Timothy; Vandenput, Liesbeth; Xiao, Su-Mei; Zheng, Hou-Feng; Zhu, Kun; Balcells, Susana; Brandi, Maria Luisa; Frost, Morten; Goltzman, David; González-Macías, Jesús; Karlsson, Magnus; Khusnutdinova, Elza K; Kollia, Panagoula; Langdahl, Bente Lomholt; Ljunggren, Osten; Lorentzon, Mattias; Marc, Janja; Mellström, Dan; Ohlsson, Claes; Olmos, José M; Ralston, Stuart H; Riancho, José A; Rousseau, François; Urreizti, Roser; Van Hul, Wim; Zarrabeitia, María T; Castano-Betancourt, Martha; Demissie, Serkalem; Grundberg, Elin; Herrera, Lizbeth; Kwan, Tony; Medina-Gómez, Carolina; Pastinen, Tomi; Sigurdsson, Gunnar; Thorleifsson, Gudmar; Vanmeurs, Joyce Bj; Blangero, John; Hofman, Albert; Liu, Yongmei; Mitchell, Braxton D; O'Connell, Jeffrey R; Oostra, Ben A; Rotter, Jerome I; Stefansson, Kari; Streeten, Elizabeth A; Styrkarsdottir, Unnur; Thorsteinsdottir, Unnur; Tylavsky, Frances A; Uitterlinden, Andre; Cauley, Jane A; Harris, Tamara B; Ioannidis, John Pa; Psaty, Bruce M; Robbins, John A; Zillikens, M Carola; Vanduijn, Cornelia M; Prince, Richard L; Karasik, David; Rivadeneira, Fernando; Kiel, Douglas P; Cupples, L Adrienne; Hsu, Yi-Hsiang

2012-10-01

Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however, the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and performed expression quantitative trait loci (eQTL) analysis and bioinformatics network analysis. We conducted an autosomal genome-wide meta-analysis of gene-by-sex interaction on lumbar spine (LS) and femoral neck (FN) BMD in 25,353 individuals from 8 cohorts. In a second stage, we followed up the 12 top single-nucleotide polymorphisms (SNPs; p < 1 × 10(-5) ) in an additional set of 24,763 individuals. Gene-by-sex interaction and sex-specific effects were examined in these 12 SNPs. We detected one novel genome-wide significant interaction associated with LS-BMD at the Chr3p26.1-p25.1 locus, near the GRM7 gene (male effect = 0.02 and p = 3.0 × 10(-5) ; female effect = -0.007 and p = 3.3 × 10(-2) ), and 11 suggestive loci associated with either FN- or LS-BMD in discovery cohorts. However, there was no evidence for genome-wide significant (p < 5 × 10(-8) ) gene-by-sex interaction in the joint analysis of discovery and replication cohorts. Despite the large collaborative effort, no genome-wide significant evidence for gene-by-sex interaction was found to influence BMD variation in this screen of autosomal markers. If they exist, gene-by-sex interactions for BMD probably have weak effects, accounting for less than 0.08% of the variation in these traits per implicated SNP. © 2012 American Society for Bone and Mineral Research. Copyright © 2012 American Society for Bone and Mineral Research.

Case-only gene-environment interaction between ALAD tagSNPs and occupational lead exposure in prostate cancer.

PubMed

Neslund-Dudas, Christine; Levin, Albert M; Rundle, Andrew; Beebe-Dimmer, Jennifer; Bock, Cathryn H; Nock, Nora L; Jankowski, Michelle; Datta, Indrani; Krajenta, Richard; Dou, Q Ping; Mitra, Bharati; Tang, Deliang; Rybicki, Benjamin A

2014-05-01

Black men have historically had higher blood lead levels than white men in the U.S. and have the highest incidence of prostate cancer in the world. Inorganic lead has been classified as a probable human carcinogen. Lead (Pb) inhibits delta-aminolevulinic acid dehydratase (ALAD), a gene recently implicated in other genitourinary cancers. The ALAD enzyme is involved in the second step of heme biosynthesis and is an endogenous inhibitor of the 26S proteasome, a master system for protein degradation and a current target of cancer therapy. Using a case-only study design, we assessed potential gene-environment (G × E) interactions between lifetime occupational Pb exposure and 11 tagSNPs within ALAD in black (N = 260) and white (N = 343) prostate cancer cases. Two ALAD tagSNPs in high linkage disequilibrium showed significant interaction with high Pb exposure among black cases (rs818684 interaction odds ratio or IOR = 2.73, 95% CI 1.43-5.22, P = 0.002; rs818689 IOR = 2.20, 95% CI 1.15-4.21, P = 0.017) and an additional tagSNP, rs2761016, showed G × E interaction with low Pb exposure (IOR = 2.08, 95% CI 1.13-3.84, P = 0.019). Further, the variant allele of rs818684 was associated with a higher Gleason grade in those with high Pb exposure among both blacks (OR 3.96, 95% CI 1.01-15.46, P = 0.048) and whites (OR 2.95, 95% CI 1.18-7.39, P = 0.020). Genetic variation in ALAD may modify associations between Pb and prostate cancer. Additional studies of ALAD, Pb, and prostate cancer are warranted and should include black men. Prostate 74:637-646, 2014. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.

Early Adverse Environments and Genetic Influences on Age at First Sex: Evidence for Gene × Environment Interaction

ERIC Educational Resources Information Center

Carlson, Marie D.; Mendle, Jane; Harden, K. Paige

2014-01-01

Youth who experience adverse environments in early life initiate sexual activity at a younger age, on average, than those from more advantaged circumstances. Evolutionary theorists have posited that ecological stress precipitates earlier reproductive and sexual onset, but it is unclear how stressful environments interact with genetic influences on…

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models.

PubMed

Sul, Jae Hoon; Bilow, Michael; Yang, Wen-Yun; Kostem, Emrah; Furlotte, Nick; He, Dan; Eskin, Eleazar

2016-03-01

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.

Tightly Regulated Expression of Autographa californica Multicapsid Nucleopolyhedrovirus Immediate Early Genes Emerges from Their Interactions and Possible Collective Behaviors

PubMed Central

Taka, Hitomi; Asano, Shin-ichiro; Matsuura, Yoshiharu; Bando, Hisanori

2015-01-01

To infect their hosts, DNA viruses must successfully initiate the expression of viral genes that control subsequent viral gene expression and manipulate the host environment. Viral genes that are immediately expressed upon infection play critical roles in the early infection process. In this study, we investigated the expression and regulation of five canonical regulatory immediate-early (IE) genes of Autographa californica multicapsid nucleopolyhedrovirus: ie0, ie1, ie2, me53, and pe38. A systematic transient gene-expression analysis revealed that these IE genes are generally transactivators, suggesting the existence of a highly interactive regulatory network. A genetic analysis using gene knockout viruses demonstrated that the expression of these IE genes was tolerant to the single deletions of activator IE genes in the early stage of infection. A network graph analysis on the regulatory relationships observed in the transient expression analysis suggested that the robustness of IE gene expression is due to the organization of the IE gene regulatory network and how each IE gene is activated. However, some regulatory relationships detected by the genetic analysis were contradictory to those observed in the transient expression analysis, especially for IE0-mediated regulation. Statistical modeling, combined with genetic analysis using knockout alleles for ie0 and ie1, showed that the repressor function of ie0 was due to the interaction between ie0 and ie1, not ie0 itself. Taken together, these systematic approaches provided insight into the topology and nature of the IE gene regulatory network. PMID:25816136

Have studies of the developmental regulation of behavioral phenotypes revealed the mechanisms of gene-environment interactions?

PubMed Central

Hall, F. Scott; Perona, Maria T. G.

2012-01-01

This review addresses the recent convergence of our long-standing knowledge of the regulation of behavioral phenotypes by developmental experience with recent advances in our understanding of mechanisms regulating gene expression. This review supports a particular perspective on the developmental regulation of behavioral phenotypes: That the role of common developmental experiences (e.g. maternal interactions, peer interactions, exposure to a complex environment, etc.) is to fit individuals to the circumstances of their lives within bounds determined by long-standing (evolutionary) mechanisms that have shaped responses to critical and fundamental types of experience via those aspects of gene structure that regulate gene expression. The phenotype of a given species is not absolute for a given genotype but rather variable within bounds that are determined by mechanisms regulated by experience (e.g. epigenetic mechanisms). This phenotypic variation is not necessarily random, or evenly distributed along a continuum of description or measurement, but often highly disjointed, producing distinct, even opposing, phenotypes. The potentiality for these varying phenotypes is itself the product of evolution, the potential for alternative phenotypes itself conveying evolutionary advantage. Examples of such phenotypic variation, resulting from environmental or experiential influences, have a long history of study in neurobiology, and a number of these will be discussed in this review: neurodevelopmental experiences that produce phenotypic variation in visual perception, cognitive function, and emotional behavior. Although other examples will be discussed, particular emphasis will be made on the role of social behavior on neurodevelopment and phenotypic determination. It will be argued that an important purpose of some aspects of social behavior is regulation of neurobehavioral phenotypes by experience via genetic regulatory mechanisms. PMID:22643448

Gene-environment interaction in atopic diseases: a population-based twin study of early-life exposures.

PubMed

Kahr, Niklas; Naeser, Vibeke; Stensballe, Lone Graff; Kyvik, Kirsten Ohm; Skytthe, Axel; Backer, Vibeke; Bønnelykke, Klaus; Thomsen, Simon Francis

2015-01-01

The development of atopic diseases early in life suggests an important role of perinatal risk factors. To study whether early-life exposures modify the genetic influence on atopic diseases in a twin population. Questionnaire data on atopic diseases from 850 monozygotic and 2279 like-sex dizygotic twin pairs, 3-9 years of age, from the Danish Twin Registry were cross-linked with data on prematurity, Cesarean section, maternal age at birth, parental cohabitation, season of birth and maternal smoking during pregnancy, from the Danish National Birth Registry. Significant predictors of atopic diseases were identified with logistic regression and subsequently tested for genetic effect modification using variance components analysis. After multivariable adjustment, prematurity (gestational age below 32 weeks) [odds ratio (OR) = 1.93, confidence interval (CI) = 1.45-2.56], Cesarean section (OR = 1.25, CI = 1.05-1.49) and maternal smoking during pregnancy (OR = 1.70, CI = 1.42-2.04) significantly influenced the risk of asthma, whereas none of the factors were significantly associated with atopic dermatitis and hay fever. Variance components analysis stratified by exposure status showed no significant change in the heritability of asthma according to the identified risk factors. In this population-based study of children, there was no evidence of genetic effect modification of atopic diseases by several identified early-life risk factors. The causal relationship between these risk factors and atopic diseases may therefore be mediated via mechanisms different from gene-environment interaction. © 2014 John Wiley & Sons Ltd.

PROP taster status interacts with the built environment to influence children's food acceptance and body weight status

PubMed Central

Burd, Carlye; Senerat, Araliya; Chambers, Earle; Keller, Kathleen L.

2012-01-01

Eating behaviors and obesity are complex phenotypes influenced by genes and access to foods in the environment, but few studies have investigated the interaction of these two variables. The purpose of this study was to use a gene-environment interaction model to test for differences in children's food acceptance and body weights. Inherited ability to taste 6-n-propylthiouracil (PROP) was assessed as a marker of oral taste responsiveness. Food environment was classified as “healthy” or “unhealthy” based on proximity to outlets that sell fruits/vegetables and fast foods using Geographic Information Systems (GIS). The cohort consisted of 120 children, ages 4–6 years, recruited from New York City over 2005–2010. Home address and other demographic variables were reported by parents and PROP status, food acceptance, and anthropometrics were assessed in the laboratory. Based on a screening test, children were classified as PROP tasters or non-tasters. Hierarchical linear models analysis of variance was performed to examine differences in food acceptance and body mass index (BMI) z-scores as a function of PROP status, the food environment (“healthy” vs. “unhealthy”), and their interaction. Results showed an interaction between taster status and the food environment on BMI z-score and food acceptance. Non-taster children living in healthy food environments had greater acceptance of vegetables than taster children living in healthy food environments (p≤0.005). Moreover, non-tasters from unhealthy food environments had higher BMI z-scores than all other groups (p≤0.005). Incorporating genetic markers of taste into studies that assess the built environment may improve the ability of these measures to predict risk for obesity and eating behaviors. PMID:23401219

Gene-Diet Interaction and Precision Nutrition in Obesity

PubMed Central

Heianza, Yoriko; Qi, Lu

2017-01-01

The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition. PMID:28387720

Gene essentiality and the topology of protein interaction networks

PubMed Central

Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

2005-01-01

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

Participant Interaction in Asynchronous Learning Environments: Evaluating Interaction Analysis Methods

ERIC Educational Resources Information Center

Blanchette, Judith

2012-01-01

The purpose of this empirical study was to determine the extent to which three different objective analytical methods--sequence analysis, surface cohesion analysis, and lexical cohesion analysis--can most accurately identify specific characteristics of online interaction. Statistically significant differences were found in all points of…

A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility

PubMed Central

Gui, Jiang; Andrew, Angeline S.; Andrews, Peter; Nelson, Heather M.; Kelsey, Karl T.; Karagas, Margaret R.; Moore, Jason H.

2010-01-01

A central goal of human genetics is to identify and characterize susceptibility genes for common complex human diseases. An important challenge in this endeavor is the modeling of gene-gene interaction or epistasis that can result in non-additivity of genetic effects. The multifactor dimensionality reduction (MDR) method was developed as machine learning alternative to parametric logistic regression for detecting interactions in absence of significant marginal effects. The goal of MDR is to reduce the dimensionality inherent in modeling combinations of polymorphisms using a computational approach called constructive induction. Here, we propose a Robust Multifactor Dimensionality Reduction (RMDR) method that performs constructive induction using a Fisher’s Exact Test rather than a predetermined threshold. The advantage of this approach is that only those genotype combinations that are determined to be statistically significant are considered in the MDR analysis. We use two simulation studies to demonstrate that this approach will increase the success rate of MDR when there are only a few genotype combinations that are significantly associated with case-control status. We show that there is no loss of success rate when this is not the case. We then apply the RMDR method to the detection of gene-gene interactions in genotype data from a population-based study of bladder cancer in New Hampshire. PMID:21091664

Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information

PubMed Central

Lopes, Anne; Sacquin-Mora, Sophie; Dimitrova, Viktoriya; Laine, Elodie; Ponty, Yann; Carbone, Alessandra

2013-01-01

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary

The Tangled Tale of Genes and Environment: Moore's The Dependent Gene: The Fallacy of “nature VS. Nurture”

PubMed Central

Schneider, Susan M

2007-01-01

Nature–nurture views that smack of genetic determinism remain prevalent. Yet, the increasing knowledge base shows ever more clearly that environmental factors and genes form a fully interactional system at all levels. Moore's book covers the major topics of discovery and dispute, including behavior genetics and the twin studies, developmental psychobiology, and developmental systems theory. Knowledge of this larger life-sciences context for behavior principles will become increasingly important as the full complexity of gene–environment relations is revealed. Behavior analysis both contributes to and gains from the larger battle for the recognition of how nature and nurture really work.

Child Dopamine Transporter Genotype and Parenting: Evidence for Evocative Gene-Environment Correlations

PubMed Central

Hayden, Elizabeth P.; Hanna, Brigitte; Sheikh, Haroon I.; Laptook, Rebecca S.; Kim, Jiyon; Singh, Shiva M.; Klein, Daniel N.

2017-01-01

The dopamine transporter (DAT1) gene is implicated in psychopathology risk. While the processes by which this gene exerts its effects on risk are poorly understood, a small body of research suggests that DAT1 influences early emerging negative emotionality (NE), a marker of children’s psychopathology risk. As child NE evokes negative parenting practices, the DAT1 may also play a role in gene-environment correlations. To test this model, children (N = 365) were genotyped for DAT1 and participated in standardized parent-child interaction tasks with their primary caregiver. The DAT1 9-repeat variant was associated with child negative affect expressed toward the parent during parent-child interactions, and parents of children with a 9-repeat allele exhibited more hostility and lower guidance/engagement than parents of children without a 9-repeat allele. These gene-environment associations were partially mediated by child negative affect toward the parent. Findings implicate a specific polymorphism in eliciting negative parenting, suggesting that evocative associations play a role in elevating children’s risk for emotional trajectories toward psychopathology risk. PMID:23398760

Human-Computer Interaction in Smart Environments

PubMed Central

Paravati, Gianluca; Gatteschi, Valentina

2015-01-01

Here, we provide an overview of the content of the Special Issue on “Human-computer interaction in smart environments”. The aim of this Special Issue is to highlight technologies and solutions encompassing the use of mass-market sensors in current and emerging applications for interacting with Smart Environments. Selected papers address this topic by analyzing different interaction modalities, including hand/body gestures, face recognition, gaze/eye tracking, biosignal analysis, speech and activity recognition, and related issues.

Genome-environment interactions and prospective technology assessment: evolution from pharmacogenomics to nutrigenomics and ecogenomics.

PubMed

Ozdemir, Vural; Motulsky, Arno G; Kolker, Eugene; Godard, Béatrice

2009-02-01

The relationships between food, nutrition science, and health outcomes have been mapped over the past century. Genomic variation among individuals and populations is a new factor that enriches and challenges our understanding of these complex relationships. Hence, the confluence of nutritional science and genomics-nutrigenomics--was the focus of the OMICS: A Journal of Integrative Biology in December 2008 (Part 1). The 2009 Special Issue (Part 2) concludes the analysis of nutrigenomics research and innovations. Together, these two issues expand the scope and depth of critical scholarship in nutrigenomics, in keeping with an integrated multidisciplinary analysis across the bioscience, omics technology, social, ethical, intellectual property and policy dimensions. Historically, the field of pharmacogenetics provided the first examples of specifically identifiable gene variants predisposing to unexpected responses to drugs since the 1950s. Brewer coined the term ecogenetics in 1971 to broaden the concept of gene-environment interactions from drugs and nutrition to include environmental agents in general. In the mid-1990s, introduction of high-throughput technologies led to the terms pharmacogenomics, nutrigenomics and ecogenomics to describe, respectively, the contribution of genomic variability to differential responses to drugs, food, and environment defined in the broadest sense. The distinctions, if any, between these newer fields (e.g., nutrigenomics) and their predecessors (e.g., nutrigenetics) remain to be delineated. For nutrigenomics, its reliance on genome-wide analyses may lead to detection of new biological mechanisms governing host response to food. Recognizing "genome-environment interactions" as the conceptual thread that connects and runs through pharmacogenomics, nutrigenomics, and ecogenomics may contribute toward anticipatory governance and prospective real-time analysis of these omics fields. Such real-time analysis of omics technologies and

Supportive Family Environments, Genes That Confer Sensitivity, and Allostatic Load Among Rural African American Emerging Adults: A Prospective Analysis

PubMed Central

Brody, Gene H.; Yu, Tianyi; Chen, Yi-fu; Kogan, Steven M.; Evans, Gary W.; Windle, Michael; Gerrard, Meg; Gibbons, Frederick X.; Simons, Ronald L.; Philibert, Robert A.

2012-01-01

The purpose of this study was to investigate interactions between exposure to supportive family environments and genetic characteristics, which were hypothesized to forecast variations in allostatic load (AL) in a representative sample of 315 rural African American youths. Data on family environments were gathered when youths were 11–13, and genetic data were collected when they were 16, years of age. Data on AL were obtained at the beginning of emerging adulthood, age 19 years. The data analyses revealed that, as predicted, emerging adults exposed to less supportive family environments across preadolescence manifested higher levels of AL when they carried the short (s) allele at the 5-HTTLPR and an allele of DRD4 with 7 or more repeats. This is an E(family environment) × G(5-HTTLPR status) × G(DRD4 status) interaction. These data suggest that African American youths carrying genes that confer sensitivity who are exposed to less supportive family environments may be at greater risk for adverse physical health consequences that AL presages. PMID:22468688

Genotype-environment interaction and sociology: contributions and complexities.

PubMed

Seabrook, Jamie A; Avison, William R

2010-05-01

Genotype-environment interaction (G x E) refers to situations in which genetic effects connected to a phenotype are dependent upon variability in the environment, or when genes modify an organism's sensitivity to particular environmental features. Using a typology suggested in the G x E literature, we provide an overview of recent papers that show how social context can trigger a genetic vulnerability, compensate for a genetic vulnerability, control behaviors for which a genetic vulnerability exists, and improve adaptation via proximal causes. We argue that to improve their understanding of social structure, sociologists can take advantage of research in behavior genetics by assessing the impact of within-group variance of various health outcomes and complex human behaviors that are explainable by genotype, environment and their interaction. Insights from life course sociology can aid in ensuring that the dynamic nature of the environment in G x E has been accounted for. Identification of an appropriate entry point for sociologists interested in G x E research could begin with the choice of an environmental feature of interest, a genetic factor of interest, and/or behavior of interest. Optimizing measurement in order to capture the complexity of G x E is critical. Examining the interaction between poorly measured environmental factors and well measured genetic variables will overestimate the effects of genetic variables while underestimating the effect of environmental influences, thereby distorting the interaction between genotype and environment. Although the expense of collecting environmental data is very high, reliable and precise measurement of an environmental pathogen enhances a study's statistical power. Copyright 2010 Elsevier Ltd. All rights reserved.

Gene-by-Socioeconomic Status Interaction on School Readiness

PubMed Central

Rhemtulla, Mijke; Tucker-Drob, Elliot M.

2017-01-01

In previous work with a nationally representative sample of over 1,400 monozygotic and dizygotic twins born in the United States, Tucker-Drob, Rhemtulla, Harden, Turkheimer, and Fask (2011; Psychological Science, 22, 125–133) uncovered a gene × environment interaction on scores on the Bayley Short Form test of mental ability at 2 years of age—higher socioeconomic status (SES) was associated not only with higher mental ability, but also with larger genetic contributions to individual differences in mental ability. The current study examined gene × SES interactions in mathematics skill and reading skill at 4 years of age (preschool age) in the same sample of twins, and further examined whether interactions detected at 4 years could be attributed to the persistence of the interaction previously observed at 2 years. For early mathematics skill but not early reading skill, genetic influences were more pronounced at higher levels of SES. This interaction was not accounted for by the interaction observed at 2 years. These findings indicate that SES moderates the etiological influences on certain cognitive functions at multiple stages of child development. PMID:22350185

Research Review: Gene-Environment Interaction Research in Youth Depression--A Systematic Review with Recommendations for Future Research

ERIC Educational Resources Information Center

Dunn, Erin C.; Uddin, Monica; Subramanian, S. V.; Smoller, Jordan W.; Galea, Sandro; Koenen, Karestan C.

2011-01-01

Background: Depression is a major public health problem among youth, currently estimated to affect as many as 9% of US children and adolescents. The recognition that both genes (nature) and environments (nurture) are important for understanding the etiology of depression has led to a rapid growth in research exploring gene-environment interactions…

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

PubMed Central

Hartman, John L.; Stisher, Chandler; Outlaw, Darryl A.; Guo, Jingyu; Shah, Najaf A.; Tian, Dehua; Santos, Sean M.; Rodgers, John W.; White, Richard A.

2015-01-01

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease. PMID:25668739

Complex Genotype by Environment interactions and changing genetic architectures across thermal environments in the Australian field cricket, Teleogryllus oceanicus

PubMed Central

2011-01-01

Background Biologists studying adaptation under sexual selection have spent considerable effort assessing the relative importance of two groups of models, which hinge on the idea that females gain indirect benefits via mate discrimination. These are the good genes and genetic compatibility models. Quantitative genetic studies have advanced our understanding of these models by enabling assessment of whether the genetic architectures underlying focal phenotypes are congruent with either model. In this context, good genes models require underlying additive genetic variance, while compatibility models require non-additive variance. Currently, we know very little about how the expression of genotypes comprised of distinct parental haplotypes, or how levels and types of genetic variance underlying key phenotypes, change across environments. Such knowledge is important, however, because genotype-environment interactions can have major implications on the potential for evolutionary responses to selection. Results We used a full diallel breeding design to screen for complex genotype-environment interactions, and genetic architectures underlying key morphological traits, across two thermal environments (the lab standard 27°C, and the cooler 23°C) in the Australian field cricket, Teleogryllus oceanicus. In males, complex three-way interactions between sire and dam parental haplotypes and the rearing environment accounted for up to 23 per cent of the scaled phenotypic variance in the traits we measured (body mass, pronotum width and testes mass), and each trait harboured significant additive genetic variance in the standard temperature (27°C) only. In females, these three-way interactions were less important, with interactions between the paternal haplotype and rearing environment accounting for about ten per cent of the phenotypic variance (in body mass, pronotum width and ovary mass). Of the female traits measured, only ovary mass for crickets reared at the cooler

Gene expression analysis uncovers novel Hedgehog interacting protein (HHIP) effects in human bronchial epithelial cells

PubMed Central

Zhou, Xiaobo; Qiu, Weiliang; Sathirapongsasuti, J. Fah.; Cho, Michael H.; Mancini, John D.; Lao, Taotao; Thibault, Derek M.; Litonjua, Gus; Bakke, Per S.; Gulsvik, Amund; Lomas, David A.; Beaty, Terri H.; Hersh, Craig P.; Anderson, Christopher; Geigenmuller, Ute; Raby, Benjamin A.; Rennard, Stephen I.; Perrella, Mark A.; Choi, Augustine M.K.; Quackenbush, John; Silverman, Edwin K.

2013-01-01

Hedgehog Interacting Protein (HHIP) was implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS). However, it remains unclear how HHIP contributes to COPD pathogenesis. To identify genes regulated by HHIP, we performed gene expression microarray analysis in a human bronchial epithelial cell line (Beas-2B) stably infected with HHIP shRNAs. HHIP silencing led to differential expression of 296 genes; enrichment for variants nominally associated with COPD was found. Eighteen of the differentially expressed genes were validated by real-time PCR in Beas-2B cells. Seven of 11 validated genes tested in human COPD and control lung tissues demonstrated significant gene expression differences. Functional annotation indicated enrichment for extracellular matrix and cell growth genes. Network modeling demonstrated that the extracellular matrix and cell proliferation genes influenced by HHIP tended to be interconnected. Thus, we identified potential HHIP targets in human bronchial epithelial cells that may contribute to COPD pathogenesis. PMID:23459001

Antioxidant Defense Enzyme Genes and Asthma Susceptibility: Gender-Specific Effects and Heterogeneity in Gene-Gene Interactions between Pathogenetic Variants of the Disease

PubMed Central

Polonikov, Alexey V.; Ivanov, Vladimir P.; Bogomazov, Alexey D.; Freidin, Maxim B.; Illig, Thomas; Solodilova, Maria A.

2014-01-01

Oxidative stress resulting from an increased amount of reactive oxygen species and an imbalance between oxidants and antioxidants plays an important role in the pathogenesis of asthma. The present study tested the hypothesis that genetic susceptibility to allergic and nonallergic variants of asthma is determined by complex interactions between genes encoding antioxidant defense enzymes (ADE). We carried out a comprehensive analysis of the associations between adult asthma and 46 single nucleotide polymorphisms of 34 ADE genes and 12 other candidate genes of asthma in Russian population using set association analysis and multifactor dimensionality reduction approaches. We found for the first time epistatic interactions between ADE genes underlying asthma susceptibility and the genetic heterogeneity between allergic and nonallergic variants of the disease. We identified GSR (glutathione reductase) and PON2 (paraoxonase 2) as novel candidate genes for asthma susceptibility. We observed gender-specific effects of ADE genes on the risk of asthma. The results of the study demonstrate complexity and diversity of interactions between genes involved in oxidative stress underlying susceptibility to allergic and nonallergic asthma. PMID:24895604

Allowing for population stratification in case-only studies of gene-environment interaction, using genomic control.

PubMed

Yadav, Pankaj; Freitag-Wolf, Sandra; Lieb, Wolfgang; Dempfle, Astrid; Krawczak, Michael

2015-10-01

Gene-environment interactions (G × E) have attracted considerable research interest in the past owing to their scientific and public health implications, but powerful statistical methods are required to successfully track down G × E, particularly at a genome-wide level. Previously, a case-only (CO) design has been proposed as a means to identify G × E with greater efficiency than traditional case-control or cohort studies. However, as with genotype-phenotype association studies themselves, hidden population stratification (PS) can impact the validity of G × E studies using a CO design. Since this problem has been subject to little research to date, we used comprehensive simulation to systematically assess the type I error rate, power and effect size bias of CO studies of G × E in the presence of PS. Three types of PS were considered, namely genetic-only (PSG), environment-only (PSE), and joint genetic and environmental stratification (PSGE). Our results reveal that the type I error rate of an unadjusted Wald test, appropriate for the CO design, would be close to its nominal level (0.05 in our study) as long as PS involves only one interaction partner (i.e., either PSG or PSE). In contrast, if the study population is stratified with respect to both G and E (i.e., if there is PSGE), then the type I error rate is seriously inflated and estimates of the underlying G × E interaction are biased. Comparison of CO to a family-based case-parents design confirmed that the latter is more robust against PSGE, as expected. However, case-parent trios may be particularly unsuitable for G × E studies in view of the fact that they require genotype data from parents and that many diseases with an environmental component are likely to be of late onset. An alternative approach to adjusting for PS is principal component analysis (PCA), which has been widely used for this very purpose in past genome-wide association studies (GWAS). However, resolving genetic PS properly by PCA

On meta- and mega-analyses for gene–environment interactions

PubMed Central

Huang, Jing; Liu, Yulun; Vitale, Steve; Penning, Trevor M.; Whitehead, Alexander S.; Blair, Ian A.; Vachani, Anil; Clapper, Margie L.; Muscat, Joshua E.; Lazarus, Philip; Scheet, Paul; Moore, Jason H.; Chen, Yong

2017-01-01

Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a “mega” data set, which can be fit by a joint “mega-analysis.” Alternatively, analyses can be done at each site, and results across sites can be combined through a “meta-analysis” procedure without integrating individual level data across sites. Although mega-analysis has been advocated in several recent initiatives, meta-analysis has the advantages of simplicity and feasibility, and has recently led to several important findings in identifying main genetic effects. In this paper, we conducted empirical and simulation studies, using data from a G × E study of lung cancer, to compare the mega- and meta-analyses in four commonly used G × E analyses under the scenario that the number of studies is small and sample sizes of individual studies are relatively large. We compared the two data integration approaches in the context of fixed effect models and random effects models separately. Our investigations provide valuable insights in understanding the differences between mega- and meta-analyses in practice of combining small number of studies in identifying G × E interactions. PMID:29110346

Allelic-based gene-gene interaction associated with quantitative traits.

PubMed

Jung, Jeesun; Sun, Bin; Kwon, Deukwoo; Koller, Daniel L; Foroud, Tatiana M

2009-05-01

Recent studies have shown that quantitative phenotypes may be influenced not only by multiple single nucleotide polymorphisms (SNPs) within a gene but also by the interaction between SNPs at unlinked genes. We propose a new statistical approach that can detect gene-gene interactions at the allelic level which contribute to the phenotypic variation in a quantitative trait. By testing for the association of allelic combinations at multiple unlinked loci with a quantitative trait, we can detect the SNP allelic interaction whether or not it can be detected as a main effect. Our proposed method assigns a score to unrelated subjects according to their allelic combination inferred from observed genotypes at two or more unlinked SNPs, and then tests for the association of the allelic score with a quantitative trait. To investigate the statistical properties of the proposed method, we performed a simulation study to estimate type I error rates and power and demonstrated that this allelic approach achieves greater power than the more commonly used genotypic approach to test for gene-gene interaction. As an example, the proposed method was applied to data obtained as part of a candidate gene study of sodium retention by the kidney. We found that this method detects an interaction between the calcium-sensing receptor gene (CaSR), the chloride channel gene (CLCNKB) and the Na, K, 2Cl cotransporter gene (CLC12A1) that contributes to variation in diastolic blood pressure.

Selective breeding for susceptibility to myopia reveals a gene-environment interaction.

PubMed

Chen, Yen-Po; Hocking, Paul M; Wang, Ling; Povazay, Boris; Prashar, Ankush; To, Chi-Ho; Erichsen, Jonathan T; Feldkaemper, Marita; Hofer, Bernd; Drexler, Wolfgang; Schaeffel, Frank; Guggenheim, Jeremy A

2011-06-08

Purpose. To test whether the interanimal variability in susceptibility to visually induced myopia is genetically determined. Methods. Monocular deprivation of sharp vision (DSV) was induced in outbred White Leghorn chicks aged 4 days. After 4 days' DSV, myopia susceptibility was quantified by the relative changes in axial length and refraction. Chicks in the extreme tails of the distribution of susceptibility to DSV were kept and paired for breeding (high- and low-susceptibility lines). A second round of selection was then performed. The third generation of chicks, derived from the selected parents, was assessed after either monocular DSV (4 or 10 days) or lens wear. Results. After two rounds of selective breeding, the chicks from the high-susceptibility line developed approximately twice as much myopia in response to 4 days' DSV as did those from the low-susceptibility line (P < 0.001). All ocular component dimensions differed significantly (P < 0.001) between the two selected lines, both before treatment and in the responses of the treated eye. When DSV was conducted for 10 days, the relative changes in axial length and refractive error were still significantly different between the high and low lines (P < 0.001). The chicks bred for high or low susceptibility to DSV also showed significantly different responses to minus lens wear, but not to plus lens wear. Additive genetic effects explained ∼50% of the interanimal variability in response to DSV. Conclusions. Genes and environment interact to shape refractive development in chicks.

Childhood problem behavior and parental divorce: evidence for gene-environment interaction.

PubMed

Robbers, Sylvana; van Oort, Floor; Huizink, Anja; Verhulst, Frank; van Beijsterveldt, Catharina; Boomsma, Dorret; Bartels, Meike

2012-10-01

The importance of genetic and environmental influences on children's behavioral and emotional problems may vary as a function of environmental exposure. We previously reported that 12-year-olds with divorced parents showed more internalizing and externalizing problems than children with married parents, and that externalizing problems in girls precede and predict later parental divorce. The aim of the current study was to investigate as to whether genetic and environmental influences on internalizing and externalizing problems were different for children from divorced versus non-divorced families. Maternal ratings on internalizing and externalizing problems were collected with the Child Behavior Checklist in 4,592 twin pairs at ages 3 and 12 years, of whom 367 pairs had experienced a parental divorce between these ages. Variance in internalizing and externalizing problems at ages 3 and 12 was analyzed with biometric models in which additive genetic and environmental effects were allowed to depend on parental divorce and sex. A difference in the contribution of genetic and environmental influences between divorced and non-divorced groups would constitute evidence for gene-environment interaction. For both pre- and post-divorce internalizing and externalizing problems, the total variances were larger for children from divorced families, which was mainly due to higher environmental variances. As a consequence, heritabilities were lower for children from divorced families, and the relative contributions of environmental influences were higher. Environmental influences become more important in explaining variation in children's problem behaviors in the context of parental divorce.

Single gene and gene interaction effects on fertilization and embryonic survival rates in cattle.

PubMed

Khatib, H; Huang, W; Wang, X; Tran, A H; Bindrim, A B; Schutzkus, V; Monson, R L; Yandell, B S

2009-05-01

Decrease in fertility and conception rates is a major cause of economic loss and cow culling in dairy herds. Conception rate is the product of fertilization rate and embryonic survival rate. Identification of genetic factors that cause the death of embryos is the first step in eliminating this problem from the population and thereby increasing reproductive efficiency. A candidate pathway approach was used to identify candidate genes affecting fertilization and embryo survival rates using an in vitro fertilization experimental system. A total of 7,413 in vitro fertilizations were performed using oocytes from 504 ovaries and semen samples from 10 different bulls. Fertilization rate was calculated as the number of cleaved embryos 48 h postfertilization out of the total number of oocytes exposed to sperm. Survival rate of embryos was calculated as the number of blastocysts on d 7 of development out of the number of total embryos cultured. All ovaries were genotyped for 8 genes in the POU1F1 signaling pathway. Single-gene analysis revealed significant associations of GHR, PRLR, STAT5A, and UTMP with survival rate and of POU1F1, GHR, STAT5A, and OPN with fertilization rate. To further characterize the contribution of the entire integrated POU1F1 pathway to fertilization and early embryonic survival, a model selection procedure was applied. Comparisons among the different models showed that interactions between adjacent genes in the pathway revealed a significant contribution to the variation in fertility traits compared with other models that analyzed only bull information or only genes without interactions. Moreover, some genes that were not significant in the single-gene analysis showed significant effects in the interaction analysis. Thus, we propose that single genes as well as an entire pathway can be used in selection programs to improve reproduction performance in dairy cattle.

Genetic Expression Outside the Skin: Clues to Mechanisms of Genotype × Environment Interaction

PubMed Central

Reiss, David; Leve, Leslie D.

2007-01-01

The rapidly moving study of Gene × Environment interaction needs interim conceptual tools to track progress, integrate findings, and apply this knowledge to preventive intervention. We define two closely related concepts: the social mediation of the expression of genetic influences and the interaction between the entire genotype and the social environment (Genotype × Environment interaction; G×E). G×E interaction, the primary focus of this report, assesses individual differences in the full genotype using twin, sibling, and adoption designs and, for the most part, employs fine-grained analyses of relational processes in the social environment. In comparison, studies of Allele × Environment interaction (A×E) assess the influence on development of one or more measured polymorphisms as modified by environmental factors. G×E studies build on work showing how the social environment responds to genetic influences and how genetic influences shape the social environment. Recent G×E research has yielded new insight into variations in the sensitivity of the social environment to genotypic influences and provides clues to the specificity and timing of these environmental responses that can be leveraged to inform preventive interventions aimed at reducing genetic risk for problem behavior. PMID:17931431

Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis.

PubMed

Cullis, B R; Smith, A B; Beeck, C P; Cowling, W A

2010-11-01

Exploring and exploiting variety by environment (V × E) interaction is one of the major challenges facing plant breeders. In paper I of this series, we presented an approach to modelling V × E interaction in the analysis of complex multi-environment trials using factor analytic models. In this paper, we develop a range of statistical tools which explore V × E interaction in this context. These tools include graphical displays such as heat-maps of genetic correlation matrices as well as so-called E-scaled uniplots that are a more informative alternative to the classical biplot for large plant breeding multi-environment trials. We also present a new approach to prediction for multi-environment trials that include pedigree information. This approach allows meaningful selection indices to be formed either for potential new varieties or potential parents.

Caregiving and 5-HTTLPR Genotype Predict Adolescent Physiological Stress Reactivity: Confirmatory Tests of Gene × Environment Interactions.

PubMed

Sumner, Jennifer A; McLaughlin, Katie A; Walsh, Kate; Sheridan, Margaret A; Koenen, Karestan C

2015-03-03

A theory-driven confirmatory approach comparing diathesis-stress and differential susceptibility models of Gene × Environment (G × E) interactions was applied to examine whether 5-HTTLPR genotype moderated the effect of early maternal caregiving on autonomic nervous system (ANS) stress reactivity in 113 adolescents aged 13-17 years. Findings supported a differential susceptibility, rather than diathesis-stress, framework. Carriers of one or more 5-HTTLPR short alleles (SS/SL carriers) reporting higher quality caregiving exhibited approach ANS responses to a speech task, whereas those reporting lower quality caregiving exhibited withdrawal ANS responses. Carriers of two 5-HTTLPR long alleles (LL carriers) were unaffected by caregiving. Findings suggest that 5-HTTLPR genotype and early caregiving in interaction are associated with ANS stress reactivity in adolescents in a "for better and for worse" fashion, and they demonstrate the promise of confirmatory methods for testing G × E interactions. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

A Critical Look at Entropy-Based Gene-Gene Interaction Measures.

PubMed

Lee, Woojoo; Sjölander, Arvid; Pawitan, Yudi

2016-07-01

Several entropy-based measures for detecting gene-gene interaction have been proposed recently. It has been argued that the entropy-based measures are preferred because entropy can better capture the nonlinear relationships between genotypes and traits, so they can be useful to detect gene-gene interactions for complex diseases. These suggested measures look reasonable at intuitive level, but so far there has been no detailed characterization of the interactions captured by them. Here we study analytically the properties of some entropy-based measures for detecting gene-gene interactions in detail. The relationship between interactions captured by the entropy-based measures and those of logistic regression models is clarified. In general we find that the entropy-based measures can suffer from a lack of specificity in terms of target parameters, i.e., they can detect uninteresting signals as interactions. Numerical studies are carried out to confirm theoretical findings. © 2016 WILEY PERIODICALS, INC.

Genome-wide association studies suggest that APOL1-environment interactions more likely trigger kidney disease in African Americans with nondiabetic nephropathy than strong APOL1-second gene interactions.

PubMed

Langefeld, Carl D; Comeau, Mary E; Ng, Maggie C Y; Guan, Meijian; Dimitrov, Latchezar; Mudgal, Poorva; Spainhour, Mitzie H; Julian, Bruce A; Edberg, Jeffrey C; Croker, Jennifer A; Divers, Jasmin; Hicks, Pamela J; Bowden, Donald W; Chan, Gary C; Ma, Lijun; Palmer, Nicholette D; Kimberly, Robert P; Freedman, Barry I

2018-06-06

African Americans carrying two apolipoprotein L1 gene (APOL1) renal risk variants have a high risk for nephropathy. However, only a minority develops end-stage renal disease (ESRD). Hence, modifying factors likely contribute to initiation of kidney disease such as endogenous (HIV infection) or exogenous (interferon treatment) environmental modifiers. In this report, genome-wide association studies and a meta-analysis were performed to identify novel loci for nondiabetic ESRD in African Americans and to detect genetic modifiers in APOL1-associated nephropathy. Two African American cohorts were analyzed, 1749 nondiabetic ESRD cases and 1136 controls from Wake Forest and 901 lupus nephritis (LN)-ESRD cases and 520 controls with systemic lupus erythematosus but lacking nephropathy from the LN-ESRD Consortium. Association analyses adjusting for APOL1 G1/G2 renal-risk variants were completed and stratified by APOL1 risk genotype status. Individual genome-wide association studies and meta-analysis results of all 2650 ESRD cases and 1656 controls did not detect significant genome-wide associations with ESRD beyond APOL1. Similarly, no single nucleotide polymorphism showed significant genome-wide evidence of an interaction with APOL1 risk variants. Thus, although variants with small individual effects cannot be ruled out and are likely to exist, our results suggest that APOL1-environment interactions may be of greater clinical importance in triggering nephropathy in African Americans than APOL1 interactions with other single nucleotide polymorphisms. Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Gene-environment interaction in problematic substance use: interaction between DRD4 and insecure attachments.

PubMed

Olsson, Craig A; Moyzis, Robert K; Williamson, Elizabeth; Ellis, Justine A; Parkinson-Bates, Mandy; Patton, George C; Dwyer, Terry; Romaniuk, Helena; Moore, Elya E

2013-07-01

To investigate the combined effect of an exon III variable number tandem repeat in the dopamine receptor gene (DRD4) and insecure attachment style on risk for tobacco, cannabis and alcohol use problems in young adulthood. It was hypothesized that (1) individuals with 5, 6, 7 or 8 repeats (labelled 7R+) would be at increased risk for problematic drug use, and (2) risk for drug use would be further increased in individuals with 7R+ repeats who also have a history of insecure parent-child attachment relations. Data were drawn from the Victorian Adolescent Health Cohort Study, an eight-wave longitudinal study of adolescent and young adult development. DRD4 genotypes were available for 839 participants. Risk attributable to the combined effects of 7R+ genotype and insecure attachments was evaluated within a sufficient causes framework under the assumptions of additive interaction using a two-by-four table format with a common reference group. 7R+ alleles were associated with higher tobacco, cannabis and alcohol use (binging). Insecure attachments were associated with higher tobacco and cannabis use but lower alcohol use. For tobacco, there was evidence of interaction for anxious but not avoidant attachments. For cannabis, there was evidence of interaction for both anxious and avoidant attachments, although the interaction for anxious attachments was more substantial. There is no evidence of interaction for binge drinking. Results are consistent with a generic reward deficit hypothesis of drug addiction for which the 7R+ disposition may play a role. Interaction between 7R+ alleles and attachment insecurity may intensify risk for problematic tobacco and cannabis use. © 2011 Murdoch Childrens Research Institute.

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

PubMed

Vadigepalli, Rajanikanth; Chakravarthula, Praveen; Zak, Daniel E; Schwaber, James S; Gonye, Gregory E

2003-01-01

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Differential sensitivity to the environment: contribution of cognitive biases and genes to psychological wellbeing.

PubMed

Fox, E; Beevers, C G

2016-12-01

Negative cognitive biases and genetic variation have been associated with risk of psychopathology in largely independent lines of research. Here, we discuss ways in which these dynamic fields of research might be fruitfully combined. We propose that gene by environment (G × E) interactions may be mediated by selective cognitive biases and that certain forms of genetic 'reactivity' or 'sensitivity' may represent heightened sensitivity to the learning environment in a 'for better and for worse' manner. To progress knowledge in this field, we recommend including assessments of cognitive processing biases; examining G × E interactions in 'both' negative and positive environments; experimentally manipulating the environment when possible; and moving beyond single-gene effects to assess polygenic sensitivity scores. We formulate a new methodological framework encapsulating cognitive and genetic factors in the development of both psychopathology and optimal wellbeing that holds long-term promise for the development of new personalized therapies.

MINER: exploratory analysis of gene interaction networks by machine learning from expression data.

PubMed

Kadupitige, Sidath Randeni; Leung, Kin Chun; Sellmeier, Julia; Sivieng, Jane; Catchpoole, Daniel R; Bain, Michael E; Gaëta, Bruno A

2009-12-03

The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

Gene-Environment Interplay between Parent-Child Relationship Problems and Externalizing Disorders in Adolescence and Young Adulthood

PubMed Central

Samek, Diana R.; Hicks, Brian M.; Keyes, Margaret A.; Bailey, Jennifer; McGue, Matt; Iacono, William G.

2014-01-01

Background Previous studies have shown that genetic risk for externalizing (EXT) disorders is greater in the context of adverse family environments during adolescence, but it is unclear whether these effects are long-lasting. The current study evaluated developmental changes in gene-environment interplay in the concurrent and prospective associations between parent-child relationship problems and EXT at ages 18 and 25. Method The sample included 1,382 twin pairs (48% male) from the Minnesota Twin Family Study, participating in assessments at ages 18 (M = 17.8 years, SD = 0.69) and 25 (M = 25.0 years, SD = 0.90). Perceptions of parent-child relationship problems were assessed using questionnaires. Structured interviews were used to assess symptoms of adult antisocial behavior and nicotine, alcohol, and illicit drug dependence. Results We detected a gene-environment interaction at age 18, such that the genetic influence on EXT was greater in the context of more parent-child relationship problems. This moderation effect was not present at age 25, nor did parent-relationship problems at age 18 moderate genetic influence on EXT at age 25. Rather, common genetic influences accounted for this longitudinal association. Conclusions Gene-environment interaction evident in the relationship between adolescent parent-child relationship problems and EXT is both proximal and developmentally limited. Common genetic influence, rather than a gene-environment interaction, accounts for the long-term association between parent-child relationship problems at age 18 and EXT at age 25. These results are consistent with a relatively pervasive importance of gene-environmental correlation in the transition from late adolescence to young adulthood. PMID:25066478

Direct protein interaction underlies gene-for-gene specificity and coevolution of the flax resistance genes and flax rust avirulence genes

PubMed Central

Dodds, Peter N.; Lawrence, Gregory J.; Catanzariti, Ann-Maree; Teh, Trazel; Wang, Ching-I. A.; Ayliffe, Michael A.; Kobe, Bostjan; Ellis, Jeffrey G.

2006-01-01

Plant resistance proteins (R proteins) recognize corresponding pathogen avirulence (Avr) proteins either indirectly through detection of changes in their host protein targets or through direct R–Avr protein interaction. Although indirect recognition imposes selection against Avr effector function, pathogen effector molecules recognized through direct interaction may overcome resistance through sequence diversification rather than loss of function. Here we show that the flax rust fungus AvrL567 genes, whose products are recognized by the L5, L6, and L7 R proteins of flax, are highly diverse, with 12 sequence variants identified from six rust strains. Seven AvrL567 variants derived from Avr alleles induce necrotic responses when expressed in flax plants containing corresponding resistance genes (R genes), whereas five variants from avr alleles do not. Differences in recognition specificity between AvrL567 variants and evidence for diversifying selection acting on these genes suggest they have been involved in a gene-specific arms race with the corresponding flax R genes. Yeast two-hybrid assays indicate that recognition is based on direct R–Avr protein interaction and recapitulate the interaction specificity observed in planta. Biochemical analysis of Escherichia coli-produced AvrL567 proteins shows that variants that escape recognition nevertheless maintain a conserved structure and stability, suggesting that the amino acid sequence differences directly affect the R–Avr protein interaction. We suggest that direct recognition associated with high genetic diversity at corresponding R and Avr gene loci represents an alternative outcome of plant–pathogen coevolution to indirect recognition associated with simple balanced polymorphisms for functional and nonfunctional R and Avr genes. PMID:16731621

Assessing the utility of gene co-expression stability in combination with correlation in the analysis of protein-protein interaction networks

PubMed Central

2011-01-01

Background Gene co-expression, in the form of a correlation coefficient, has been valuable in the analysis, classification and prediction of protein-protein interactions. However, it is susceptible to bias from a few samples having a large effect on the correlation coefficient. Gene co-expression stability is a means of quantifying this bias, with high stability indicating robust, unbiased co-expression correlation coefficients. We assess the utility of gene co-expression stability as an additional measure to support the co-expression correlation in the analysis of protein-protein interaction networks. Results We studied the patterns of co-expression correlation and stability in interacting proteins with respect to their interaction promiscuity, levels of intrinsic disorder, and essentiality or disease-relatedness. Co-expression stability, along with co-expression correlation, acts as a better classifier of hub proteins in interaction networks, than co-expression correlation alone, enabling the identification of a class of hubs that are functionally distinct from the widely accepted transient (date) and obligate (party) hubs. Proteins with high levels of intrinsic disorder have low co-expression correlation and high stability with their interaction partners suggesting their involvement in transient interactions, except for a small group that have high co-expression correlation and are typically subunits of stable complexes. Similar behavior was seen for disease-related and essential genes. Interacting proteins that are both disordered have higher co-expression stability than ordered protein pairs. Using co-expression correlation and stability, we found that transient interactions are more likely to occur between an ordered and a disordered protein while obligate interactions primarily occur between proteins that are either both ordered, or disordered. Conclusions We observe that co-expression stability shows distinct patterns in structurally and functionally

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

PubMed

Chen, Rui; Zhang, Zhiliang; Xue, Zhujia; Wang, Lin; Fu, Mingang; Lu, Yi; Bai, Ling; Zhang, Ping; Fan, Zhihong

2015-01-01

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy. © 2015 The International Society of Dermatology.

cDNA microarray reveals the alterations of cytoskeleton-related genes in osteoblast under high magneto-gravitational environment.

PubMed

Qian, Airong; Di, Shengmeng; Gao, Xiang; Zhang, Wei; Tian, Zongcheng; Li, Jingbao; Hu, Lifang; Yang, Pengfei; Yin, Dachuan; Shang, Peng

2009-07-01

The diamagnetic levitation as a novel ground-based model for simulating a reduced gravity environment has been widely applied in many fields. In this study, a special designed superconducting magnet, which can produce three apparent gravity levels (0, 1, and 2 g), namely high magneto-gravitational environment (HMGE), was used to simulate space gravity environment. The effects of HMGE on osteoblast gene expression profile were investigated by microarray. Genes sensitive to diamagnetic levitation environment (0 g), gravity changes, and high magnetic field changes were sorted on the basis of typical cell functions. Cytoskeleton, as an intracellular load-bearing structure, plays an important role in gravity perception. Therefore, 13 cytoskeleton-related genes were chosen according to the results of microarray analysis, and the expressions of these genes were found to be altered under HMGE by real-time PCR. Based on the PCR results, the expressions of WASF2 (WAS protein family, member 2), WIPF1 (WAS/WASL interacting protein family, member 1), paxillin, and talin 1 were further identified by western blot assay. Results indicated that WASF2 and WIPF1 were more sensitive to altered gravity levels, and talin 1 and paxillin were sensitive to both magnetic field and gravity changes. Our findings demonstrated that HMGE can affect osteoblast gene expression profile and cytoskeleton-related genes expression. The identification of mechanosensitive genes may enhance our understandings to the mechanism of bone loss induced by microgravity and may provide some potential targets for preventing and treating bone loss or osteoporosis.

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

PubMed

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

Endothelial pro-atherosclerotic response to extracellular diabetic-like environment: possible role of thioredoxin-interacting protein.

PubMed

Zitman-Gal, Tali; Green, Janice; Pasmanik-Chor, Metsada; Oron-Karni, Varda; Bernheim, Jacques

2010-07-01

BACKGROUND. High blood and tissue concentrations of glucose and advanced glycation end-products (AGEs) are thought to play an important role in the development of vascular diabetic complications. Therefore, the impact of extracellular AGEs and different glucose concentrations was evaluated by studying the gene expressions and the underlying cellular pathways involved in the development of inflammatory pro-atherosclerotic processes observed in cultured endothelial cells. METHODS. Fresh human umbilical vein cord endothelial cells (HUVEC) were treated in the presence of elevated extracellular glucose concentrations (5.5-28 mmol/l) with and without AGE-human serum albumin (HSA). Affymetrix GeneChip(R) Human Gene 1.0 ST arrays were used for gene expression analysis (total 20 chips). Genes of interest were further validated using real-time PCR and western blot techniques. RESULTS. Microarray analysis revealed significant changes in some gene expressions in the presence of the different stimuli, suggesting that different pathways are involved. Six genes were selected for validation as follows: thioredoxin-interacting protein (TXNIP), thioredoxin (TXN), nuclear factor of kappa B (NF-kappaB), interleukin 6 (IL6), interleukin 8 (IL8) and receptor of advanced glycation end-products (RAGE). Interestingly, it was found that the association of AGEs together with the highest pathophysiological concentration of glucose (28 mmol/l) diminished the expression of these specific genes, excluding TXN. CONCLUSIONS. In the present model that mimics a diabetic environment, the relatively short-term experimental conditions used showed an unexpected blunting action of AGEs in the presence of the highest glucose concentration (28 mmol/l). The interactive cellular pathways involved in these processes should be further investigated.

Gene-gene-environment interactions between drugs, transporters, receptors, and metabolizing enzymes: Statins, SLCO1B1, and CYP3A4 as an example.

PubMed

Sadee, Wolfgang

2013-09-01

Pharmacogenetic biomarker tests include mostly specific single gene-drug pairs, capable of accounting for a portion of interindividual variability in drug response and toxicity. However, multiple genes are likely to contribute, either acting independently or epistatically, with the CYP2C9-VKORC1-warfarin test panel, an example of a clinically used gene-gene-dug interaction. I discuss here further instances of gene-gene-drug interactions, including a proposed dynamic effect on statin therapy by genetic variants in both a transporter (SLCO1B1) and a metabolizing enzyme (CYP3A4) in liver cells, the main target site where statins block cholesterol synthesis. These examples set a conceptual framework for developing diagnostic panels involving multiple gene-drug combinations. Copyright © 2013 Wiley Periodicals, Inc.

The interaction between cannabis use and the Val158Met polymorphism of the COMT gene in psychosis: A transdiagnostic meta - analysis.

PubMed

Vaessen, Thomas Stephanus Johannes; de Jong, Lea; Schäfer, Annika Theresia; Damen, Thomas; Uittenboogaard, Aniek; Krolinski, Pauline; Nwosu, Chinyere Vicky; Pinckaers, Florentina Maria Egidius; Rotee, Iris Leah Marije; Smeets, Antonius Petrus Wilhelmus; Ermiş, Ayşegül; Kennedy, James L; Nieman, Dorien H; Tiwari, Arun; van Os, Jim; Drukker, Marjan

2018-01-01

Neither environmental nor genetic factors are sufficient to predict the transdiagnostic expression of psychosis. Therefore, analysis of gene-environment interactions may be productive. A meta-analysis was performed using papers investigating the interaction between cannabis use and catechol-O-methyl transferase (COMT) polymorphism Val158Met (COMTVal158Met). PubMed, Embase, PsychInfo. All observational studies assessing the interaction between COMTVal158Met and cannabis with any psychosis or psychotic symptoms measure as an outcome. A meta-analysis was performed using the Meta-analysis of Observational Studies in Epidemiology guidelines and forest plots were generated. Thirteen articles met the selection criteria: 7 clinical studies using a case-only design, 3 clinical studies with a dichotomous outcome, and 3 studies analysing a continuous outcome of psychotic symptoms below the threshold of psychotic disorder. The three study types were analysed separately. Validity of the included studies was assessed using "A Cochrane Risk of Bias Assessment Tool: for Non-Randomized Studies of Interventions". For case-only studies, a significant interaction was found between cannabis use and COMTVal158Met, with an OR of 1.45 (95% Confidence Interval = 1.05-2.00; Met/Met as the risk genotype). However, there was no evidence for interaction in either the studies including dichotomous outcomes (B = -0.51, 95% Confidence Interval -1.72, 0.70) or the studies including continuous outcomes (B = -0.04 95% Confidence Interval -0.16-0.08). A substantial part of the included studies used the case-only design, which has lower validity and tends to overestimate true effects. The interaction term between cannabis use and COMTVal158Met was only statistically significant in the case-only studies, but not in studies using other clinical or non-clinical psychosis outcomes. Future additional high quality studies might change current perspectives, yet currently evidence for the interaction remains

Differential sensitivity to the environment: contribution of cognitive biases and genes to psychological wellbeing

PubMed Central

Fox, E; Beevers, C G

2016-01-01

Negative cognitive biases and genetic variation have been associated with risk of psychopathology in largely independent lines of research. Here, we discuss ways in which these dynamic fields of research might be fruitfully combined. We propose that gene by environment (G × E) interactions may be mediated by selective cognitive biases and that certain forms of genetic ‘reactivity' or ‘sensitivity' may represent heightened sensitivity to the learning environment in a ‘for better and for worse' manner. To progress knowledge in this field, we recommend including assessments of cognitive processing biases; examining G × E interactions in ‘both' negative and positive environments; experimentally manipulating the environment when possible; and moving beyond single-gene effects to assess polygenic sensitivity scores. We formulate a new methodological framework encapsulating cognitive and genetic factors in the development of both psychopathology and optimal wellbeing that holds long-term promise for the development of new personalized therapies. PMID:27431291

Simple F Test Reveals Gene-Gene Interactions in Case-Control Studies

PubMed Central

Chen, Guanjie; Yuan, Ao; Zhou, Jie; Bentley, Amy R.; Adeyemo, Adebowale; Rotimi, Charles N.

2012-01-01

Missing heritability is still a challenge for Genome Wide Association Studies (GWAS). Gene-gene interactions may partially explain this residual genetic influence and contribute broadly to complex disease. To analyze the gene-gene interactions in case-control studies of complex disease, we propose a simple, non-parametric method that utilizes the F-statistic. This approach consists of three steps. First, we examine the joint distribution of a pair of SNPs in cases and controls separately. Second, an F-test is used to evaluate the ratio of dependence in cases to that of controls. Finally, results are adjusted for multiple tests. This method was used to evaluate gene-gene interactions that are associated with risk of Type 2 Diabetes among African Americans in the Howard University Family Study. We identified 18 gene-gene interactions (P < 0.0001). Compared with the commonly-used logistical regression method, we demonstrate that the F-ratio test is an efficient approach to measuring gene-gene interactions, especially for studies with limited sample size. PMID:22837643

Interactive Environment Design in Smart City

NASA Astrophysics Data System (ADS)

Deng, DeXiang; Chen, LanSha; Zhou, Xi

2017-08-01

The interactive environment design of smart city is not just an interactive progress or interactive mode design, rather than generate an environment such as the “organic” life entity as human beings through interactive design, forming a smart environment with perception, memory, thinking, and reaction.

Clock genes × stress × reward interactions in alcohol and substance use disorders.

PubMed

Perreau-Lenz, Stéphanie; Spanagel, Rainer

2015-06-01

Adverse life events and highly stressful environments have deleterious consequences for mental health. Those environmental factors can potentiate alcohol and drug abuse in vulnerable individuals carrying specific genetic risk factors, hence producing the final risk for alcohol- and substance-use disorders development. The nature of these genes remains to be fully determined, but studies indicate their direct or indirect relation to the stress hypothalamo-pituitary-adrenal (HPA) axis and/or reward systems. Over the past decade, clock genes have been revealed to be key-players in influencing acute and chronic alcohol/drug effects. In parallel, the influence of chronic stress and stressful life events in promoting alcohol and substance use and abuse has been demonstrated. Furthermore, the reciprocal interaction of clock genes with various HPA-axis components, as well as the evidence for an implication of clock genes in stress-induced alcohol abuse, have led to the idea that clock genes, and Period genes in particular, may represent key genetic factors to consider when examining gene × environment interaction in the etiology of addiction. The aim of the present review is to summarize findings linking clock genes, stress, and alcohol and substance abuse, and to propose potential underlying neurobiological mechanisms. Copyright © 2015 Elsevier Inc. All rights reserved.

A vitamin D pathway gene-gene interaction affects low-density lipoprotein cholesterol levels.

PubMed

Grave, Nathália; Tovo-Rodrigues, Luciana; da Silveira, Janaína; Rovaris, Diego Luiz; Dal Bosco, Simone Morelo; Contini, Verônica; Genro, Júlia Pasqualini

2016-12-01

Much evidence suggests an association between vitamin D deficiency and chronic diseases such as obesity and dyslipidemia. Although genetic factors play an important role in the etiology of these diseases, only a few studies have investigated the relationship between vitamin D-related genes and anthropometric and lipid profiles. The aim of this study was to investigate the association of three vitamin D-related genes with anthropometric and lipid parameters in 542 adult individuals. We analyzed the rs2228570 polymorphism in the vitamin D receptor gene (VDR), rs2134095 in the retinoid X receptor gamma gene (RXRG) and rs7041 in the vitamin D-binding protein gene (GC). Polymorphisms were genotyped by TaqMan allelic discrimination. Gene-gene interactions were evaluated by the general linear model. The functionality of the polymorphisms was investigated using the following predictors and databases: SIFT (Sorting Intolerant from Tolerant), PolyPhen-2 (Polymorphism Phenotyping v2) and Human Splicing Finder 3. We identified a significant effect of the interaction between RXRG (rs2134095) and GC (rs7041) on low-density lipoprotein cholesterol (LDL-c) levels (P=.005). Furthermore, our in silico analysis suggested a functional role for both variants in the regulation of the gene products. Our results suggest that the vitamin D-related genes RXRG and GC affect LDL-c levels. These findings are in agreement with other studies that consistently associate vitamin D and lipid profile. Together, our results corroborate the idea that analyzing gene-gene interaction would be helpful to clarify the genetic component of lipid profile. Copyright © 2016 Elsevier Inc. All rights reserved.

Inverse gene-for-gene interactions contribute additively to tan spot susceptibility in wheat.

PubMed

Liu, Zhaohui; Zurn, Jason D; Kariyawasam, Gayan; Faris, Justin D; Shi, Gongjun; Hansen, Jana; Rasmussen, Jack B; Acevedo, Maricelis

2017-06-01

Tan spot susceptibility is conferred by multiple interactions of necrotrophic effector and host sensitivity genes. Tan spot of wheat, caused by Pyrenophora tritici-repentis, is an important disease in almost all wheat-growing areas of the world. The disease system is known to involve at least three fungal-produced necrotrophic effectors (NEs) that interact with the corresponding host sensitivity (S) genes in an inverse gene-for-gene manner to induce disease. However, it is unknown if the effects of these NE-S gene interactions contribute additively to the development of tan spot. In this work, we conducted disease evaluations using different races and quantitative trait loci (QTL) analysis in a wheat recombinant inbred line (RIL) population derived from a cross between two susceptible genotypes, LMPG-6 and PI 626573. The two parental lines each harbored a single known NE sensitivity gene with LMPG-6 having the Ptr ToxC sensitivity gene Tsc1 and PI 626573 having the Ptr ToxA sensitivity gene Tsn1. Transgressive segregation was observed in the population for all races. QTL mapping revealed that both loci (Tsn1 and Tsc1) were significantly associated with susceptibility to race 1 isolates, which produce both Ptr ToxA and Ptr ToxC, and the two genes contributed additively to tan spot susceptibility. For isolates of races 2 and 3, which produce only Ptr ToxA and Ptr ToxC, only Tsn1 and Tsc1 were associated with tan spot susceptibility, respectively. This work clearly demonstrates that tan spot susceptibility in this population is due primarily to two NE-S interactions. Breeders should remove both sensitivity genes from wheat lines to obtain high levels of tan spot resistance.

Influence of 5-HTT variation, childhood trauma and self-efficacy on anxiety traits: a gene-environment-coping interaction study.

PubMed

Schiele, Miriam A; Ziegler, Christiane; Holitschke, Karoline; Schartner, Christoph; Schmidt, Brigitte; Weber, Heike; Reif, Andreas; Romanos, Marcel; Pauli, Paul; Zwanzger, Peter; Deckert, Jürgen; Domschke, Katharina

2016-08-01

Environmental vulnerability factors such as adverse childhood experiences in interaction with genetic risk variants, e.g., the serotonin transporter gene linked polymorphic region (5-HTTLPR), are assumed to play a role in the development of anxiety and affective disorders. However, positive influences such as general self-efficacy (GSE) may exert a compensatory effect on genetic disposition, environmental adversity, and anxiety traits. We, thus, assessed childhood trauma (Childhood Trauma Questionnaire, CTQ) and GSE in 678 adults genotyped for 5-HTTLPR/rs25531 and their interaction on agoraphobic cognitions (Agoraphobic Cognitions Questionnaire, ACQ), social anxiety (Liebowitz Social Anxiety Scale, LSAS), and trait anxiety (State-Trait Anxiety Inventory, STAI-T). The relationship between anxiety traits and childhood trauma was moderated by self-efficacy in 5-HTTLPR/rs25531 LALA genotype carriers: LALA probands maltreated as children showed high anxiety scores when self-efficacy was low, but low anxiety scores in the presence of high self-efficacy despite childhood maltreatment. Our results extend previous findings regarding anxiety-related traits showing an interactive relationship between 5-HTT genotype and adverse childhood experiences by suggesting coping-related measures to function as an additional dimension buffering the effects of a gene-environment risk constellation. Given that anxiety disorders manifest already early in childhood, this insight could contribute to the improvement of psychotherapeutic interventions by including measures strengthening self-efficacy and inform early targeted preventive interventions in at-risk populations, particularly within the crucial time window of childhood and adolescence.

Novel gene-by-environment interactions: APOB and NPC1L1 variants affect the relationship between dietary and total plasma cholesterol[S

PubMed Central

Kim, Daniel S.; Burt, Amber A.; Ranchalis, Jane E.; Jarvik, Ella R.; Rosenthal, Elisabeth A.; Hatsukami, Thomas S.; Furlong, Clement E.; Jarvik, Gail P.

2013-01-01

Cardiovascular disease (CVD) is the leading cause of death in developed countries. Plasma cholesterol level is a key risk factor in CVD pathogenesis. Genetic and dietary variation both influence plasma cholesterol; however, little is known about dietary interactions with genetic variants influencing the absorption and transport of dietary cholesterol. We sought to determine whether gut expressed variants predicting plasma cholesterol differentially affected the relationship between dietary and plasma cholesterol levels in 1,128 subjects (772/356 in the discovery/replication cohorts, respectively). Four single nucleotide polymorphisms (SNPs) within three genes (APOB, CETP, and NPC1L1) were significantly associated with plasma cholesterol in the discovery cohort. These were subsequently evaluated for gene-by-environment (GxE) interactions with dietary cholesterol for the prediction of plasma cholesterol, with significant findings tested for replication. Novel GxE interactions were identified and replicated for two variants: rs1042034, an APOB Ser4338Asn missense SNP and rs2072183 (in males only), a synonymous NPC1L1 SNP in linkage disequilibrium with SNPs 5′ of NPC1L1. This study identifies the presence of novel GxE and gender interactions implying that differential gut absorption is the basis for the variant associations with plasma cholesterol. These GxE interactions may account for part of the “missing heritability” not accounted for by genetic associations. PMID:23482652

Gene--Environment Interplay and Delinquent Involvement: Evidence of Direct, Indirect, and Interactive Effects

ERIC Educational Resources Information Center

Beaver, Kevin M.; DeLisi, Matt; Wright, John Paul; Vaughn, Michael G.

2009-01-01

Behavioral genetic research has revealed that biogenic factors play a role in the development of antisocial behaviors. Much of this research has also explicated the way in which the environment and genes may combine to create different phenotypes. The authors draw heavily from this literature and use data from the National Longitudinal Study of…

Nutrient-gene interactions in early pregnancy: a vascular hypothesis.

PubMed

Steegers-Theunissen, R P M; Steegers, E A P

2003-02-10

It is hypothesized that the following periconceptional and early pregnancy nutrient-gene interactions link vascular-related reproductive complications and cardiovascular diseases in adulthood: (1) Maternal and paternal genetically controlled nutrient status affects the quality of gametes and fertilization capacity; (2) The embryonic genetic constitution, derived from both parents, and the maternal genetically controlled nutrient environment determine embryogenesis and fetal growth; (3) Trophoblast invasion of decidua and spiral arteries is driven by genes derived from both parents as well as by maternal nutritional factors; (4) Angiogenesis, vasculogenesis and vascular function are dependent on the genetic constitution of the embryo, derived from both parents, and the maternal genetically controlled nutritional environment.Early intra-uterine programming of vessels may concern the same (in)dependent determinants of vascular-related complications during pregnancy and cardiovascular diseases in later life.

Conceptual shifts needed to understand the dynamic interactions of genes, environment, epigenetics, social processes, and behavioral choices.

PubMed

Jackson, Fatimah L C; Niculescu, Mihai D; Jackson, Robert T

2013-10-01

Social and behavioral research in public health is often intimately tied to profound, but frequently neglected, biological influences from underlying genetic, environmental, and epigenetic events. The dynamic interplay between the life, social, and behavioral sciences often remains underappreciated and underutilized in addressing complex diseases and disorders and in developing effective remediation strategies. Using a case-study format, we present examples as to how the inclusion of genetic, environmental, and epigenetic data can augment social and behavioral health research by expanding the parameters of such studies, adding specificity to phenotypic assessments, and providing additional internal control in comparative studies. We highlight the important roles of gene-environment interactions and epigenetics as sources of phenotypic change and as a bridge between the life and social and behavioral sciences in the development of robust interdisciplinary analyses.

A mechanistic explanation of popularity: genes, rule breaking, and evocative gene-environment correlations.

PubMed

Burt, Alexandra

2009-04-01

Previous work has suggested that the serotonergic system plays a key role in "popularity" or likeability. A polymorphism within the 5HT-sub(2A) serotonin receptor gene (-G1438A) has also been associated with popularity, suggesting that genes may predispose individuals to particular social experiences. However, because genes cannot code directly for others' reactions, any legitimate association should be mediated via the individual's behavior (i.e., genes-->behaviors-->social consequences), a phenomenon referred to as an evocative gene-environment correlation (rGE). The current study aimed to identify one such mediating behavior. The author focused on rule breaking given its prior links to both the serotonergic system and to increased popularity during adolescence. Two samples of previously unacquainted late-adolescent boys completed a peer-based interaction paradigm designed to assess their popularity. Analyses revealed that rule breaking partially mediated the genetic effect on popularity, thereby furthering our understanding of the biological mechanisms that underlie popularity. Moreover, the present results represent the first meaningfully explicated evidence that genes predispose individuals not only to particular behaviors but also to the social consequences of those behaviors. (c) 2009 APA, all rights reserved.

Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

PubMed Central

Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

2014-01-01

Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

The genetics of human longevity: an intricacy of genes, environment, culture and microbiome.

PubMed

Dato, Serena; Rose, Giuseppina; Crocco, Paolina; Monti, Daniela; Garagnani, Paolo; Franceschi, Claudio; Passarino, Giuseppe

2017-07-01

Approximately one-quarter of the variation in lifespan in developed countries can be attributed to genetic factors. However, even large population based studies investigating genetic influence on human lifespan have been disappointing, identifying only a few genes accounting for genetic susceptibility to longevity. Some environmental and lifestyle determinants associated with longevity have been identified, which interplay with genetic factors in an intricate way. The study of gene-environment and gene-gene interactions can significantly improve our chance to disentangle this complex scenario. In this review, we first describe the most recent approaches for genetic studies of longevity, from those enriched with health parameters and frailty measures to pathway-based and SNP-SNP interaction analyses. Then, we go deeper into the concept of "environmental influences" in human aging and longevity, focusing on the contribution of life style changes, social and cultural influences, as important determinants of survival differences among individuals in a population. Finally, we discuss the contribution of the microbiome in human longevity, as an example of complex interaction between organism and environment. In conclusion, evidences collected from the latest studies on human longevity provide a support for the collection of life-long genetic and environmental/lifestyle variables with beneficial or detrimental effects on health, to improve our understanding of the determinants of human lifespan. Copyright © 2017 Elsevier B.V. All rights reserved.

Social environment influences the relationship between genotype and gene expression in wild baboons

PubMed Central

Runcie, Daniel E.; Wiedmann, Ralph T.; Archie, Elizabeth A.; Altmann, Jeanne; Wray, Gregory A.; Alberts, Susan C.; Tung, Jenny

2013-01-01

Variation in the social environment can have profound effects on survival and reproduction in wild social mammals. However, we know little about the degree to which these effects are influenced by genetic differences among individuals, and conversely, the degree to which social environmental variation mediates genetic reaction norms. To better understand these relationships, we investigated the potential for dominance rank, social connectedness and group size to modify the effects of genetic variation on gene expression in the wild baboons of the Amboseli basin. We found evidence for a number of gene–environment interactions (GEIs) associated with variation in the social environment, encompassing social environments experienced in adulthood as well as persistent effects of early life social environment. Social connectedness, maternal dominance rank and group size all interacted with genotype to influence gene expression in at least one sex, and either in early life or in adulthood. These results suggest that social and behavioural variation, akin to other factors such as age and sex, can impact the genotype–phenotype relationship. We conclude that GEIs mediated by the social environment are important in the evolution and maintenance of individual differences in wild social mammals, including individual differences in responses to social stressors. PMID:23569293

Evidence for gene-gene epistatic interactions among susceptibility loci for systemic lupus erythematosus.

PubMed

Hughes, Travis; Adler, Adam; Kelly, Jennifer A; Kaufman, Kenneth M; Williams, Adrienne H; Langefeld, Carl D; Brown, Elizabeth E; Alarcón, Graciela S; Kimberly, Robert P; Edberg, Jeffrey C; Ramsey-Goldman, Rosalind; Petri, Michelle; Boackle, Susan A; Stevens, Anne M; Reveille, John D; Sanchez, Elena; Martín, Javier; Niewold, Timothy B; Vilá, Luis M; Scofield, R Hal; Gilkeson, Gary S; Gaffney, Patrick M; Criswell, Lindsey A; Moser, Kathy L; Merrill, Joan T; Jacob, Chaim O; Tsao, Betty P; James, Judith A; Vyse, Timothy J; Alarcón-Riquelme, Marta E; Harley, John B; Richardson, Bruce C; Sawalha, Amr H

2012-02-01

Several confirmed genetic susceptibility loci for lupus have been described. To date, no clear evidence for genetic epistasis in lupus has been established. The aim of this study was to test for gene-gene interactions in a number of known lupus susceptibility loci. Eighteen single-nucleotide polymorphisms tagging independent and confirmed lupus susceptibility loci were genotyped in a set of 4,248 patients with lupus and 3,818 normal healthy control subjects of European descent. Epistasis was tested by a 2-step approach using both parametric and nonparametric methods. The false discovery rate (FDR) method was used to correct for multiple testing. We detected and confirmed gene-gene interactions between the HLA region and CTLA4, IRF5, and ITGAM and between PDCD1 and IL21 in patients with lupus. The most significant interaction detected by parametric analysis was between rs3131379 in the HLA region and rs231775 in CTLA4 (interaction odds ratio 1.19, Z = 3.95, P = 7.8 × 10(-5) [FDR ≤0.05], P for multifactor dimensionality reduction = 5.9 × 10(-45)). Importantly, our data suggest that in patients with lupus, the presence of the HLA lupus risk alleles in rs1270942 and rs3131379 increases the odds of also carrying the lupus risk allele in IRF5 (rs2070197) by 17% and 16%, respectively (P = 0.0028 and P = 0.0047, respectively). We provide evidence for gene-gene epistasis in systemic lupus erythematosus. These findings support a role for genetic interaction contributing to the complexity of lupus heritability. Copyright © 2012 by the American College of Rheumatology.

Education and alcohol use: A study of gene-environment interaction in young adulthood.

PubMed

Barr, Peter B; Salvatore, Jessica E; Maes, Hermine; Aliev, Fazil; Latvala, Antti; Viken, Richard; Rose, Richard J; Kaprio, Jaakko; Dick, Danielle M

2016-08-01

The consequences of heavy alcohol use remain a serious public health problem. Consistent evidence has demonstrated that both genetic and social influences contribute to alcohol use. Research on gene-environment interaction (GxE) has also demonstrated that these social and genetic influences do not act independently. Instead, certain environmental contexts may limit or exacerbate an underlying genetic predisposition. However, much of the work on GxE and alcohol use has focused on adolescence and less is known about the important environmental contexts in young adulthood. Using data from the young adult wave of the Finnish Twin Study, FinnTwin12 (N = 3402), we used biometric twin modeling to test whether education moderated genetic risk for alcohol use as assessed by drinking frequency and intoxication frequency. Education is important because it offers greater access to personal resources and helps determine one's position in the broader stratification system. Results from the twin models show that education did not moderate genetic variance components and that genetic risk was constant across levels of education. Instead, education moderated environmental variance so that under conditions of low education, environmental influences explained more of the variation in alcohol use outcomes. The implications and limitations of these results are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Education and Alcohol Use: A Study of Gene-Environment Interaction in Young Adulthood

PubMed Central

Barr, Peter B.; Salvatore, Jessica E.; Maes, Hermine; Aliev, Fazil; Latvala, Antti; Viken, Richard; Rose, Richard J.; Kaprio, Jaakko; Dick, Danielle M.

2016-01-01

The consequences of heavy alcohol use remain a serious public health problem. Consistent evidence has demonstrated that both genetic and social influences contribute to alcohol use. Research on gene-environment interaction (GxE) has also demonstrated that these social and genetic influences do not act independently. Instead, certain environmental contexts may limit or exacerbate an underlying genetic predisposition. However, much of the work on GxE and alcohol use has focused on adolescence and less is known about the important environmental contexts in young adulthood. Using data from the young adult wave of the Finnish Twin Study, FinnTwin12 (N=3,402), we used biometric twin modeling to test whether education moderated genetic risk for alcohol use as assessed by drinking frequency and intoxication frequency. Education is important because it offers greater access to personal resources and helps determine one’s position in the broader stratification system. Results from the twin models show that education did not moderate genetic variance components and that genetic risk was constant across levels of education. Instead, education moderated environmental variance so that under conditions of low education, environmental influences explained more of the variation in alcohol use outcomes. The implications and limitations of these results are discussed. PMID:27367897

SNP by SNP by environment interaction network of alcoholism.

PubMed

Zollanvari, Amin; Alterovitz, Gil

2017-03-14

Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50-80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing interactions between a host of genetic variants and their association with non-genetic factors. In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest. Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP [Formula: see text]SNP[Formula: see text]E network. The proposed framework is quite general and can be potentially applied to the study of other complex traits.

Gene-environment interactions controlling energy and glucose homeostasis and the developmental origins of obesity.

PubMed

Bouret, Sebastien; Levin, Barry E; Ozanne, Susan E

2015-01-01

Obesity and type 2 diabetes mellitus (T2DM) often occur together and affect a growing number of individuals in both the developed and developing worlds. Both are associated with a number of other serious illnesses that lead to increased rates of mortality. There is likely a polygenic mode of inheritance underlying both disorders, but it has become increasingly clear that the pre- and postnatal environments play critical roles in pushing predisposed individuals over the edge into a disease state. This review focuses on the many genetic and environmental variables that interact to cause predisposed individuals to become obese and diabetic. The brain and its interactions with the external and internal environment are a major focus given the prominent role these interactions play in the regulation of energy and glucose homeostasis in health and disease. Copyright © 2015 the American Physiological Society.

Gene-Environment Interactions Controlling Energy and Glucose Homeostasis and the Developmental Origins of Obesity

PubMed Central

Bouret, Sebastien; Levin, Barry E.; Ozanne, Susan E.

2015-01-01

Obesity and type 2 diabetes mellitus (T2DM) often occur together and affect a growing number of individuals in both the developed and developing worlds. Both are associated with a number of other serious illnesses that lead to increased rates of mortality. There is likely a polygenic mode of inheritance underlying both disorders, but it has become increasingly clear that the pre- and postnatal environments play critical roles in pushing predisposed individuals over the edge into a disease state. This review focuses on the many genetic and environmental variables that interact to cause predisposed individuals to become obese and diabetic. The brain and its interactions with the external and internal environment are a major focus given the prominent role these interactions play in the regulation of energy and glucose homeostasis in health and disease. PMID:25540138

Gene function prediction with gene interaction networks: a context graph kernel approach.

PubMed

Li, Xin; Chen, Hsinchun; Li, Jiexun; Zhang, Zhu

2010-01-01

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium.

PubMed

Hohman, Timothy J; Bush, William S; Jiang, Lan; Brown-Gentry, Kristin D; Torstenson, Eric S; Dudek, Scott M; Mukherjee, Shubhabrata; Naj, Adam; Kunkle, Brian W; Ritchie, Marylyn D; Martin, Eden R; Schellenberg, Gerard D; Mayeux, Richard; Farrer, Lindsay A; Pericak-Vance, Margaret A; Haines, Jonathan L; Thornton-Wells, Tricia A

2016-02-01

Late-onset Alzheimer disease (AD) has a complex genetic etiology, involving locus heterogeneity, polygenic inheritance, and gene-gene interactions; however, the investigation of interactions in recent genome-wide association studies has been limited. We used a biological knowledge-driven approach to evaluate gene-gene interactions for consistency across 13 data sets from the Alzheimer Disease Genetics Consortium. Fifteen single nucleotide polymorphism (SNP)-SNP pairs within 3 gene-gene combinations were identified: SIRT1 × ABCB1, PSAP × PEBP4, and GRIN2B × ADRA1A. In addition, we extend a previously identified interaction from an endophenotype analysis between RYR3 × CACNA1C. Finally, post hoc gene expression analyses of the implicated SNPs further implicate SIRT1 and ABCB1, and implicate CDH23 which was most recently identified as an AD risk locus in an epigenetic analysis of AD. The observed interactions in this article highlight ways in which genotypic variation related to disease may depend on the genetic context in which it occurs. Further, our results highlight the utility of evaluating genetic interactions to explain additional variance in AD risk and identify novel molecular mechanisms of AD pathogenesis. Copyright © 2016 Elsevier Inc. All rights reserved.

The interaction of fungi with the environment orchestrated by RNAi.

PubMed

Villalobos-Escobedo, José Manuel; Herrera-Estrella, Alfredo; Carreras-Villaseñor, Nohemí

2016-01-01

The fungal kingdom has been key in the investigation of the biogenesis and function of small RNAs (sRNAs). The discovery of phenomena such as quelling in Neurospora crassa represents pioneering work in the identification of the main elements of the RNA interference (RNAi) machinery. Recent discoveries in the regulatory mechanisms in some yeast and filamentous fungi are helping us reach a deeper understanding of the transcriptional and post-transcriptional gene-silencing mechanisms involved in genome protection against viral infections, DNA damage and transposon activity. Although most of these mechanisms are reasonably well understood, their role in the physiology, response to the environment and interaction of fungi with other organisms had remained elusive. Nevertheless, studies in fungi such as Mucor circinelloides, Magnaporthe oryzae, Cryptococcus neoformans, Trichoderma atroviride, Botrytis cinerea and others have started to shed light on the relevance of the RNAi pathway. In these fungi gene regulation by RNAi is important for growth, reproduction, control of viral infections and transposon activity, as well as in the development of antibiotic resistance and interactions with their hosts. Moreover, the increasing number of reports of the discovery of microRNA-like RNAs in fungi under different conditions highlights the importance of fungi as models for understanding adaptation to the environment using regulation by sRNAs. The goal of this review is to provide the reader with an up-to-date overview of the importance of RNAi in the interaction of fungi with their environment. © 2016 by The Mycological Society of America.

An interactional network of genes involved in chitin synthesis in Saccharomyces cerevisiae.

PubMed

Lesage, Guillaume; Shapiro, Jesse; Specht, Charles A; Sdicu, Anne-Marie; Ménard, Patrice; Hussein, Shamiza; Tong, Amy Hin Yan; Boone, Charles; Bussey, Howard

2005-02-16

In S. cerevisiae the beta-1,4-linked N-acetylglucosamine polymer, chitin, is synthesized by a family of 3 specialized but interacting chitin synthases encoded by CHS1, CHS2 and CHS3. Chs2p makes chitin in the primary septum, while Chs3p makes chitin in the lateral cell wall and in the bud neck, and can partially compensate for the lack of Chs2p. Chs3p requires a pathway of Bni4p, Chs4p, Chs5p, Chs6p and Chs7p for its localization and activity. Chs1p is thought to have a septum repair function after cell separation. To further explore interactions in the chitin synthase family and to find processes buffering chitin synthesis, we compiled a genetic interaction network of genes showing synthetic interactions with CHS1, CHS3 and genes involved in Chs3p localization and function and made a phenotypic analysis of their mutants. Using deletion mutants in CHS1, CHS3, CHS4, CHS5, CHS6, CHS7 and BNI4 in a synthetic genetic array analysis we assembled a network of 316 interactions among 163 genes. The interaction network with CHS3, CHS4, CHS5, CHS6, CHS7 or BNI4 forms a dense neighborhood, with many genes functioning in cell wall assembly or polarized secretion. Chitin levels were altered in 54 of the mutants in individually deleted genes, indicating a functional relationship between them and chitin synthesis. 32 of these mutants triggered the chitin stress response, with elevated chitin levels and a dependence on CHS3. A large fraction of the CHS1-interaction set was distinct from that of the CHS3 network, indicating broad roles for Chs1p in buffering both Chs2p function and more global cell wall robustness. Based on their interaction patterns and chitin levels we group interacting mutants into functional categories. Genes interacting with CHS3 are involved in the amelioration of cell wall defects and in septum or bud neck chitin synthesis, and we newly assign a number of genes to these functions. Our genetic analysis of genes not interacting with CHS3 indicate expanded

An interactional network of genes involved in chitin synthesis in Saccharomyces cerevisiae

PubMed Central

Lesage, Guillaume; Shapiro, Jesse; Specht, Charles A; Sdicu, Anne-Marie; Ménard, Patrice; Hussein, Shamiza; Tong, Amy Hin Yan; Boone, Charles; Bussey, Howard

2005-01-01

Background In S. cerevisiae the β-1,4-linked N-acetylglucosamine polymer, chitin, is synthesized by a family of 3 specialized but interacting chitin synthases encoded by CHS1, CHS2 and CHS3. Chs2p makes chitin in the primary septum, while Chs3p makes chitin in the lateral cell wall and in the bud neck, and can partially compensate for the lack of Chs2p. Chs3p requires a pathway of Bni4p, Chs4p, Chs5p, Chs6p and Chs7p for its localization and activity. Chs1p is thought to have a septum repair function after cell separation. To further explore interactions in the chitin synthase family and to find processes buffering chitin synthesis, we compiled a genetic interaction network of genes showing synthetic interactions with CHS1, CHS3 and genes involved in Chs3p localization and function and made a phenotypic analysis of their mutants. Results Using deletion mutants in CHS1, CHS3, CHS4, CHS5, CHS6, CHS7 and BNI4 in a synthetic genetic array analysis we assembled a network of 316 interactions among 163 genes. The interaction network with CHS3, CHS4, CHS5, CHS6, CHS7 or BNI4 forms a dense neighborhood, with many genes functioning in cell wall assembly or polarized secretion. Chitin levels were altered in 54 of the mutants in individually deleted genes, indicating a functional relationship between them and chitin synthesis. 32 of these mutants triggered the chitin stress response, with elevated chitin levels and a dependence on CHS3. A large fraction of the CHS1-interaction set was distinct from that of the CHS3 network, indicating broad roles for Chs1p in buffering both Chs2p function and more global cell wall robustness. Conclusion Based on their interaction patterns and chitin levels we group interacting mutants into functional categories. Genes interacting with CHS3 are involved in the amelioration of cell wall defects and in septum or bud neck chitin synthesis, and we newly assign a number of genes to these functions. Our genetic analysis of genes not interacting with

Interaction between nonsynonymous polymorphisms in PLA2G7 gene and smoking on the risk of coronary heart disease in a Chinese population.

PubMed

Chi, Yunpeng; Shi, Conghong; Zhang, Xiaojiang; Xi, Yang

2018-05-04

To investigate the impact of PLA2G7 polymorphism, and additional their interactions with smoking and drinking on coronary heart disease (CHD) risk based on Chinese population. GMDR model was used to screen the best gene-smoking and gene-drinking interaction combinations. Logistic regression was performed to investigate association between 4 SNPs and CHD, and the interaction effect between rs1805017 and smoking. For CHD patient-control haplotype analyses, the SHEsis online haplotype analysis software ( http://analysis.bio-x.cn/myAnalysis.php ) was employed. CHD risks were higher in carriers of homozygous mutant of rs1805017 and rs1805018 than those with wild-type homozygotes, OR (95% CI) were 1.45 (1.16-1.92) and 1.51 (1.23-1.97), respectively, but the other two SNPs, rs16874954 and rs1051931 were not significant associated with CHD risks. GMDR analysis indicated that there was a significant two-locus model (p = 0.0107) involving rs1805017 and smoking, indicating a potential gene-environment interaction between rs1805017 and smoking. But we did not found any gene-drinking and gene-gene interaction combinations in GMDR models. The haplotype R-I was observed most frequently in two groups, with 47.43 and 54.38% in the case and control group of the population, respectively. The results also indicated that the haplotype containing the rs1805017-H and rs1805018-T alleles were associated with a statistically increased CHD risk, OR (95% CI) 1.43 (1.10-1.86), p = 0.0021. Polymorphisms in rs1805017 and rs1805018, additional interaction between rs1805017 and smoking, and haplotype containing the rs1805017-H and rs1805018-T alleles were associated with increased CHD risk.

Classroom Environments: An Experiential Analysis of the Pupil-Teacher Visual Interaction in Uruguay

ERIC Educational Resources Information Center

Cardellino, Paula; Araneda, Claudio; García Alvarado, Rodrigo

2017-01-01

We argue that the traditional physical environment is commonly taken for granted and that little consideration has been given to how this affects pupil-teacher interactions. This article presents evidence that certain physical environments do not allow equal visual interaction and, as a result, we derive a set of basic guiding principles that…

Neighborhood alcohol outlet density and genetic influences on alcohol use: evidence for gene-environment interaction.

PubMed

Slutske, Wendy S; Deutsch, Arielle R; Piasecki, Thomas M

2018-05-07

Genetic influences on alcohol involvement are likely to vary as a function of the 'alcohol environment,' given that exposure to alcohol is a necessary precondition for genetic risk to be expressed. However, few gene-environment interaction studies of alcohol involvement have focused on characteristics of the community-level alcohol environment. The goal of this study was to examine whether living in a community with more alcohol outlets would facilitate the expression of the genetic propensity to drink in a genetically-informed national survey of United States young adults. The participants were 2434 18-26-year-old twin, full-, and half-sibling pairs from Wave III of the National Longitudinal Study of Adolescent to Adult Health. Participants completed in-home interviews in which alcohol use was assessed. Alcohol outlet densities were extracted from state-level liquor license databases aggregated at the census tract level to derive the density of outlets. There was evidence that the estimates of genetic and environmental influences on alcohol use varied as a function of the density of alcohol outlets in the community. For example, the heritability of the frequency of alcohol use for those residing in a neighborhood with ten or more outlets was 74% (95% confidence limits = 55-94%), compared with 16% (95% confidence limits = 0-34%) for those in a neighborhood with zero outlets. This moderating effect of alcohol outlet density was not explained by the state of residence, population density, or neighborhood sociodemographic characteristics. The results suggest that living in a neighborhood with many alcohol outlets may be especially high-risk for those individuals who are genetically predisposed to frequently drink.

Testing Gene-Gene Interactions in the Case-Parents Design

PubMed Central

Yu, Zhaoxia

2011-01-01

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used. PMID:21778736

Attachment style and oxytocin receptor gene variation interact in influencing social anxiety.

PubMed

Notzon, S; Domschke, K; Holitschke, K; Ziegler, C; Arolt, V; Pauli, P; Reif, A; Deckert, J; Zwanzger, P

2016-01-01

Social anxiety has been suggested to be promoted by an insecure attachment style. Oxytocin is discussed as a mediator of trust and social bonding as well as a modulator of social anxiety. Applying a gene-environment (G × E) interaction approach, in the present pilot study the main and interactive effects of attachment styles and oxytocin receptor (OXTR) gene variation were probed in a combined risk factor model of social anxiety in healthy probands. Participants (N = 388; 219 females, 169 males; age 24.7 ± 4.7 years) were assessed for anxiety in social situations (Social Phobia and Anxiety Inventory) depending on attachment style (Adult Attachment Scale, AAS) and OXTR rs53576 A/G genotype. A less secure attachment style was significantly associated with higher social anxiety. This association was partly modulated by OXTR genotype, with a stronger negative influence of a less secure attachment style on social anxiety in A allele carriers as compared to GG homozygotes. The present pilot data point to a strong association of less secure attachment and social anxiety as well as to a gene-environment interaction effect of OXTR rs53576 genotype and attachment style on social anxiety possibly constituting a targetable combined risk marker of social anxiety disorder.

Gene–environment interaction between the oxytocin receptor (OXTR) gene and parenting behaviour on children’s theory of mind

PubMed Central

Wade, Mark; Hoffmann, Thomas J.; Jenkins, Jennifer M.

2015-01-01

Theory of mind (ToM) is the ability to interpret and understand human behaviour by representing the mental states of others. Like many human capacities, ToM is thought to develop through both complex biological and socialization mechanisms. However, no study has examined the joint effect of genetic and environmental influences on ToM. This study examined how variability in the oxytocin receptor gene (OXTR) and parenting behaviour—two widely studied factors in ToM development—interacted to predict ToM in pre-school-aged children. Participants were 301 children who were part of an ongoing longitudinal birth cohort study. ToM was assessed at age 4.5 using a previously validated scale. Parenting was assessed through observations of mothers’ cognitively sensitive behaviours. Using a family-based association design, it was suggestive that a particular variant (rs11131149) interacted with maternal cognitive sensitivity on children’s ToM (P = 0.019). More copies of the major allele were associated with higher ToM as a function of increasing cognitive sensitivity. A sizeable 26% of the variability in ToM was accounted for by this interaction. This study provides the first empirical evidence of gene–environment interactions on ToM, supporting the notion that genetic factors may be modulated by potent environmental influences early in development. PMID:25977357

Shared epitope-aryl hydrocarbon receptor crosstalk underlies the mechanism of gene-environment interaction in autoimmune arthritis.

PubMed

Fu, Jiaqi; Nogueira, Sarah V; Drongelen, Vincent van; Coit, Patrick; Ling, Song; Rosloniec, Edward F; Sawalha, Amr H; Holoshitz, Joseph

2018-05-01

The susceptibility to autoimmune diseases is affected by genetic and environmental factors. In rheumatoid arthritis (RA), the shared epitope (SE), a five-amino acid sequence motif encoded by RA-associated HLA-DRB1 alleles, is the single most significant genetic risk factor. The risk conferred by the SE is increased in a multiplicative way by exposure to various environmental pollutants, such as cigarette smoke. The mechanism of this synergistic interaction is unknown. It is worth noting that the SE has recently been found to act as a signal transduction ligand that facilitates differentiation of Th17 cells and osteoclasts in vitro and in vivo. Intriguingly, the aryl hydrocarbon receptor (AhR), a transcription factor that mediates the xenobiotic effects of many pollutants, including tobacco combustion products, has been found to activate similar biologic effects. Prompted by these similarities, we sought to determine whether the SE and AhR signaling pathways interact in autoimmune arthritis. Here we uncovered a nuclear factor kappa B-mediated synergistic interaction between the SE and AhR pathways that leads to markedly enhanced osteoclast differentiation and Th17 polarization in vitro. Administration of AhR pathway agonists to transgenic mice carrying human SE-coding alleles resulted in a robust increase in arthritis severity, bone destruction, overabundance of osteoclasts, and IL17-expressing cells in the inflamed joints and draining lymph nodes of arthritic mice. Thus, this study identifies a previously unrecognized mechanism of gene-environment interaction that could provide insights into the well-described but poorly understood amplification of the genetic risk for RA upon exposure to environmental pollutants. Copyright © 2018 the Author(s). Published by PNAS.

Gene-Environment Interplay in Physical, Psychological, and Cognitive Domains in Mid to Late Adulthood: Is APOE a Variability Gene?

PubMed

Reynolds, Chandra A; Gatz, Margaret; Christensen, Kaare; Christiansen, Lene; Dahl Aslan, Anna K; Kaprio, Jaakko; Korhonen, Tellervo; Kremen, William S; Krueger, Robert; McGue, Matt; Neiderhiser, Jenae M; Pedersen, Nancy L

2016-01-01

Despite emerging interest in gene-environment interaction (GxE) effects, there is a dearth of studies evaluating its potential relevance apart from specific hypothesized environments and biometrical variance trends. Using a monozygotic within-pair approach, we evaluated evidence of G×E for body mass index (BMI), depressive symptoms, and cognition (verbal, spatial, attention, working memory, perceptual speed) in twin studies from four countries. We also evaluated whether APOE is a 'variability gene' across these measures and whether it partly represents the 'G' in G×E effects. In all three domains, G×E effects were pervasive across country and gender, with small-to-moderate effects. Age-cohort trends were generally stable for BMI and depressive symptoms; however, they were variable-with both increasing and decreasing age-cohort trends-for different cognitive measures. Results also suggested that APOE may represent a 'variability gene' for depressive symptoms and spatial reasoning, but not for BMI or other cognitive measures. Hence, additional genes are salient beyond APOE.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

PubMed Central

Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

PubMed

Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Chemical-gene interaction networks and causal reasoning for ...

EPA Pesticide Factsheets

Evaluating the potential human health and ecological risks associated with exposures to complex chemical mixtures in the environment is one of the main challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrate chemical monitoring and biological effects data to evaluate risks associated with chemicals present in the environment. Here, we used prior knowledge about chemical-gene interactions to develop a knowledge assembly model for detected chemicals at five locations near the North Branch and Chisago wastewater treatment plants (WWTP) in the St. Croix River Basin, MN and WI. The assembly model was used to generate hypotheses about the biological impacts of the chemicals at each location. The hypotheses were tested using empirical hepatic gene expression data from fathead minnows exposed for 12 d at each location. Empirical gene expression data were also mapped to the assembly models to evaluate the likelihood of a chemical contributing to the observed biological responses using richness and concordance statistics. The prior knowledge approach was able predict the observed biological pathways impacted at one site but not the other. Atrazine was identified as a potential contributor to the observed gene expression responses at a location upstream of the North Branch WTTP. Four chemicals were identified as contributors to the observed biological responses at the effluent and downstream o

Phthalic acid chemical probes synthesized for protein-protein interaction analysis.

PubMed

Liang, Shih-Shin; Liao, Wei-Ting; Kuo, Chao-Jen; Chou, Chi-Hsien; Wu, Chin-Jen; Wang, Hui-Min

2013-06-24

Plasticizers are additives that are used to increase the flexibility of plastic during manufacturing. However, in injection molding processes, plasticizers cannot be generated with monomers because they can peel off from the plastics into the surrounding environment, water, or food, or become attached to skin. Among the various plasticizers that are used, 1,2-benzenedicarboxylic acid (phthalic acid) is a typical precursor to generate phthalates. In addition, phthalic acid is a metabolite of diethylhexyl phthalate (DEHP). According to Gene_Ontology gene/protein database, phthalates can cause genital diseases, cardiotoxicity, hepatotoxicity, nephrotoxicity, etc. In this study, a silanized linker (3-aminopropyl triethoxyslane, APTES) was deposited on silicon dioxides (SiO2) particles and phthalate chemical probes were manufactured from phthalic acid and APTES-SiO2. These probes could be used for detecting proteins that targeted phthalic acid and for protein-protein interactions. The phthalic acid chemical probes we produced were incubated with epithelioid cell lysates of normal rat kidney (NRK-52E cells) to detect the interactions between phthalic acid and NRK-52E extracted proteins. These chemical probes interacted with a number of chaperones such as protein disulfide-isomerase A6, heat shock proteins, and Serpin H1. Ingenuity Pathways Analysis (IPA) software showed that these chemical probes were a practical technique for protein-protein interaction analysis.

From Genes to Networks: Characterizing Gene-Regulatory Interactions in Plants.

PubMed

Kaufmann, Kerstin; Chen, Dijun

2017-01-01

Plants, like other eukaryotes, have evolved complex mechanisms to coordinate gene expression during development, environmental response, and cellular homeostasis. Transcription factors (TFs), accompanied by basic cofactors and posttranscriptional regulators, are key players in gene-regulatory networks (GRNs). The coordinated control of gene activity is achieved by the interplay of these factors and by physical interactions between TFs and DNA. Here, we will briefly outline recent technological progress made to elucidate GRNs in plants. We will focus on techniques that allow us to characterize physical interactions in GRNs in plants and to analyze their regulatory consequences. Targeted manipulation allows us to test the relevance of specific gene-regulatory interactions. The combination of genome-wide experimental approaches with mathematical modeling allows us to get deeper insights into key-regulatory interactions and combinatorial control of important processes in plants.

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.

PubMed

Mazaya, Maulida; Trinh, Hung-Cuong; Kwon, Yung-Keun

2017-12-21

Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene

[Analytic methods for seed models with genotype x environment interactions].

PubMed

Zhu, J

1996-01-01

Genetic models with genotype effect (G) and genotype x environment interaction effect (GE) are proposed for analyzing generation means of seed quantitative traits in crops. The total genetic effect (G) is partitioned into seed direct genetic effect (G0), cytoplasm genetic of effect (C), and maternal plant genetic effect (Gm). Seed direct genetic effect (G0) can be further partitioned into direct additive (A) and direct dominance (D) genetic components. Maternal genetic effect (Gm) can also be partitioned into maternal additive (Am) and maternal dominance (Dm) genetic components. The total genotype x environment interaction effect (GE) can also be partitioned into direct genetic by environment interaction effect (G0E), cytoplasm genetic by environment interaction effect (CE), and maternal genetic by environment interaction effect (GmE). G0E can be partitioned into direct additive by environment interaction (AE) and direct dominance by environment interaction (DE) genetic components. GmE can also be partitioned into maternal additive by environment interaction (AmE) and maternal dominance by environment interaction (DmE) genetic components. Partitions of genetic components are listed for parent, F1, F2 and backcrosses. A set of parents, their reciprocal F1 and F2 seeds is applicable for efficient analysis of seed quantitative traits. MINQUE(0/1) method can be used for estimating variance and covariance components. Unbiased estimation for covariance components between two traits can also be obtained by the MINQUE(0/1) method. Random genetic effects in seed models are predictable by the Adjusted Unbiased Prediction (AUP) approach with MINQUE(0/1) method. The jackknife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects, which can be further used in a t-test for parameter. Unbiasedness and efficiency for estimating variance components and predicting genetic effects are tested by

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Testing differential susceptibility: Plasticity genes, the social environment, and their interplay in adolescent response inhibition.

PubMed

Richards, Jennifer S; Arias Vásquez, Alejandro; van Rooij, Daan; van der Meer, Dennis; Franke, Barbara; Hoekstra, Pieter J; Heslenfeld, Dirk J; Oosterlaan, Jaap; Faraone, Stephen V; Hartman, Catharina A; Buitelaar, Jan K

2017-06-01

Impaired inhibitory control is a key feature of attention-deficit/hyperactivity disorder (ADHD). We investigated gene-environment interaction (GxE) as a possible contributing factor to response inhibition variation in context of the differential susceptibility theory. This states individuals carrying plasticity gene variants will be more disadvantaged in negative, but more advantaged in positive environments. Behavioural and neural measures of response inhibition were assessed during a Stop-signal task in participants with (N = 197) and without (N = 295) ADHD, from N = 278 families (age M = 17.18, SD =3.65). We examined GxE between candidate plasticity genes (DAT1, 5-HTT, DRD4) and social environments (maternal expressed emotion, peer affiliation). A DRD4 × Positive peer affiliation interaction was found on the right fusiform gyrus (rFG) activation during successful inhibition. Further, 5-HTT short allele carriers showed increased rFG activation during failed inhibitions. Maternal warmth and positive peer affiliation were positively associated with right inferior frontal cortex activation during successful inhibition. Deviant peer affiliation was positively related to the error rate. While a pattern of differential genetic susceptibility was found, more clarity on the role of the FG during response inhibition is warranted before firm conclusions can be made. Positive and negative social environments were related to inhibitory control. This extends previous research emphasizing adverse environments.

Study design options in evaluating gene-environment interactions: practical considerations for a planned case-control study of pediatric leukemia.

PubMed

Goodman, Michael; Dana Flanders, W

2007-04-01

We compare methodological approaches for evaluating gene-environment interaction using a planned study of pediatric leukemia as a practical example. We considered three design options: a full case-control study (Option I), a case-only study (Option II), and a partial case-control study (Option III), in which information on controls is limited to environmental exposure only. For each design option we determined its ability to measure the main effects of environmental factor E and genetic factor G, and the interaction between E and G. Using the leukemia study example, we calculated sample sizes required to detect and odds ratio (OR) of 2.0 for E alone, an OR of 10 for G alone and an interaction G x E of 3. Option I allows measuring both main effects and interaction, but requires a total sample size of 1,500 cases and 1,500 controls. Option II allows measuring only interaction, but requires just 121 cases. Option III allows calculating the main effect of E, and interaction, but not the main effect of G, and requires a total of 156 cases and 133 controls. In this case, the partial case-control study (Option III) appears to be more efficient with respect to its ability to answer the research questions for the amount of resources required. The design options considered in this example are not limited to observational epidemiology and may be applicable in studies of pharmacogenomics, survivorship, and other areas of pediatric ALL research.

Interactive effects of 5-HTTLPR genotype and rearing environment on affective attitude towards own infant in Japanese mothers.

PubMed

Sawano, Erika; Doi, Hirokazu; Nagai, Tomoko; Ikeda, Satoko; Shinohara, Kauyuki

2017-05-15

Maternal positive attitude towards one's own infant is the cornerstone of effective parenting. Previous research has revealed an influence of both genetic and environmental factors on maternal parenting behavior, but little is known of the potential gene-environment interaction in shaping a mother's affective attitude. To address this gap, we investigated the effect of a mother's childhood rearing environment and a serotonin transporter gene polymorphism (5-HTTLPR) on affective attitude towards her infant. Our analyses found an interactive effect between rearing environment and 5-HTTLPR genotype on maternal attitude. Specifically, a poor rearing environment (characterized by low maternal care and high paternal overprotection) decreased positive attitude towards one's own infant in mothers with homozygous short allele genotype. In contrast, this detrimental effect was almost eliminated in long allele carriers. Altogether, our results indicate that the 5-HTTLPR gene moderates the influence of experienced rearing environment on maternal parental behavior in a manner consistent with the notion that the short 5-HTTLPR allele amplifies environmental influence. Copyright © 2016 Elsevier B.V. All rights reserved.

Genes related to antioxidant metabolism are involved in Methylobacterium mesophilicum-soybean interaction.

PubMed

Araújo, Welington Luiz; Santos, Daiene Souza; Dini-Andreote, Francisco; Salgueiro-Londoño, Jennifer Katherine; Camargo-Neves, Aline Aparecida; Andreote, Fernando Dini; Dourado, Manuella Nóbrega

2015-10-01

The genus Methylobacterium is composed of pink-pigmented methylotrophic bacterial species that are widespread in natural environments, such as soils, stream water and plants. When in association with plants, this genus colonizes the host plant epiphytically and/or endophytically. This association is known to promote plant growth, induce plant systemic resistance and inhibit plant infection by phytopathogens. In the present study, we focused on evaluating the colonization of soybean seedling-roots by Methylobacterium mesophilicum strain SR1.6/6. We focused on the identification of the key genes involved in the initial step of soybean colonization by methylotrophic bacteria, which includes the plant exudate recognition and adaptation by planktonic bacteria. Visualization by scanning electron microscopy revealed that M. mesophilicum SR1.6/6 colonizes soybean roots surface effectively at 48 h after inoculation, suggesting a mechanism for root recognition and adaptation before this period. The colonization proceeds by the development of a mature biofilm on roots at 96 h after inoculation. Transcriptomic analysis of the planktonic bacteria (with plant) revealed the expression of several genes involved in membrane transport, thus confirming an initial metabolic activation of bacterial responses when in the presence of plant root exudates. Moreover, antioxidant genes were mostly expressed during the interaction with the plant exudates. Further evaluation of stress- and methylotrophic-related genes expression by qPCR showed that glutathione peroxidase and glutathione synthetase genes were up-regulated during the Methylobacterium-soybean interaction. These findings support that glutathione (GSH) is potentially a key molecule involved in cellular detoxification during plant root colonization. In addition to methylotrophic metabolism, antioxidant genes, mainly glutathione-related genes, play a key role during soybean exudate recognition and adaptation, the first step in

An interactive environment for the analysis of large Earth observation and model data sets

NASA Technical Reports Server (NTRS)

Bowman, Kenneth P.; Walsh, John E.; Wilhelmson, Robert B.

1993-01-01

We propose to develop an interactive environment for the analysis of large Earth science observation and model data sets. We will use a standard scientific data storage format and a large capacity (greater than 20 GB) optical disk system for data management; develop libraries for coordinate transformation and regridding of data sets; modify the NCSA X Image and X DataSlice software for typical Earth observation data sets by including map transformations and missing data handling; develop analysis tools for common mathematical and statistical operations; integrate the components described above into a system for the analysis and comparison of observations and model results; and distribute software and documentation to the scientific community.

An interactive environment for the analysis of large Earth observation and model data sets

NASA Technical Reports Server (NTRS)

Bowman, Kenneth P.; Walsh, John E.; Wilhelmson, Robert B.

1992-01-01

We propose to develop an interactive environment for the analysis of large Earth science observation and model data sets. We will use a standard scientific data storage format and a large capacity (greater than 20 GB) optical disk system for data management; develop libraries for coordinate transformation and regridding of data sets; modify the NCSA X Image and X Data Slice software for typical Earth observation data sets by including map transformations and missing data handling; develop analysis tools for common mathematical and statistical operations; integrate the components described above into a system for the analysis and comparison of observations and model results; and distribute software and documentation to the scientific community.

Interactions Between Secondhand Smoke and Genes That Affect Cystic Fibrosis Lung Disease

PubMed Central

Collaco, J. Michael; Vanscoy, Lori; Bremer, Lindsay; McDougal, Kathryn; Blackman, Scott M.; Bowers, Amanda; Naughton, Kathleen; Jennings, Jacky; Ellen, Jonathan; Cutting, Garry R.

2011-01-01