Genome-wide Gene-environment Interaction Study Research Articles

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

BackgroundGenome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92–0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88–0.94).ConclusionsOverall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.

A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women.

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6). In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene-environment interactions in breast cancer risk.

Associations between electronic devices use and common mental traits: A gene-environment interaction model using the UK Biobank data.

Electronic devices use has been reported to be associated with depression. However, limited effort has been provided to elucidate the associations between electronic devices use and mental traits in interaction with genetic factors. We first conducted an observational study consisting of 138 976-383 742 participants for TV watching, 29 636-38 599 participants for computer using and 118 61-330 985 participants for computer playing in the UK Biobank cohort. A linear regression model was used to evaluate the associations between common mental traits and electronic devices use. Subsequently, a genome-wide gene-environment interaction study (GWEIS) was performed by PLINK2.0 to estimate the interaction effects of genes and electronic devices use on the risks of the four mental traits. In the UK Biobank cohort, significant associations were observed between electronic devices use and mental traits (all P < 1.0 × 10-9 ), including depression score (B = 0.094 for TV watching), anxiety score (B = 0.051 for TV watching), cigarette smoking (B = 0.046 for computer using) and alcohol drinking (B = 0.010 for computer playing). GWEIS identified multiple mental traits associated loci, interacting with electronic devices use, such as DCDC2 (rs115986722, P = 4.10 × 10-10 ) for anxiety score and TV watching, PRKCE (rs56181965, P = 9.64 × 10-10 ) for smoking and computer using and FRMD4A (rs56227933, P = 7.42 × 10-11 ) for depression score and computer playing. Our findings suggested that electronic devices use was associated with common mental traits and provided new clues for understanding genetic architecture of mental traits.

Insomnia affects the levels of plasma bilirubin and protein metabolism: an observational study and GWGEIS in UK Biobank cohort

Study objectivesWe aim to explore the mechanism of relationship between insomnia and liver metabolism by examining the gene × insomnia interactions. MethodsIndividual level genotypic and phenotypic data were obtained from the UK Biobank cohort. Regression analysis was first conducted to test the association of insomnia with plasma total bilirubin (TBil; n = 186,793), direct bilirubin (DBil; n = 159,854) and total protein (TP; n = 171,574) in UK Biobank cohort. Second, genome-wide gene-environment interaction study (GWGEIS) was conducted by PLINK 2.0, and FUMA platform was used to identify enriched pathway terms. ResultsIn UK Biobank cohort, we found that TP (P < 2.00 × 10−16), DBil (P = 1.72 × 10−3) and TBil (P = 3.38 × 10−5) were significantly associated with insomnia. GWGEIS of both DBil and TBil observed significant G × INSOMNIA effects between insomnia and UDP Glucuronosyltransferase Family 1 (rs6431558, P = 6.26 × 10−11) gene. GWGEIS of TP also detected several significant genes interacting with insomnia, such as KLF15, (rs70940816, P = 6.77 × 10−10) and DOK7, (rs2344205, P = 1.37 × 10−9). Multiple gene ontology (GO) terms were identified for bilirubin, such as GO_URONIC_ACID_METABOLIC_PROCESS (adjusted P = 4.15 × 10−26). ConclusionOur study results suggested negative associations between insomnia and DBil and TBil; and a positive association between insomnia and TP.

Maternal smoking during pregnancy and risks to depression and anxiety in offspring: An observational study and genome-wide gene-environment interaction analysis in UK biobank cohort

BackgroundMaternal smoking during pregnancy (MSDP) has been reported to be associated with increased anxiety and depression behaviors in offspring. However, there is still scant evidence to support the link between MSDP and anxiety/depression. MethodsUsing the subjects from the UK Biobank cohort (n = 371,903–432,881). Logistic regression analyses were first conducted to test the correlation between MSDP and anxiety/depression in offspring. Second, genome-wide gene-environment interaction study (GWGEIS) analyses were conducted by PLINK, using MSDP as environmental factor. Genetic correlation analysis of anxiety/depression and smoking was conducted by the LDSC software using the published genome-wide association study (GWAS) summary data of four smoking traits (n = 337,334–1,232,091), anxiety (n = 31,880) and depression (n = 490,359). Finally, pathway enrichment analysis was carried out to detect the pathway involved in the development of offspring anxiety caused by the interaction of MSDP × SNPs. ResultsObservational analyses showed that anxiety and depression status in offspring were significantly associated with MSDP (all p < 0.0001). Further GWEGI analyses observed significant MSDP-gene interaction effects at UNC80 gene for anxiety (p = 9.09 × 10−9). LDSC did not detect significant genetic correlation between anxiety and smoking traits. Pathway analysis identified 19 significant pathways for anxiety, such as MANALO_HYPOXIA_UP (FDR = 5.50 × 10−4), REACTOME_ADHERENS_JUNCTIONS_INTERACTIONS (FDR = 0.0304) and ONDER_CDH1_TARGETS_2_UP (FDR = 0.0371). ConclusionOur study results suggested the important impact of MDSP on the risk of anxiety in offspring, partly attributing to environment-gene interactions effects.

Post Genome-Wide Gene-Environment Interaction Study Using Random Survival Forest: Insulin Resistance, Lifestyle Factors, and Colorectal Cancer Risk.

Molecular and genetic pathways of insulin resistance (IR) connecting colorectal cancer and obesity factors in postmenopausal women remain inconclusive. We examined the IR pathways on both genetic and phenotypic perspectives at the genome-wide level. We further constructed colorectal cancer risk profiles with the most predictive IR SNPs and lifestyle factors. In our earlier genome-wide association gene-environmental interaction study, we used data from a large cohort of postmenopausal women in the Women's Health Initiative Database for Genotypes and Phenotypes Study and identified 58 SNPs in relation to IR phenotypes. In this study, we evaluated the identified IR SNPs and selected 34 lifestyles for their association with colorectal cancer risk in a total of 11,078 women (including 736 women with colorectal cancer) using a 2-stage multimodal random survival forest analysis. In overall and subgroup (defined via body mass index, exercise, and dietary-fat intake) analyses, we identified 2 SNPs (LINC00460 rs1725459 and MTRR rs722025) and lifetime cumulative exposure to estrogen (oral contraceptive use) and cigarette smoking as the most common and strongest predictive markers for colorectal cancer risk across the analyses. The combinations of genetic and lifestyle factors had much greater impact on colorectal cancer risk than any individual risk factors, and a possible synergism existed to increase colorectal cancer risk in a gene-behavior dose-dependent manner. Our findings may inform research on the role of IR in the etiology of colorectal cancer and contribute to more accurate prediction of colorectal cancer risk, suggesting potential intervention strategies for women with specific genotypes and lifestyles to reduce their colorectal cancer risk.

Post genome-wide gene-environment interaction study: The effect of genetically driven insulin resistance on breast cancer risk using Mendelian randomization.

PurposeThe role of insulin resistance (IR) in developing postmenopausal breast cancer has not been thoroughly resolved and may be confounded by lifestyle factors such as obesity. We examined whether genetically determined IR is causally associated with breast cancer risk.MethodsWe conducted Mendelian randomization (MR) analyses using individual-level data from our previous meta-analysis of a genome-wide association study (GWAS) (n = 11,109 non-Hispanic white postmenopausal women). Four single-nucleotide polymorphisms were associated with fasting glucose (FG), 2 with fasting insulin (FI), and 6 with homeostatic model assessment–IR (HOMA-IR) but were not associated with obesity. We used this GWAS to employ hazard ratios (HRs) for breast cancer risk by adjusting for potential confounding factors.ResultsNo direct association was observed between comprising 12 IR genetic instruments and breast cancer risk (HR = 0.93, 95% CI: 0.76–1.14). In phenotype-specific analysis, genetically elevated FG was associated with reduced risk for breast cancer (main contributor of this MR-effect estimate: G6PC2 rs13431652; HR = 0.59, 95% CI: 0.35–0.99). Genetically driven FI and HOMA-IR were not significantly associated. Stratification analyses by body mass index, exercise, and dietary fat intake with combined phenotypes showed that genetically elevated IR was associated with greater breast cancer risk in overall obesity and inactive subgroups (single contributor: MTRR/LOC729506 rs13188458; HR = 2.21, 95% CI: 1.03–4.75).ConclusionsWe found complex evidence for causal association between IR and risk of breast cancer, which may support the potential value of intervention trials to lower IR and reduce breast cancer risk.

Breast Cancer Risk and Insulin Resistance: Post Genome-Wide Gene-Environment Interaction Study Using a Random Survival Forest.

Obesity-insulin connections have been considered potential risk factors for postmenopausal breast cancer, and the association between insulin resistance (IR) genotypes and phenotypes can be modified by obesity-lifestyle factors, affecting breast cancer risk. In this study, we explored the role of IR in those pathways at the genome-wide level. We identified IR-genetic factors and selected lifestyles to generate risk profiles for postmenopausal breast cancer. Using large-scale cohort data from postmenopausal women in the Women's Health Initiative Database for Genotypes and Phenotypes Study, our previous genome-wide association gene-behavior interaction study identified 58 loci for associations with IR phenotypes (homeostatic model assessment-IR, hyperglycemia, and hyperinsulinemia). We evaluated those single-nucleotide polymorphisms (SNP) and additional 31 lifestyles in relation to breast cancer risk by conducting a two-stage multimodal random survival forest analysis. We identified the most predictive genetic and lifestyle variables in overall and subgroup analyses [stratified by body mass index (BMI), exercise, and dietary fat intake]. Two SNPs (LINC00460 rs17254590 and MKLN1 rs117911989), exogenous factors related to lifetime cumulative exposure to estrogen, BMI, and dietary alcohol consumption were the most common influential factors across the analyses. Individual SNPs did not have significant associations with breast cancer, but SNPs and lifestyles combined synergistically increased the risk of breast cancer in a gene-behavior, dose-dependent manner. These findings may contribute to more accurate predictions of breast cancer and suggest potential intervention strategies for women with specific genetic and lifestyle factors to reduce their breast cancer risk. SIGNIFICANCE: These findings identify insulin resistance SNPs in combination with lifestyle as synergistic factors for breast cancer risk, suggesting lifestyle changes can prevent breast cancer in women who carry the risk genotypes.

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

BackgroundNeurodevelopment 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.MethodsTo 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.ResultsWe identified a locus on chromosome 8, containing UNC5D, and demonstrated evidence of its genome-wide significance with mental composite scores (rs9642758, pmeta = 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.ConclusionsOur findings suggest that certain genetic polymorphisms within/near genes relevant to neurodevelopment might modify the toxic effects of Pb exposure via oxidative stress.

Genome-wide gene-environment interaction study to identify potential genes for respiratory syncytial virus infection in asthmatic children

Genome-wide gene-environment interaction study to identify potential genes for respiratory syncytial virus infection in asthmatic children

A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density.

IntroductionMammographic density is an established breast cancer risk factor with a strong genetic component and can be increased in women using menopausal hormone therapy (MHT). Here, we aimed to identify genetic variants that may modify the association between MHT use and mammographic density.MethodsThe study comprised 6,298 postmenopausal women from the Mayo Mammography Health Study and nine studies included in the Breast Cancer Association Consortium. We selected for evaluation 1327 single nucleotide polymorphisms (SNPs) showing the lowest P-values for interaction (Pint) in a meta-analysis of genome-wide gene-environment interaction studies with MHT use on risk of breast cancer, 2541 SNPs in candidate genes (AKR1C4, CYP1A1-CYP1A2, CYP1B1, ESR2, PPARG, PRL, SULT1A1-SULT1A2 and TNF) and ten SNPs (AREG-rs10034692, PRDM6-rs186749, ESR1-rs12665607, ZNF365-rs10995190, 8p11.23-rs7816345, LSP1-rs3817198, IGF1-rs703556, 12q24-rs1265507, TMEM184B-rs7289126, and SGSM3-rs17001868) associated with mammographic density in genome-wide studies. We used multiple linear regression models adjusted for potential confounders to evaluate interactions between SNPs and current use of MHT on mammographic density.ResultsNo significant interactions were identified after adjustment for multiple testing. The strongest SNP-MHT interaction (unadjusted Pint <0.0004) was observed with rs9358531 6.5kb 5′ of PRL. Furthermore, three SNPs in PLCG2 that had previously been shown to modify the association of MHT use with breast cancer risk were found to modify also the association of MHT use with mammographic density (unadjusted Pint <0.002), but solely among cases (unadjusted Pint SNP×MHT×case-status <0.02).ConclusionsThe study identified potential interactions on mammographic density between current use of MHT and SNPs near PRL and in PLCG2, which require confirmation. Given the moderate size of the interactions observed, larger studies are needed to identify genetic modifiers of the association of MHT use with mammographic density.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0625-9) contains supplementary material, which is available to authorized users.

Lack of replication of the GRIN2A-by-coffee interaction in Parkinson disease.

Overview The etiology of Parkinson disease (PD) involves both genetic susceptibility and environmental exposures. In particular, coffee consumption is inversely associated with PD but the mechanisms underlying this intriguing association are unknown. According to a recent genome-wide gene–environment interaction study, the inverse coffee–PD association was two times stronger among carriers of the T allele of SNP rs4998386 in gene GRIN2A than in homozygotes for the C allele. We attempted to replicate this result in a similarly sized pooled analysis of 2,289 cases and 2,809 controls from four independent studies (Denmark, France, Seattle-United States (US), and Rochester-US) with detailed caffeinated coffee consumption data and rs4998386 genotypes. Using a variety of definitions of coffee drinking and statistical modeling techniques , we failed to replicate this interaction. Notably, whereas in the original study there was an association between rs4998386 and coffee consumption among controls, but not among cases, none of the datasets analyzed here indicated an association between rs4998386 and coffee consumption among controls. Based on large, well-characterized datasets independent from the original study, our results are not in favor of an interaction between caffeinated coffee consumption and rs4998386 for PD risk and suggest that the original finding may have been driven by an association of coffee consumption with rs4998386 in controls. The next years will likely see an increasing number of papers examining gene–environment interactions at the genome-wide level, which poses important methodological challenges. Our findings underline the need for a careful assessment of the findings of such studies.

Gene-environment interaction effects on lung function- a genome-wide association study within the Framingham heart study

BackgroundPrevious studies in occupational exposure and lung function have focused only on the main effect of occupational exposure or genetics on lung function. Some disease-susceptible genes may be missed due to their low marginal effects, despite potential involvement in the disease process through interactions with the environment. Through comprehensive genome-wide gene-environment interaction studies, we can uncover these susceptibility genes. Our objective in this study was to explore gene by occupational exposure interaction effects on lung function using both the individual SNPs approach and the genetic network approach.MethodsThe study population comprised the Offspring Cohort and the Third Generation from the Framingham Heart Study. We used forced expiratory volume in one second (FEV1) and ratio of FEV1 to forced vital capacity (FVC) as outcomes. Occupational exposures were classified using a population-specific job exposure matrix. We performed genome-wide gene-environment interaction analysis, using the Affymetrix 550 K mapping array for genotyping. A linear regression-based generalized estimating equation was applied to account for within-family relatedness. Network analysis was conducted using results from single-nucleotide polymorphism (SNP)-level analyses and from gene expression study results.ResultsThere were 4,785 participants in total. SNP-level analysis and network analysis identified SNP rs9931086 (Pinteraction =1.16 × 10-7) in gene SLC38A8, which may significantly modify the effects of occupational exposure on FEV1. Genes identified from the network analysis included CTLA-4, HDAC, and PPAR-alpha.ConclusionsOur study implies that SNP rs9931086 in SLC38A8 and genes CTLA-4, HDAC, and PPAR-alpha, which are related to inflammatory processes, may modify the effect of occupational exposure on lung function.

Gene–environment interactions in genome‐wide association studies: current approaches and new directions

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 gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge. We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually. Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research. Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

Genome-wide association study reveals class I MHC–restricted T cell–associated molecule gene (CRTAM) variants interact with vitamin D levels to affect asthma exacerbations

It has recently been shown that vitamin D deficiency can increase asthma development and severity and that variations in vitamin D receptor genes are associated with asthma susceptibility. We sought to find genetic factors that might interact with vitamin D levels to affect the risk of asthma exacerbation. We conducted a genome-wide study of gene-vitamin D interaction on asthma exacerbations using population-based and family-based approaches on 403 subjects and trios from the Childhood Asthma Management Program. Twenty-three polymorphisms with significant interactions were studied in a replication analysis in 584 children from a Costa Rican cohort. We identified 3 common variants in the class I MHC-restricted T cell-associated molecule gene (CRTAM) that were associated with an increased rate of asthma exacerbations based on the presence of a low circulating vitamin D level. These results were replicated in a second independent population (unadjusted combined interaction, P= .00028-.00097; combined odds ratio, 3.28-5.38). One variant, rs2272094, is a nonsynonymous coding polymorphism of CRTAM. Functional studies on cell lines confirmed the interaction of vitamin D and rs2272094 on CRTAM expression. CRTAM is highly expressed in activated human CD8(+) and natural killer T cells, both of which have been implicated in asthmatic patients. The findings highlight an important gene-environment interaction that elucidates the role of vitamin D and CD8(+) and natural killer T cells in asthma exacerbation in a genome-wide gene-environment interaction study that has been replicated in an independent population. The results suggest the potential importance of maintaining adequate vitamin D levels in subsets of high-risk asthmatic patients.