scholarly journals A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

2021 ◽  
Author(s):  
Mohammad Khan ◽  
Matteo Di Scipio ◽  
Conor Judge ◽  
Nicolas Perrot ◽  
Michael Chong ◽  
...  
Keyword(s):  
Interaction Effects ◽  
Environment Interaction ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Marginal Association ◽  
Unbiased Estimates ◽  
Gxe Interactions ◽  
The Uk ◽  
Variance Explained ◽  
Interaction Variance

AbstractCurrent methods to evaluate gene-by-environment (GxE) interactions on biobank-scale datasets are limited. MonsterLM enables multiple linear regression on genome-wide datasets, does not rely on parameters specification and provides unbiased estimates of variance explained by GxE interaction effects. We applied MonsterLM to the UK Biobank for eight blood biomarkers (N=325,991), identifying significant genome-wide interaction variance with waist-to-hip ratio for five biomarkers, with variance explained by interactions ranging from 0.11 to 0.58. 48% to 94% of GxE interaction variance can be attributed to variants without significant marginal association with the phenotype of interest. Conversely, for most traits, >40% of interaction variance was explained by less than 5% of genetic variants. We observed significant improvements in polygenic score prediction with incorporation of GxE interactions in four biomarkers. Our results imply an important contribution of GxE interaction effects, driven largely by a restricted set of variants distinct from loci with strong marginal effects.

scholarly journals GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jisu Shin ◽  
Sang Hong Lee
Keyword(s):  
Complex Traits ◽  
Error Rates ◽  
Type I ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Summary Statistics ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Scale Data ◽  
Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

scholarly journals A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

2020 ◽  
Author(s):  
Arunabha Majumdar ◽  
Kathryn S. Burch ◽  
Sriram Sankararaman ◽  
Bogdan Pasaniuc ◽  
W. James Gauderman ◽  
...  
Keyword(s):  
Genetic Effect ◽  
Single Step ◽  
Environment Interaction ◽  
Step Method ◽  
Gene Environment Interaction ◽  
Gxe Interaction ◽  
Multiple Phenotypes ◽  
Gene Environment ◽  
Genome Wide ◽  
The Uk

AbstractWhile gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18% – 43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two independent genome-wide significant signals of an overall GxE effect on the vector of lipids.

scholarly journals VarExp: Estimating variance explained by Genome-Wide GxE summary statistics

2017 ◽  
Author(s):  
Vincent Laville ◽  
Amy R. Bentley ◽  
Florian Privé ◽  
Xiafoeng Zhu ◽  
Jim Gauderman ◽  
...  
Keyword(s):  
Association Studies ◽  
Statistical Genetics ◽  
Interaction Effects ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Ethical Challenges ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Variance Explained

AbstractMany genomic analyses, such as genome-wide association studies (GWAS) or genome-wide screening for Gene-Environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. In practice, this is commonly estimated using individual genotype data. However, using individual-level data faces practical and ethical challenges when the GWAS results are derived in large consortia through meta-analysis of results from multiple cohorts. In this work, we present a R package, “VarExp”, that allows for the estimation of the percentage of phenotypic variance explained by variants of interest using summary statistics only. Our package allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects, and main genetic and interaction effects jointly. Its implementation integrates all recent methodological developments on the topic and does not need external data to be uploaded by users.The R source code, tutorial and associated example are available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git.

Abstract P530: Pleiotropic Effects on Blood Pressure Traits Using Genome-wide Analysis of Gene-alcohol Interactions

Hypertension ◽  
2017 ◽  
Vol 70 (suppl_1) ◽  
Author(s):  
Aldi T Kraja ◽  
Mary F Feitosa ◽  
Daniel Chasman ◽  
Yun J Sung ◽  
Thomas W Winkler ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Alcohol Consumption ◽  
Meta Analysis ◽  
Left Ventricular ◽  
European Ancestry ◽  
The Novel ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Gxe Interactions ◽  
Interaction Approach

We tested for pleiotropy in European ancestry subjects (N>90K) via GWAS of systolic and diastolic blood pressure (BP), mean arterial pressure, and pulse pressure, using gene (G)-alcohol consumption (E) interactions. The approach was a correlated meta-analysis (PMCID-PMC3773990) that combined simultaneously the 4 BP traits genome-wide GxE interactions summary meta-P values. This approach adjusts for correlations among single traits at the genomic level. A variant was considered pleiotropic when the overall correlated meta-analysis yielded P ≤5E-08 and GxE meta- P ≤E-04 for at least two single traits. The novel pleiotropic variants localize in eight loci. TTLL7 (1p31.1) is a tubulin modifier. DYRK3 (1q32.1) is a transcription regulator. MAPKAPK2 (1q32.1) is a stress-activated serine/threonine-protein kinase involved in cytokine production especially for TNF , IL6 and phosphorylates (among others) LSP1 , identified in our GWAS GxE study for individual BP traits. FSTL5 (4q32.2) is annotated as calcium ion binding . A locus at 11q13.1 includes SNX32 , EFEMP2, and FOSL1 . FOSL1 variants may regulate expression of SNX32 . EFEMP2 is implicated in blood coagulation. CATSPER2 (15q15.3) is a cation channel. CCDC151 (19p13.2) is an outer dynein arm assembly. The functions of two other loci (17q22 and 18q22.3) are unknown. We also identified 4 pleiotropic loci ( SGK223 , TNKS , GATA4 , FTO ) that were found significant at our GxE meta-GWAS of single traits in 572K multi-ancestry individuals. In addition, we detected 24 pleiotropic BP-known loci. Some of these genes relate to alcohol consumption (e.g., BLK , GATA4 , FTO ). TNKS , MAPKAPK2 and FSTL5 interact with the Wnt/β-catenin signaling pathway, which contributes to hypertension. Several pleiotropic variants showed features of regulation by locating at promoter and enhancer histone marks, at DNAse, at proteins binding sites and being eQTL. The 36 novel and BP-known loci comprising 86 significant genes were enriched for Hypertension , Cardiac arrhythmias , Myocardial infarction , Atrial fibrillation, and Left ventricular hypertrophy . Our correlated meta-analysis of GxE interaction approach identified novel pleiotropic loci and validated known BP loci, thus providing insights into the mechanisms of hypertension.

scholarly journals Modeling Parent-Specific Genetic Nurture in Families with Missing Parental Genotypes: Application to Birthweight and BMI

2020 ◽  
Author(s):  
Justin D. Tubbs ◽  
Liang-Dar Hwang ◽  
Justin Luong ◽  
David M. Evans ◽  
Pak C. Sham
Keyword(s):  
Genetic Effects ◽  
Unbiased Estimation ◽  
Paternal Effects ◽  
Genetic Transmission ◽  
Nuclear Families ◽  
Genome Wide ◽  
Unbiased Estimates ◽  
Related Individuals ◽  
Missing Genotypes ◽  
The Uk

AbstractDisaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; Hwang et al., 2020) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body mass index.

scholarly journals Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Josefin Werme ◽  
Sophie van der Sluis ◽  
Danielle Posthuma ◽  
Christiaan A. de Leeuw
Keyword(s):  
Exploratory Study ◽  
Predictive Ability ◽  
Risk Scores ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Sets ◽  
Gene Environment ◽  
Genome Wide ◽  
The Uk ◽  
Main Effects

AbstractGene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.

scholarly journals Assessing the effects of genotype-by-environment interaction on epigenetic, transcriptomic, and phenotypic response in a Pacific salmon

2021 ◽  
Author(s):  
Kris A Christensen ◽  
Jérémy Le Luyer ◽  
Michelle T T Chan ◽  
Eric B Rondeau ◽  
Ben F Koop ◽  
...  
Keyword(s):  
Growth Rate ◽  
Liver Tissue ◽  
Coho Salmon ◽  
Pacific Salmon ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Gxe Interaction ◽  
Differentially Methylated Genes ◽  
Gxe Interactions ◽  
Term Analysis

Abstract Genotype-by-environment (GxE) interactions are non-parallel reaction norms among individuals with different genotypes in response to different environmental conditions. GxE interactions are an extension of phenotypic plasticity and consequently studying such interactions improves our ability to predict effects of different environments on phenotype as well as the fitness of genetically distinct organisms and their capacity to interact with ecosystems. Growth hormone transgenic coho salmon grow much faster than non-transgenics when raised in tank environments, but show little difference in growth when reared in nature-like streams. We used this model system to evaluate potential mechanisms underlying this growth rate GxE interaction, performing RNA-seq to measure gene transcription and whole-genome bisulfite sequencing to measure gene methylation in liver tissue. Gene ontology (GO) term analysis revealed stress as an important biological process potentially influencing growth rate GxE interactions. While few genes with transcription differences also had methylation differences, in promoter or gene regions, many genes were differentially methylated between tank and stream environments. A GO term analysis of differentially methylated genes between tank and stream environments revealed increased methylation in the stream environment of more than 95% of the differentially methylated genes, many with biological processes unrelated to liver function. The lower nutritional condition of the stream environment may cause increased negative regulation of genes less vital for liver tissue function than when fish are reared in tanks with unlimited food availability. These data show a large effect of rearing environment both on gene expression and methylation, but it is less clear that the detected epigenetic marks are responsible for the observed altered growth and physiological responses.

scholarly journals Atlas of epistasis

2021 ◽  
Author(s):  
Clément Chatelain ◽  
Samuel Lessard ◽  
Vincent Thuillier ◽  
Cedric Carliez ◽  
Deepak Rajpal ◽  
...  
Keyword(s):  
Disease Risk ◽  
Fold Increase ◽  
Summary Statistics ◽  
Common Variants ◽  
Mineral Absorption ◽  
Genome Wide ◽  
A Genome ◽  
Marginal Association ◽  
The Uk

AbstractWe performed a genome-wide epistasis search across 502 phenotypes in case control matched cohorts from the UK Biobank. We identified 152,519 genome wide significant interactions in 68 distinct phenotypes, and 3,398 interactions in 19 phenotypes were successfully replicated in independent cohorts from the Finngen consortium. Most interactions (79%) involved variants that did not present significant marginal association and might explain part of the missing heritability for these diseases. In 10 phenotypes we show the presence of epistasis between common variants with intermediate to large effect size (OR > 2) supporting the hypothesis that common diseases are modulated by common variants. Most of the variants in interactions (82%) were more than 1Mb apart and cis-epistasis was hardly found outside the HLA region. Functional annotation of the variants suggests that most mechanisms behind epistasis occurs at the supra pathway level and that intra-gene or intra-pathway epistasis is rare. Surprisingly we find a significant biais toward antagonistic epistasis, representing 60% to 95% of interactions. In type 1 diabetes, hypothyroidism, disorders of mineral absorption, rheumatoid arthritis, asthma, and multiple sclerosis more than 50% of interactions were completely compensating the effect of the marginally associated variant. In psoriasis we identified an interaction between a stop gain variant in CCHCR1 with two missense variants in MUC22 and HSPA1L leading to a 3 fold increase of the effect of CCHCR1 variant on disease risk. Our study shows that there is still much to discover in epistasis and we provide the full summary statistics results to researchers interested in studying epistasis.

scholarly journals Vitamin D and the Risks of Depression and Anxiety: An Observational Analysis and Genome-Wide Environment Interaction Study

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3343
Author(s):  
Zhen Zhang ◽  
Xuena Yang ◽  
Yumeng Jia ◽  
Yan Wen ◽  
Shiqiang Cheng ◽  
...  
Keyword(s):  
Vitamin D ◽  
Genome Wide Association Study ◽  
Polygenic Risk Score ◽  
Environment Interaction ◽  
Depression And Anxiety ◽  
Uk Biobank ◽  
Genome Wide ◽  
Study Results ◽  
Functional Relevance ◽  
The Uk

Previous studies have suggested that vitamin D (VD) was associated with psychiatric diseases, but efforts to elucidate the functional relevance of VD with depression and anxiety from genetic perspective have been limited. Based on the UK Biobank cohort, we first calculated polygenic risk score (PRS) for VD from genome-wide association study (GWAS) data of VD. Linear and logistic regression analysis were conducted to evaluate the associations of VD traits with depression and anxiety traits, respectively. Then, using individual genotype and phenotype data from the UK Biobank, genome-wide environment interaction studies (GWEIS) were performed to identify the potential effects of gene × VD interactions on the risks of depression and anxiety traits. In the UK Biobank cohort, we observed significant associations of blood VD level with depression and anxiety traits, as well as significant associations of VD PRS and depression and anxiety traits. GWEIS identified multiple candidate loci, such as rs114086183 (p = 4.11 × 10−8, LRRTM4) for self-reported depression status and rs149760119 (p = 3.88 × 10−8, GNB5) for self-reported anxiety status. Our study results suggested that VD was negatively associated with depression and anxiety. GWEIS identified multiple candidate genes interacting with VD, providing novel clues for understanding the biological mechanism potential associations between VD and psychiatric disorders.

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