scholarly journals Empirical Hierarchical Bayes Approach to Gene-Environment Interactions: Development and Application to Genome-Wide Association Studies of Lung Cancer in TRICL

2013 ◽  
Vol 37 (6) ◽  
pp. 551-559 ◽  
Author(s):  
Melanie Sohns ◽  
Elena Viktorova ◽  
Christopher I. Amos ◽  
Paul Brennan ◽  
Gord Fehringer ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Association Studies ◽  
Hierarchical Bayes ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genome Wide ◽  
Bayes Approach

Genetic polymorphisms and lung cancer risk: Evidence from meta-analyses and genome-wide association studies

Lung Cancer ◽  
2017 ◽  
Vol 113 ◽  
pp. 18-29 ◽  
Author(s):  
Caiyang Liu ◽  
Huijie Cui ◽  
Dongqing Gu ◽  
Min Zhang ◽  
Yanfei Fang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Genetic Polymorphisms ◽  
Lung Cancer Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Meta Analyses

scholarly journals Geographic Confounding in Genome-Wide Association Studies

2021 ◽  
Author(s):  
Abdel Abdellaoui ◽  
Karin Verweij ◽  
Michel G Nivard
Keyword(s):  
Educational Attainment ◽  
Social Stratification ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Geographic Region ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genome Wide

Abstract Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N = 69,772–271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

scholarly journals Geographic Confounding in Genome-Wide Association Studies

2021 ◽  
Author(s):  
Abdel Abdellaoui ◽  
Karin J.H. Verweij ◽  
Michel G. Nivard
Keyword(s):  
Educational Attainment ◽  
Social Stratification ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Geographic Region ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genome Wide

Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N=69,772-271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

Two-Stage Procedures for the Identification of Gene × Environment and Gene × Gene Interactions in Genome-Wide Association Studies

2016 ◽  
Author(s):  
Charles Kooperberg ◽  
James Y. Dai ◽  
Li Hsu
Keyword(s):  
Association Studies ◽  
Gene Interaction ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Genetic Studies ◽  
Gene Environment ◽  
Genome Wide ◽  
Sequencing Studies ◽  
Generation Sequencing

Genome-wide association studies and next generation sequencing studies offer us an unprecedented opportunity to study the genetic etiology of diseases and other traits. Over the last few years, many replicated associations between SNPs and traits have been published. It is of particular interest to identify how genes may interact with environmental factors and other genes. In this chapter, we show that a two-stage approach, where in the first stage SNPs are screened for their potential to be involved in interactions, and interactions are then tested only among SNPs that pass the screening can greatly enhance power for detecting gene-environment and gene-gene interaction in large genetic studies compared to the tests without screening.

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