Meta-Regression of Gene-Environment Interaction in Genome-Wide Association Studies

2013 ◽  
Vol 12 (4) ◽  
pp. 354-362 ◽  
Author(s):  
Xiaoxiao Xu ◽  
Gang Shi ◽  
Arye Nehorai
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Meta Regression

Current Challenges in the Genetics of Psoriatic Arthritis: A Report from the GRAPPA 2009 Annual Meeting

2011 ◽  
Vol 38 (3) ◽  
pp. 564-566 ◽  
Author(s):  
PROTON RAHMAN
Keyword(s):  
Psoriatic Arthritis ◽  
Annual Meeting ◽  
Association Studies ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Novel Genes ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide

Psoriasis and psoriatic arthritis (PsA) are heterogeneous diseases. While both have a strong genetic basis, it is strongest for PsA, where fewer investigators are studying its genetics. Over the last year the number of independent genetic loci associated with psoriasis has substantially increased, mostly due to completion of multiple genome-wide association studies (GWAS) in psoriasis. At least 2 GWAS efforts are now under way in PsA to identify novel genes in this disease; a metaanalysis of genome-wide scans and further studies must follow to examine the genetics of disease expression, epistatic interaction, and gene-environment interaction. In the long term, it is anticipated that genome-wide sequencing is likely to generate another wave of novel genes in PsA. At the annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) in Stockholm, Sweden, in 2009, members discussed issues and challenges regarding the advancement of the genetics of PsA; results of those discussions are summarized here.

scholarly journals HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

2020 ◽  
Vol 21 (18) ◽  
pp. 6724
Author(s):  
Sungkyoung Choi ◽  
Sungyoung Lee ◽  
Iksoo Huh ◽  
Heungsun Hwang ◽  
Taesung Park
Keyword(s):  
Structural Component ◽  
Latent Variable ◽  
Association Studies ◽  
Component Analysis ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Missing Heritability Problem

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

scholarly journals Haplotype and Haplotype-Environment Interaction Analysis Revealed Roles of SPRY2 for NSCL/P among Chinese Populations

2019 ◽  
Vol 16 (4) ◽  
pp. 557 ◽  
Author(s):  
Ren Zhou ◽  
Mengying Wang ◽  
Wenyong Li ◽  
Siyue Wang ◽  
Hongchen Zheng ◽  
...  
Keyword(s):  
Interaction Analysis ◽  
Genome Wide Association ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Effects ◽  
Multivitamin Supplementation ◽  
Chinese Populations ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is one of common birth defects in China, with genetic and environmental components contributing to the etiology. Genome wide association studies (GWASs) have identified SPRY1 and SPRY2 to be associated with NSCL/P among Chinese populations. This study aimed to further explore potential genetic effect and gene—environment interaction among SPRY genes based on haplotype analysis, using 806 Chinese case—parent NSCL/P trios drawn from an international consortium which conducted a genome-wide association study. After the process of quality control, 190 single nucleotide polymorphisms (SNPs) of SPRY genes were included for analyses. Haplotype and haplotype—environment interaction analyses were conducted in Population-Based Association Test (PBAT) software. A 2-SNP haplotype and three 3-SNP haplotypes showed a significant association with the risk of NSCL/P after Bonferroni correction (corrected significance level = 2.6 × 10−4). Moreover, haplotype—environment interaction analysis identified these haplotypes respectively showing statistically significant interactions with maternal multivitamin supplementation or maternal environmental tobacco smoke. This study showed SPRY2 to be associated with NSCL/P among the Chinese population through not only gene effects, but also a gene—environment interaction, highlighting the importance of considering environmental exposures in the genetic etiological study of NSCL/P.

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.

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