Detecting rare and common haplotype-environment interaction under uncertainty of gene-environment independence assumption

Background: The body of evidence on gene-environment interaction (GEI) related to type 2 diabetes (T2D) has grown in the recent years. However, most studies on GEI have sought to explain variation within individuals of European ancestry and results among ethnic minority groups are inconclusive. Objective: To investigate any interaction between a gene and an environmental factor in relation to T2D among ethnic minority groups living in Europe and North America. Methods: We systematically searched Medline and EMBASE databases for the published literature in English up to 25th March 2019. The screening, data extraction and quality assessment were performed by reviewers independently. Results: 1068 studies identified through our search, of which nine cohorts of six studies evaluating several different GEIs were included. The mean follow-up time in the included studies ranged from 5 to 25.7 years. Most studies were relatively small scale and few provided replication data. All studies included in the review included ethnic minorities from North America (Native-Americans, African- Americans, and Aboriginal Canadian), none of the studies in Europe assessed GEI in relation to T2D incident in ethnic minorities. The only significant GEI among ethnic minorities was HNF1A rs137853240 and smoking on T2D incident among Native-Canadians (Pinteraction = 0.006). Conclusion: There is a need for more studies on GEI among ethnicities, broadening the spectrum of ethnic minority groups being investigated, performing more discovery using genome-wide approaches, larger sample sizes for these studies by collaborating efforts such as the InterConnect approach, and developing a more standardized method of reporting GEI studies are discussed.

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Estimating the effective sample size in association studies of quantitative traits

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab057 ◽

2021 ◽

Author(s):

Andrey Ziyatdinov ◽

Jihye Kim ◽

Dmitry Prokopenko ◽

Florian Privé ◽

Fabien Laporte ◽

...

Keyword(s):

Statistical Power ◽

Quantitative Traits ◽

Mixed Model ◽

Association Studies ◽

Effective Sample Size ◽

Environment Interaction ◽

Uk Biobank ◽

Gene Environment Interaction ◽

Gene Environment ◽

The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

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Gene‐environment Interaction

The Wiley Encyclopedia of Personality and Individual Differences ◽

10.1002/9781119547143.ch37 ◽

2020 ◽

pp. 221-223

Author(s):

Karen Nolidin

Keyword(s):

Environment Interaction ◽

Gene Environment Interaction ◽

Gene Environment

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Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

Scientific Reports ◽

10.1038/s41598-021-86871-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chao-Yu Guo ◽

Reng-Hong Wang ◽

Hsin-Chou Yang

Keyword(s):

Complex Traits ◽

Association Studies ◽

Association Test ◽

Whole Genome Sequence ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Sequence Kernel Association Test ◽

Gene Environment ◽

Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

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