Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental...

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Bibliographic Details
Published in:PLoS genetics 2016-03, Vol.12 (3), p.e1005849-e1005849
Main Authors: Sul, Jae Hoon, Bilow, Michael, Yang, Wen-Yun, Kostem, Emrah, Furlotte, Nick, He, Dan, Eskin, Eleazar
Format: Article
Language:English
Subjects:
Bias
Biology and Life Sciences
Computer Simulation
Datasets
Gene-Environment Interaction
Genes
Genetics, Population
Genome, Human
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Genotype & phenotype
Humans
Methods
Models, Genetic
Observations
Phenotype
Phenotypes
Physical Sciences
Polymorphism, Single Nucleotide - genetics
Population
Principal components analysis
Research and Analysis Methods
Simulation
Studies
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Summary:Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1005849