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Efficient gene–environment interaction tests for large biobank‐scale sequencing studies
Author(s) -
Wang Xinyu,
Lim Elise,
Liu ChingTi,
Sung Yun Ju,
Rao Dabeeru C.,
Morrison Alanna C.,
Boerwinkle Eric,
Manning Alisa K.,
Chen Han
Publication year - 2020
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22351
Subject(s) - biobank , covariate , genome wide association study , genetic association , aggregate (composite) , exome sequencing , computer science , genetics , computational biology , biology , statistics , gene , mutation , single nucleotide polymorphism , mathematics , machine learning , genotype , materials science , composite material
Abstract Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene–environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank‐scale sequencing studies with correlated samples. We propose efficient Mixed‐model Association tests for GEne–Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large‐scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.