Apr 29, 2020

Efficient gene-environment interaction tests for large biobank-scale sequencing studies

BioRxiv : the Preprint Server for Biology
X. WangHan Chen

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 th...Continue Reading

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Mentioned in this Paper

Calcinus elegans
Cyartonema elegans
Coleonyx elegans
Cestrum elegans
Clarkia unguiculata
Clathrulina elegans
Cardioglossa elegans
Cymbella elegans
Cyrenella elegans
Chaetophora elegans

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