DeepNull models non-linear covariate effects to improve phenotypic prediction and association power

Zachary R. McCaw, Thomas Colthurst, Taedong Yun, Nicholas A. Furlotte, Andrew Carroll, Babak Alipanahi, Cory Y. McLean () and Farhad Hormozdiari ()
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Zachary R. McCaw: Google Health
Thomas Colthurst: Google Health
Taedong Yun: Google Health
Nicholas A. Furlotte: Google Health
Andrew Carroll: Google Health
Babak Alipanahi: Google Health
Cory Y. McLean: Google Health
Farhad Hormozdiari: Google Health

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

Date: 2022
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DOI: 10.1038/s41467-021-27930-0

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