Covariate Adjustment

DeepNull is a method that identifies and adjusts for non-linear and interactive covariate effects in GWAS using a deep neural network. It maintains tight control of type I error while increasing statistical power by up to 20% in the presence of non-linear covariate effects. Published in Nature Communications.

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, maintaining tight control of the type I error while increasing statistical power by up to 20%.