Nat Commun

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%.

InsightGWAS is a Transformer-based model that enhances genetic discovery for migraine GWAS by integrating functional annotations and leveraging transfer learning from GWAS datasets of major depressive disorder. It identified 293 previously unreported loci from 53,109 cases and 230,876 controls. Published in Nature Communications.

Migraine is a complex neurological disorder with substantial heritability, yet genome-wide association studies (GWAS) have explained only a fraction of its genetic component. We developed InsightGWAS, a Transformer-based model, to enhance genetic discovery for migraine by integrating functional annotations and leveraging transfer learning from GWAS datasets of major depressive disorder (MDD), identifying 293 previously unreported loci.