Novel Alzheimer’s disease genes and epistasis identified using machine learning GWAS platform

Abstract

AbstractAlzheimer’s disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this ‘missing heritability’, however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning (ML) approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci,APOE, and identify two novel genome-wide significant AD associated loci in both cohorts,SH3BP4andSASH1, which are also in significant epistatic interactions withAPOE. We show that theSH3BP4SNP has a modulating effect on the known pathogenicAPOESNP, demonstrating a possible protective mechanism against AD.SASH1is involved in a triplet interaction with pathogenicAPOESNP andACOT11,where theSASH1SNP lowered the pathogenic interaction effect betweenACOT11andAPOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.