Fast Kernel-based Association Testing of non-linear genetic effects for Biobank-scale data

Abstract

Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a powerful approach to test for nonlinear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a Kernel-based approach that can test for non-linear effects of a set of variants on a trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of individuals. We applied FastKAST to thirty quantitative traits measured across {approx} 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with nonlinear effects at genome-wide significance.