LDAK-KVIK performs fast and powerful mixed-model association analysis of quantitative and binary phenotypes

Abstract

Mixed-model association analysis (MMAA) is the preferred tool for performing a genome-wide association study, because it enables robust control of type 1 error and increased statistical power to detect trait-associated loci. However, existing MMAA tools often suffer from long runtimes and high memory requirements. We present LDAK-KVIK, a novel MMAA tool for analyzing quantitative and binary phenotypes. Using simulated phenotypes, we show that LDAK-KVIK produces well-calibrated test statistics, both for homogeneous and heterogeneous datasets. LDAK-KVIK is computationally-efficient, requiring less than 20 CPU hours and 8Gb memory to analyse genome-wide data for 350k individuals. These demands are similar to those of REGENIE, one of the most efficient existing MMAA tools, and up to 30 times less than those of BOLT-LMM, currently the most powerful MMAA tool. When applied to real phenotypes, LDAK-KVIK has the highest power of all tools considered. For example, across 40 quantitative phenotypes from the UK Biobank (average sample size 349k), LDAK-KVIK finds 16% more significant loci than classical linear regression, whereas BOLT-LMM and REGENIE find 15% and 11% more, respectively. LDAK-KVIK can also perform gene-based tests; across the 40 quantitative UK Biobank phenotypes, LDAK-KVIK finds 18% more significant genes than the leading existing tool.