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 ten CPU hours and 5Gb 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 approximately ten 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 the 40 quantitative UK Biobank phenotypes (average sample size 349k), LDAK-KVIK finds 16% more independent, genome-wide 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. Lastly, LDAK-KVIK produces state-of-the-art polygenic scores.