RT Journal Article SR Electronic T1 Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture JF bioRxiv FD Cold Spring Harbor Laboratory SP 526855 DO 10.1101/526855 A1 Kangcheng Hou A1 Kathryn S. Burch A1 Arunabha Majumdar A1 Huwenbo Shi A1 Nicholas Mancuso A1 Yue Wu A1 Sriram Sankararaman A1 Bogdan Pasaniuc YR 2019 UL http://biorxiv.org/content/early/2019/01/23/526855.abstract AB The proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-heritability reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.