Scalable Multi-component Linear Mixed Models with Application to SNP Heritability Estimation

摘要

A central question in human genetics is to find the proportion of variation in a trait that can be explained by genetic variation [1]. A number of methods have been developed to estimate this quantity, termed narrow-sense heritability, from genome-wide SNP data [2-6]. Recently, it has become clear that estimates of narrow-sense heritability are sensitive to modeling assumptions that relate the effect sizes of a SNP to its minor allele frequency (MAF) and linkage disequilibrium (LD) patterns [6, 7]. A principled approach to estimate heritability while accounting for variation in SNP effect sizes involves the application of linear Mixed Models (LMMs) [8] with multiple variance components where each variance component represents the fraction of genetic variance explained by SNPs that belong to a given range of MAF and LD values. Beyond their importance in accurately estimating genome-wide SNP heritability, multiple variance component LMMs are useful in partitioning the contribution of genomic annotations to trait heritability which, in turn, can provide insights into biological processes that are associated with the trait.