Improved Heritability Estimation from Genome-wide SNPs

Doug Speed,Gibran Hemani,Michael R Johnson,David J Balding

doi:10.1016/j.ajhg.2012.10.010

Abstract

Estimation of narrow-sense heritability, h(2), from genome-wide SNPs genotyped in unrelated individuals has recently attracted interest and offers several advantages over traditional pedigree-based methods. With the use of this approach, it has been estimated that over half the heritability of human height can be attributed to the ~300,000 SNPs on a genome-wide genotyping array. In comparison, only 5%-10% can be explained by SNPs reaching genome-wide significance. We investigated via simulation the validity of several key assumptions underpinning the mixed-model analysis used in SNP-based h(2) estimation. Although we found that the method is reasonably robust to violations of four key assumptions, it can be highly sensitive to uneven linkage disequilibrium (LD) between SNPs: contributions to h(2) are overestimated from causal variants in regions of high LD and are underestimated in regions of low LD. The overall direction of the bias can be up or down depending on the genetic architecture of the trait, but it can be substantial in realistic scenarios. We propose a modified kinship matrix in which SNPs are weighted according to local LD. We show that this correction greatly reduces the bias and increases the precision of h(2) estimates. We demonstrate the impact of our method on the first seven diseases studied by the Wellcome Trust Case Control Consortium. Our LD adjustment revises downward the h(2) estimate for immune-related diseases, as expected because of high LD in the major-histocompatibility region, but increases it for some nonimmune diseases. To calculate our revised kinship matrix, we developed LDAK, software for computing LD-adjusted kinships.

Highlights

The linear mixed model, long a mainstay of heritability estimation,[1,2,3] fits a covariance structure specified by a matrix of kinship coefficients to a vector of measured phenotypes
The American Journal of Human Genetics 91, 1011–1021, December 7, 2012 1011 any causal-variation component that is not tagged by a genotyped SNP, but we show that even when all causal variants are genotyped and fully tagged, SNP-based hb[2] reflects patterns of linkage disequilibrium (LD), in addition to causal variation
We have examined the assumptions that underlie the use of SNP-based mixed-model analysis for estimating h2, and we have illustrated through simulation that the resulting hb[2] is reasonably robust to four underpinning assumptions but is vulnerable to uneven LD

Summary

Introduction

The linear mixed model, long a mainstay of heritability estimation,[1,2,3] fits a covariance structure specified by a matrix of kinship coefficients to a vector of measured phenotypes. Mixed-model analysis in quantitative genetics was developed by animal breeders decades ago, but the advent of dense genome-wide SNP data has radically enhanced its applicability, for example, to the use of apparently unrelated individuals. This might seem counterintuitive given that relatedness is central to heritability, but the key insight made by Yang et al.[5] is that dense genotype data permit the exploitation of small differences in the proportions of genome shared among apparently unrelated individuals, and this has advantages over the traditional pedigree-based approaches. Investigating the genetic architecture of complex traits becomes possible through the estimation of h2 components tagged by different SNP sets, which one can obtain, for example, by imposing minor-allele-frequency (MAF) thresholds or restricting attention to specific pathways or genomic regions (termed ‘‘genomic partitioning’’).[6]

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Conclusion