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Improved heritability estimation from genome-wide SNPs.

Speed, D; Hemani, G; Johnson, MR; Balding, DJ; (2012) Improved heritability estimation from genome-wide SNPs. The American Journal of Human Genetics , 91 (6) 1011 - 1021. 10.1016/j.ajhg.2012.10.010. Green open access

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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.

Type: Article
Title: Improved heritability estimation from genome-wide SNPs.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ajhg.2012.10.010
Publisher version: http://dx.doi.org/10.1016/j.ajhg.2012.10.010
Language: English
Additional information: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMCID: PMC3516604
Keywords: Algorithms, Computer Simulation, Genome, Human, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Models, Genetic, Multifactorial Inheritance, Pedigree, Polymorphism, Single Nucleotide
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
URI: https://discovery.ucl.ac.uk/id/eprint/1361783
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