University of Oulu

Mathew, B., Léon, J., Sillanpää, M. (2018) A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction. Heredity, 120 (4), 356-368. doi:10.1038/s41437-017-0023-4

A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction

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Author: Mathew, Boby1; Léon, Jens1; Sillanpää, Mikko J.2
Organizations: 1INRES Pflanzenzüchtung, University of Bonn, 53115, Bonn, Germany
2Department of Mathematical Sciences and Biocenter Oulu, University of Oulu, FIN-90014, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.1 MB)
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Language: English
Published: Springer Nature, 2018
Publish Date: 2018-04-16


Single nucleotide polymorphism (SNP)-heritability estimation is an important topic in several research fields, including animal, plant and human genetics, as well as in ecology. Linear mixed model estimation of SNP-heritability uses the structures of genomic relationships between individuals, which is constructed from genome-wide sets of SNP-markers that are generally weighted equally in their contributions. Proposed methods to handle dependence between SNPs include, “thinning” the marker set by linkage disequilibrium (LD)-pruning, the use of haplotype-tagging of SNPs, and LD-weighting of the SNP-contributions. For improved estimation, we propose a new conceptual framework for genomic relationship matrix, in which Mahalanobis distance-based LD-correction is used in a linear mixed model estimation of SNP-heritability. The superiority of the presented method is illustrated and compared to mixed-model analyses using a VanRaden genomic relationship matrix, a matrix used by GCTA and a matrix employing LD-weighting (as implemented in the LDAK software) in simulated (using real human, rice and cattle genotypes) and real (maize, rice and mice) datasets. Despite of the computational difficulties, our results suggest that by using the proposed method one can improve the accuracy of SNP-heritability estimates in datasets with high LD.

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Series: Heredity
ISSN: 0018-067X
ISSN-E: 1365-2540
ISSN-L: 0018-067X
Volume: 120
Issue: 4
Pages: 356 - 368
DOI: 10.1038/s41437-017-0023-4
Type of Publication: A1 Journal article – refereed
Field of Science: 1184 Genetics, developmental biology, physiology
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