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

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...

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Bibliographic Details
Published inHeredity Vol. 120; no. 4; pp. 356 - 368
Main Authors Mathew, Boby, Léon, Jens, Sillanpää, Mikko J
Format Journal Article
LanguageEnglish
Published England Springer Nature B.V 01.04.2018
Springer International Publishing
Subjects
Computer simulation
Corn
Datasets
Gene polymorphism
Genetic linkage
Genetics
Genomes
Genotypes
Haplotypes
Heritability
Linkage disequilibrium
Mathematical analysis
Matrix methods
Mouse devices
Polymorphism
Single-nucleotide polymorphism
Statistical analysis
Tagging
Weighting
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Summary: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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ISSN:0018-067X
1365-2540
DOI:10.1038/s41437-017-0023-4