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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| Published in | Heredity Vol. 120; no. 4; pp. 356 - 368 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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England
Springer Nature B.V
01.04.2018
Springer International Publishing |
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| Abstract | 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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| AbstractList | 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. |
| Author | Sillanpää, Mikko J Mathew, Boby Léon, Jens |
| Author_xml | – sequence: 1 givenname: Boby orcidid: 0000-0001-8841-6446 surname: Mathew fullname: Mathew, Boby email: boby.mathew@hotmail.com organization: INRES Pflanzenzüchtung, University of Bonn, 53115, Bonn, Germany. boby.mathew@hotmail.com – sequence: 2 givenname: Jens surname: Léon fullname: Léon, Jens organization: INRES Pflanzenzüchtung, University of Bonn, 53115, Bonn, Germany – sequence: 3 givenname: Mikko J surname: Sillanpää fullname: Sillanpää, Mikko J organization: Department of Mathematical Sciences and Biocenter Oulu, University of Oulu, FIN-90014, Oulu, Finland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29238077$$D View this record in MEDLINE/PubMed |
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| Title | A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction |
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