Components of the accuracy of genomic prediction in a multi-breed sheep population

Title
Components of the accuracy of genomic prediction in a multi-breed sheep population
Publication Date
2012
Author(s)
Daetwyler, H D
Kemper, K E
Van Der Werf, Julius H
( author )
OrcID: https://orcid.org/0000-0003-2512-1696
Email: jvanderw@une.edu.au
UNE Id une-id:jvanderw
Hayes, Ben J
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
American Society of Animal Science
Place of publication
United States of America
DOI
10.2527/jas.2011-4557
UNE publication id
une:12730
Abstract
In genome-wide association studies, failure to remove variation due to population structure results in spurious associations. In contrast, for predictions of future phenotypes or estimated breeding values from dense SNP data, exploiting population structure arising from relatedness can actually increase the accuracy of prediction in some cases, for example, when the selection candidates are offspring of the reference population where the prediction equation was derived. In populations with large effective population size or with multiple breeds and strains, it has not been demonstrated whether and when accounting for or removing variation due to population structure will affect the accuracy of genomic prediction. Our aim in this study was to determine whether accounting for population structure would increase the accuracy of genomic predictions, both within and across breeds. First, we have attempted to decompose the accuracy of genomic prediction into contributions from population structure or linkage disequilibrium (LD) between markers and QTL using a diverse multi-breed sheep ('Ovis aries') data set, genotyped for 48,640 SNP. We demonstrate that SNP from a single chromosome can achieve up to 86% of the accuracy for genomic predictions using all SNP. This result suggests that most of the prediction accuracy is due to population structure, because a single chromosome is expected to capture relationships but is unlikely to contain all QTL. We then explored principal component analysis (PCA) as an approach to disentangle the respective contributions of population structure and LD between SNP and QTL to the accuracy of genomic predictions. Results showed that fitting an increasing number of principle components (PC; as covariates) decreased within breed accuracy until a lower plateau was reached. We speculate that this plateau is a measure of the accuracy due to LD. In conclusion, a large proportion of the accuracy for genomic predictions in our data was due to variation associated with population structure. Surprisingly, accounting for this structure generally decreased the accuracy of across breed genomic predictions.
Link
Citation
Journal of Animal Science, 90(10), p. 3375-3384
ISSN
1525-3163
0021-8812
Start page
3375
End page
3384

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