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

Daetwyler, H. D., Kemper, K. E., van der Werf, J. H. J. and Hayes, B. J. (2012) Components of the accuracy of genomic prediction in a multi-breed sheep population. Journal of Animal Science, 90 10: 3375-3384. doi:10.2527/jas.2011-4557

Author Daetwyler, H. D.
Kemper, K. E.
van der Werf, J. H. J.
Hayes, B. J.
Title Components of the accuracy of genomic prediction in a multi-breed sheep population
Journal name Journal of Animal Science   Check publisher's open access policy
ISSN 0021-8812
Publication date 2012-03
Year available 2012
Sub-type Article (original research)
DOI 10.2527/jas.2011-4557
Open Access Status Not Open Access
Volume 90
Issue 10
Start page 3375
End page 3384
Total pages 10
Place of publication Champaign, IL, United States
Publisher American Society of Animal Science
Language eng
Formatted abstract
In genome-wide association studies, failure to remove variation due to population structure results in spurious associations. In contrast, for predic-tions of future phenotypes or estimated breeding values from dense SNP data, exploiting population structure arising from relatedness can actually increase the accu-racy of prediction in some cases, for example, when the selection candidates are offspring of the reference popu-lation 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 deter-mine whether accounting for population structure would increase the accuracy of genomic predictions, both with-in and across breeds. First, we have attempted to decom-pose the accuracy of genomic prediction into contribu-tions from population structure or linkage disequilib-rium (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 contribu-tions 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 specu-late 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 varia-tion associated with population structure. Surprisingly, accounting for this structure generally decreased the accuracy of across breed genomic predictions.
Keyword Genomic prediction
Genomic selection
Population structure
Principal component analysis
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Agriculture and Food Sciences
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Citation counts: TR Web of Science Citation Count  Cited 44 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 46 times in Scopus Article | Citations
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