Genomic prediction of reproduction traits for Merino sheep

Bolormaa, S., Brown, D. J., Swan, A. A., van der Werf, J. H. J., Hayes, B. J. and Daetwyler, H. D. (2017) Genomic prediction of reproduction traits for Merino sheep. Animal Genetics, 48 3: 338-348. doi:10.1111/age.12541

Author Bolormaa, S.
Brown, D. J.
Swan, A. A.
van der Werf, J. H. J.
Hayes, B. J.
Daetwyler, H. D.
Title Genomic prediction of reproduction traits for Merino sheep
Journal name Animal Genetics   Check publisher's open access policy
ISSN 0268-9146
Publication date 2017-06-01
Sub-type Article (original research)
DOI 10.1111/age.12541
Open Access Status Not yet assessed
Volume 48
Issue 3
Start page 338
End page 348
Total pages 11
Place of publication Chichester, West Sussex, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Abstract Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17–0.61) were higher than were those from sire family cross-validations (range 0.00–0.51). The GEBV accuracies of 0.41–0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.
Keyword Genomic prediction accuracy
Litter size
Number of lambs born
Number of lambs weaned
Single nucleotide polymorphisms
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
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Created: Wed, 18 Oct 2017, 10:50:29 EST by Melissa Rowan on behalf of Learning and Research Services (UQ Library)