A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers

Moser, Gerhard, Tier, Bruce, Crump, Ron E., Khatkar, Mehar S. and Raadsma, Herman W. (2009) A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genetics Selection Evolution, 41 1: 56.1-56.16. doi:10.1186/1297-9686-41-56


Author Moser, Gerhard
Tier, Bruce
Crump, Ron E.
Khatkar, Mehar S.
Raadsma, Herman W.
Title A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 0999-193X
1297-9686
Publication date 2009-12
Sub-type Article (original research)
DOI 10.1186/1297-9686-41-56
Open Access Status DOI
Volume 41
Issue 1
Start page 56.1
End page 56.16
Total pages 16
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle.

Methods: Genotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls.

Results: For both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy. All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time.

Conclusions: The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended.
Keyword Regression methods
Bias
Molecular breeding values
Genetic value
Dairy
Cattle
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Diamantina Institute - Open Access Collection
UQ Diamantina Institute Publications
 
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