Abstract
BackgroundGenomic 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.MethodsGenotypes 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.ResultsFor 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.ConclusionsThe 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.
Highlights
Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals
Genomic estimated breeding values (GEBV) can be calculated for both sexes at an early stage in life, and GS can increase the profitability and accelerate genetic gain of dairy cattle breeding by reducing the generation interval and cost of proving bulls [11,12]
Comparison of methods The choice of methods evaluated here represent a range of methods proposed previously for the potential use in genomic selection including variable selection methods (FR-LS, [10,13,47], shrinkage methods (Bayes-R and best linear unbiased prediction (BLUP), [10,13,14]); support vector learning methods (SVR, [15,43]) and dimension reduction methods (PLSR, [17,18])
Summary
Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. A dramatic change in terms of the use of genomic information to estimate the total genetic value for breeding animals, known as genomic selection (GS) or Genome Wide Selection (GWS) was predicted by Meuwissen et al [10] Using simulations, they showed that with a dense marker map covering all the chromosomes, it is possible to accurately estimate the breeding value of animals without information about their phenotype or that of close relatives. Genomic estimated breeding values (GEBV) can be calculated for both sexes at an early stage in life, and GS can increase the profitability and accelerate genetic gain of dairy cattle breeding by reducing the generation interval and cost of proving bulls [11,12]. This is projected to restructure dairy cattle breeding schemes, many of which rely on progeny testing sires and the recording of hundreds of thousands and often millions of cows [12]
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