Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCπ methods for genomic selection in French Holstein and Montbéliarde breeds

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Colombani, Carine, Colombani | Legarra, Andres | Fritz, Sebastien, S. | Guillaume, François, F. | Croiseau, Pascal | Ducrocq, Vincent | Robert-Granié, Christèle

Edité par CCSD ; American Dairy Science Association -

Recently, the amount of available single nucleotide polymorphism (SNP) marker data has considerably increased in dairy cattle breeds, both for research purposes and for application in commercial breeding and selection programs. Bayesian methods are currently used in the genomic evaluation of dairy cattle to handle very large sets of explanatory variables with a limited number of observations. In this study, we applied 2 Bayesian methods, BayesCπ and Bayesian least absolute shrinkage and selection operator (LASSO), to 2 genotyped and phenotyped reference populations consisting of 3,940 Holstein bulls and 1,172 Montbeliarde bulls with approximately 40,000 polymorphic SNP. We compared the accuracy of the Bayesian methods for the prediction of 3 traits (milk yield, fat content, and conception rate) with pedigree-based BLUP, genomic BLUP, partial least squares (PLS) regression, and sparse PLS regression, a variable selection PLS variant. The results showed that the correlations between observed and predicted phenotypes were similar in BayesCπ (including or not pedigree information) and Bayesian LASSO for most of the traits and whatever the breed. In the Holstein breed, Bayesian methods led to higher correlations than other approaches for fat content and were similar to genomic BLUP for milk yield and to genomic BLUP and PLS regression for the conception rate. In the Montbeliarde breed, no method dominated the others, except BayesCπ for fat content. The better performances of the Bayesian methods for fat content in Holstein and Montbeliarde breeds are probably due to the effect of the DGAT1 gene. The SNP identified by the BayesCπ, Bayesian LASSO, and sparse PLS regression methods, based on their effect on the different traits of interest, were located at almost the same position on the genome. As the Bayesian methods resulted in regressions of direct genomic values on daughter trait deviations closer to 1 than for the other methods tested in this study, Bayesian methods are suggested for genomic evaluations of French dairy cattle.

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