Evaluating the Efficiency of Phenomic Selection in Dairy Cattle

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Odah, E.O. | Sanchez, M.P. | Cuyabano, B. | Croiseau, P.

Edité par CCSD -

International audience. Phenomic selection is a relatively new, high-throughput method that uses spectra information from biological samples for phenotype prediction and selection. This study proposes a large-scale application of phenomic selection in dairy cattle using mid-infrared (MIR) spectra, routinely produced from milk samples, to predict milk composition. The objective is to compare the accuracy of the predictions obtained from phenomic evaluation (hyperspectral BLUP, or HBLUP) with those obtained with genomic evaluation (GBLUP). 36,986 milk MIR spectra records were available from three French dairy cattle breeds: Holstein (n = 2,330), Montbéliarde (n = 1,726), and Normande (n = 2,805), to predict eleven traits related to milk production (milk, protein, and fat yields, protein and fat contents), udder health (somatic cell count and clinical mastitis), fertility (heifer and cow conception rates, and calving to artificial insemination interval), and height at sacrum. All traits were evaluated using their yield deviations. The estimated heritabilities of absorbances along the wavelengths of the MIR spectra varied from 0 to 0.5, indicating the potential of using MIR spectra for genetic valuation. Although the accuracy of the HBLUP predictions is not as good as that obtained with a GBLUP, on average, the HBLUP achieved up to 89% of the GBLUP prediction accuracy for functional traits, 73% for production traits and 42% for type traits. This result suggests that phenomic selection can be a low-cost alternative to the use of genomic information for breeding value estimation in dairy cattle. The next ongoing step of our study is to combine genomic and MIR spectra information to predict breeding values.

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