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Whole‐genome prediction of fatty acid composition in meat of Japanese Black cattle
Author(s) -
Onogi A.,
Ogino A.,
Komatsu T.,
Shoji N.,
Shimizu K.,
Kurogi K.,
Yasumori T.,
Togashi K.,
Iwata H.
Publication year - 2015
Publication title -
animal genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 81
eISSN - 1365-2052
pISSN - 0268-9146
DOI - 10.1111/age.12300
Subject(s) - best linear unbiased prediction , biology , beef cattle , selection (genetic algorithm) , marbled meat , intramuscular fat , genetics , zoology , artificial intelligence , computer science
Summary Because fatty acid composition influences the flavor and texture of meat, controlling it is particularly important for cattle breeds such as the Japanese Black, characterized by high meat quality. We evaluated the predictive ability of single‐step genomic best linear unbiased prediction (ss GBLUP ) in fatty acid composition of Japanese Black cattle by assessing the composition of seven fatty acids in 3088 cattle, of which 952 had genome‐wide marker genotypes. All sires of the genotyped animals were genotyped, but their dams were not. Cross‐validation was conducted for the 952 animals. The prediction accuracy was higher with ss GBLUP than with best linear unbiased prediction ( BLUP ) for all traits, and in an empirical investigation, the gain in accuracy of using ss GBLUP over BLUP increased as the deviations in phenotypic values of the animals increased. In addition, the superior accuracy of ss GBLUP tended to be more evident in animals whose maternal grandsire was genotyped than in other animals, although the effect was small.