Application of linear and machine learning models to genomic prediction of fatty acid composition in Japanese Black cattle.

We collected 3180 records of oleic acid (C18:1) and monounsaturated fatty acid (MUFA) measured using gas chromatography (GC) and 6960 records of C18:1 and MUFA measured using near-infrared spectroscopy (NIRS) in intermuscular fat samples of Japanese Black cattle. We compared genomic prediction performance for four linear models (genomic best linear unbiased prediction [GBLUP], kinship-adjusted multiple loci [KAML], BayesC, and BayesLASSO) and five machine learning models (Gaussian kernel [GK], deep kernel [DK], random forest [RF], extreme gradient boost [XGB], and convolutional neural network [CNN]). For GC-based C18:1 and MUFA, KAML showed the highest accuracies, followed by BayesC, XGB, DK, GK, and BayesLASSO, with more than 6% gain of accuracy by KAML over GBLUP. Meanwhile, DK had the highest prediction accuracy for NIRS-based C18:1 and MUFA, but the difference in accuracies between DK and KAML was slight. For all traits, accuracies of RF and CNN were lower than those of GBLUP. The KAML extends GBLUP methods, of which marker effects are weighted, and involves only additive genetic effects; whereas machine learning methods capture non-additive genetic effects. Thus, KAML is the most suitable method for breeding of fatty acid composition in Japanese Black cattle.

Identification of the core rumen bacterial taxa and their population dynamics during the fattening period in Japanese Black cattle.

The rumen microbiota comprises a vast range of bacterial taxa, which may affect the production of high-quality meat in Japanese Black cattle. The aim of this study was to identify core rumen microbiota in rumen fluid samples collected from 74 Japanese Black cattle raised under different dietary conditions using 16S rRNA gene amplicon sequencing. In the rumen of fattening Japanese Black cattle, 10 bacterial taxa, showing >1% average relative abundance and >95% prevalence, irrespective of the dietary conditions and the fattening periods, were identified as the core rumen bacterial taxa, which accounted for approximately 80% of the rumen microbiota in Japanese Black cattle. Additionally, population dynamics of the core rumen bacterial taxa revealed two distinct patterns: Prevotella spp. and unclassified Bacteroidales decreased in the mid-fattening period, whereas unclassified Clostridiales, unclassified Ruminococcaceae, Ruminococcus spp., and unclassified Christensenellaceae increased during the same period. Therefore, the present study reports the wide distribution of the core rumen bacterial taxa in Japanese Black cattle, and the complementary nature of the population dynamics of these core taxa, which may ensure stable rumen fermentation during the fattening period.