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.

Broad-range detection of arboviruses belonging to Simbu serogroup lineage 1 and specific detection of Akabane, Aino and Peaton viruses by newly developed multiple TaqMan assays.

TaqMan assays were developed for the broad-range detection of arboviruses belonging to Simbu serogroup lineage 1 in the genus Orthobunyavirus and also for the specific detection of three viruses in the lineage, Akabane, Aino and Peaton viruses (AKAV, AINOV and PEAV, respectively). A primer and probe set was designed for the broad-range detection of Simbu serogroup lineage 1 (Pan-Simbu1 set) mainly targeting AKAV, AINOV, PEAV, Sathuperi and Shamonda viruses (SATV and SHAV), and the forward and reverse primers of the Pan-Simbu1 set were also used for the specific detection of AKAV with another probe (AKAV-specific set). In addition, two more primer and probe sets were designed for AINOV- and PEAV-specific detection, respectively (AINOV- and PEAV-specific sets). All of the four primer and probe sets successfully detected targeted viruses, and thus broad-range and specific detection of all the targeted viruses can be achieved by using two multiplex assays and a single assay in a dual (two-color) assay format when another primer and probe set for a bovine ß-actin control is also used. The assays had an analytical sensitivity of 10 copies/tube for AKAV, at least 100 copies/tube for AINOV, 100 copies/tube for PEAV, one copy/tube for SATV and at least 10 copies/tube for SHAV, respectively. Diagnostic sensitivity of the assays was tested with field-collected bovine samples, and the results suggested that the sensitivity was higher than that of a conventional RT-PCR. These data indicate that the newly developed TaqMan assays will be useful tools for the diagnosis and screening of field-collected samples for infections of AKAV and several other arboviruses belonging to the Simbu serogroup lineage 1.