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 perfo...

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Bibliographic Details
Published in:Animal science journal 2023-01, Vol.94 (1), p.e13883-n/a
Main Authors: Nishio, Motohide, Inoue, Keiichi, Arakawa, Aisaku, Ichinoseki, Kasumi, Kobayashi, Eiji, Okamura, Toshihiro, Fukuzawa, Yo, Ogawa, Shinichiro, Taniguchi, Masaaki, Oe, Mika, Takeda, Masayuki, Kamata, Takehiro, Konno, Masaru, Takagi, Michihiro, Sekiya, Mario, Matsuzawa, Tamotsu, Inoue, Yoshinobu, Watanabe, Akihiro, Kobayashi, Hiroshi, Shibata, Eri, Ohtani, Akihumi, Yazaki, Ryu, Nakashima, Ryotaro, Ishii, Kazuo
Format: Article
Language:English
Subjects:
Artificial neural networks
Cattle
Composition
Fatty acid composition
Fatty acids
Gas chromatography
Genetic effects
genomic prediction
Genomics
Infrared spectroscopy
Japanese Black cattle
Kernels
Learning algorithms
Machine learning
Near infrared radiation
Neural networks
Oleic acid
Predictions
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Summary: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.
ISSN:1344-3941
1740-0929
DOI:10.1111/asj.13883