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Predicting pig digestibility coefficients with microbial and genomic data using machine learning prediction algorithms

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Abstract

Classical methods as genomic BLUP performs well for genomic prediction of polygenic trait, but does not consider interaction between genes or between genes and other information such as host genetic or microbial data. This study aims at comparing several methods including parametric and machine learning methods to predict digestive coefficient using genomic, microbial and both genomic and microbial information. Considering only microbial data led to the best prediction accuracies for digestive coefficients, whereas considering only genomic data performed worst. BLUP, RKHS and GSVM gave the best prediction accuracies except when combined genomic and microbial data was used. Combining microbial and genomic data did not improve prediction accuracies for all traits and methods considered in this study. Thus, considering microbial information is crucial to predict digestive efficiency and interactions between host genetic and faecal microbial information seem to be limited.
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Dates and versions

hal-03739243 , version 1 (27-07-2022)

Identifiers

  • HAL Id : hal-03739243 , version 1

Cite

Céline Carillier-Jacquin, Vanille Déru, Llibertat Tusell, Alban Bouquet, Laval Jacquin, et al.. Predicting pig digestibility coefficients with microbial and genomic data using machine learning prediction algorithms. WCGALP (World Congress on Genetics Applied to Livestock Production), Jul 2022, Rotterdam, Netherlands. ⟨hal-03739243⟩
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