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.
Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)
Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges
EditorsR.F. Veerkamp and Y. de Haas
Published: 2022 Pages: 3364
eISBN: 978-90-8686-940-4
Book Type: Conference Proceedings
403. Predicting pig digestibility coefficients with microbial and genomic data using machine learning prediction algorithms
C. Carillier-Jacquin Related information
1GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France.
*Corresponding author: celine. carillier-jacquin@inrae. fr
, V. Deru Related information*Corresponding author: celine.
1GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France.
2France Génétique Porc, 35651 Le Rheu, France.
, L. Tusell Related information2France Génétique Porc, 35651 Le Rheu, France.
3Animal Breeding and Genetics Program, Institute of Agriculture and Food Research and Technology (IRTA), Barcelona, Spain.
, A. Bouquet Related information2France Génétique Porc, 35651 Le Rheu, France.
4IFIP-Institut du Porc, 35650 Le Rheu, France.
, L. Jacquin Related information4IFIP-Institut du Porc, 35650 Le Rheu, France.
5CIRAD, BIOS, UMR AGAP, Montpellier, France.
, H. Gilbert Related information1GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France.
Pages: 1680 - 1683
Published Online: February 09, 2023
Abstract: