Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits

  • Joon-Ki Hong (Swine Division, National Institute of Animal Science, Rural Development Administration) ;
  • Yong-Min Kim (Swine Division, National Institute of Animal Science, Rural Development Administration) ;
  • Eun-Seok Cho (Swine Division, National Institute of Animal Science, Rural Development Administration) ;
  • Jae-Bong Lee (Korea Zoonosis Research Institute, Jeonbuk National University) ;
  • Young-Sin Kim (Swine Division, National Institute of Animal Science, Rural Development Administration) ;
  • Hee-Bok Park (Department of Animal Resources Science, Kongju National University)
  • Received : 2023.07.14
  • Accepted : 2023.11.03
  • Published : 2024.04.01
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Abstract

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

Keywords

Acknowledgement

The authors sincerely thank Miguel Perez-Enciso, at Universitat Autonoma de Barcelona (UAB), for valuable comments on the manuscript.

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