RÉSUMÉ
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.
RÉSUMÉ
Meat quality comprises a set of key traits such as pH, meat color, water-holding capacity, tenderness and marbling. These traits are complex because they are affected by multiple genetic and environmental factors. The aim of this study was to investigate the molecular genetic basis underlying nine meat quality-related traits in a Yorkshire pig population using a genome-wide association study (GWAS) and subsequent biological pathway analysis. In total, 45,926 single nucleotide polymorphism (SNP) markers from 543 pigs were selected for the GWAS after quality control. Data were analyzed using a genome-wide efficient mixed model association (GEMMA) method. This linear mixed model-based approach identified two quantitative trait loci (QTLs) for meat color (b*) on chromosome 2 (SSC2) and one QTL for shear force on chromosome 8 (SSC8). These QTLs acted additively on the two phenotypes and explained 3.92%-4.57% of the phenotypic variance of the traits of interest. The genes encoding HAUS8 on SSC2 and an lncRNA on SSC8 were identified as positional candidate genes for these QTLs. The results of the biological pathway analysis revealed that positional candidate genes for meat color (b*) were enriched in pathways related to muscle development, muscle growth, intramuscular adipocyte differentiation, and lipid accumulation in muscle, whereas positional candidate genes for shear force were overrepresented in pathways related to cell growth, cell differentiation, and fatty acids synthesis. Further verification of these identified SNPs and genes in other independent populations could provide valuable information for understanding the variations in pork quality-related traits.