Genetic dissection and genomic prediction for pork cuts and carcass morphology traits in pig.

J Anim Sci Biotechnol

State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China.

Published: September 2023

AI Article Synopsis

  • - The study aimed to identify quantitative trait loci (QTLs) related to pork cuts in pigs, using data from 2,012 pigs and examining 17 different pork cuts along with 12 morphology traits to enhance selection methods in the industry.
  • - Researchers discovered 14 QTLs through genome-wide association studies (GWAS), with specific genes (HMGA1, VRTN, BMP2) linked to traits like body length and bone structure, which influence the quality of various pork cuts.
  • - The accuracy of genomic estimated breeding values (GEBVs) for pork cuts varied, with certain cuts showing prediction accuracies above 0.600, demonstrating the potential for improving selection strategies in pig breeding through better genetic modeling

Article Abstract

Background: As pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts.

Results: We identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs.

Conclusions: We performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475202PMC
http://dx.doi.org/10.1186/s40104-023-00914-4DOI Listing

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