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Animal genetics
Feb, 2025;56 (1): e70001.

Integrating QTL and expression QTL of PigGTEx to improve the accuracy of genomic prediction for small population in Yorkshire pigs.

Haoran Shi, He Geng, Bin Yang, Zongjun Yin, Yang Liu
Author Information
  1. Haoran Shi: Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China. ORCID
  2. He Geng: Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China.
  3. Bin Yang: Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China.
  4. Zongjun Yin: College of Animal Science and Technology, Anhui Agricultural University, Hefei, China.
  5. Yang Liu: Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China. ORCID
PMID: 39916449 DOI: 10.1111/age.70001

Abstract

The size of the reference population and sufficient phenotypic records are crucial for the accuracy of genomic selection. However, for small-to-medium-sized pig farms or breeds with limited population sizes, conducting genomic breeding programs presents significant challenges. In this study, 2295 Yorkshire pigs were selected from three distinct regions, including 1500 from an American line, 500 from a Canadian line, and 295 from a Danish line. All populations were genotyped using the GeneSeek 50K GGP Porcine HD chip. To enhance genomic selection accuracy, we proposed strategies that combined multiple populations and leveraged multi-omics prior information. Cis-QTL from the PigGTEx database and QTL identified through genome-wide association studies were incorporated into the genomic feature best linear unbiased prediction (GFBLUP) model to predict the ADG100 and the BF100 traits. Results demonstrated that combining multiple populations effectively improved prediction accuracy for small population, accuracy for ADG100 increased by an average of 0.29 and accuracy for BF100 by 0.05. The GFBLUP model, which integrates biological priors, showed some improvements in prediction accuracy for the BF100 trait. Specifically, for the small population, accuracy increased by 0.09 in Scheme 1, where each population size was predicted independently. In Scheme 3, where the large population was used as a reference group to predict the small population, accuracy increased by 0.03. However, the GFBLUP model did not provide additional benefits in predicting the ADG100 trait. These findings offer effective strategies for genetic improvement in developing regions and highlight the potential of multi-omics integration to enhance prediction models.

Keywords

PigGTEx QTL genomic feature best linear unbiased prediction genomic selection

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Grants

  1. 340000211260001000431/the Joint Research Project of Excellent Livestock Breeds in Anhui Province
  2. JBGS(2021)026/the Project of the Open Competition Mechanism to Select the Best for Revitalizing Seed Industry in Jiangsu Province
  3. 2023ZD0404401-03/the Biological Breeding-National Science and Technology Major Projects

MeSH Term

Animals
Quantitative Trait Loci
Sus scrofa
Breeding
Genomics
Genotype
Phenotype
Models, Genetic
Journal Article

Word Cloud

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