Genomic Selection in Pigs: Precision Breeding and Trait Optimization

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Farm Animal Production".

Deadline for manuscript submissions: 10 June 2026 | Viewed by 1931

Special Issue Editors

College of Animal Science and Technology, Henan Agricultural University, Zhenghzou 450046, China
Interests: genomic prediction; selection accuracy; non-additive genetic effects; genomics mating; mating allocation
Special Issues, Collections and Topics in MDPI journals
College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: animal model; machine learning; deep learning; phenomics; estimated breeding value
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
Interests: genomic selection; genetic gain; molecular breeding; multi-omics; trait optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of genomic selection has revolutionized pig breeding by enabling more accurate and efficient selection of superior animals. As global demand for pork production increases, genomic selection, powered by high-throughput genotyping and advanced computational methods, offers unprecedented opportunities to enhance genetic improvement while addressing industry challenges such as feed efficiency, disease resistance, and meat quality. The integration of SNP arrays and whole-genome sequencing data has dramatically enhanced our ability to identify causal variants and unravel the genetic architecture of complex traits.

Combining genomic information with traditional breeding strategies has not only accelerated genetic gain but has also deepened our understanding of the biological mechanisms underlying economically important traits. The emergence of novel statistical methods and machine learning algorithms has enhanced our capability to handle large-scale genomic data and predict breeding values with higher accuracy. Additionally, the incorporation of non-additive genetic effects and multi-omics data—including transcriptomics, metabolomics, and microbiome information—has opened new avenues for elucidating genotype-to-phenotype relationships. These advances are particularly vital for complex traits where traditional selection methods have achieved limited progress, and they lay the foundation for precision breeding programs that can swiftly adapt to evolving market demands.

This topic aims to showcase the latest research and developments in genomic selection for pig breeding, with an emphasis on both theoretical innovation and practical application. It encompasses a broad spectrum of areas, including but not limited to the following:

  • Non-additive Genetic Effects and Gene Networks;
  • Multi-trait Genomic Selection Strategies;
  • Machine Learning Applications in Breeding Value Prediction;
  • Integration of Omics Data in Selection Programs;
  • Host-Microbiome Interactions and Their Role in Breeding;
  • Precision Phenotyping and High-throughput Data Collection;
  • Economic Implementation of Genomic Selection Programs;
  • Sustainable Breeding Goals and Genetic Resource Conservation.

Dr. Xiuling Li
Dr. Lilin Yin
Prof. Dr. Jie Yang
Guest Editors

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Keywords

  • genomic selection
  • genetic gain
  • animal model
  • molecular breeding
  • machine learning
  • phenomics
  • multi-omics
  • trait optimization
  • estimated breeding value
  • pigs

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Published Papers (1 paper)

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Research

14 pages, 2446 KB  
Article
Multi-Omics Annotation and Residual Split Strategy-Based Deep Learning Model for Efficient and Robust Genomic Prediction in Pigs
by Jingnan Ma, Zhenshuang Tang, Haohao Zhang, Yangfan Liu, Xiong Xiong, Xiaolei Liu, Lilin Yin and Minggang Lei
Agriculture 2025, 15(22), 2354; https://doi.org/10.3390/agriculture15222354 - 13 Nov 2025
Viewed by 1116
Abstract
Genomic selection has become a widely adopted and effective breeding technology for modern genetic improvements in pigs. However, the core model currently used in genetic evaluation is primarily based on a linear mixed model, which accounts for only additive genetic effects. Non-additive effects [...] Read more.
Genomic selection has become a widely adopted and effective breeding technology for modern genetic improvements in pigs. However, the core model currently used in genetic evaluation is primarily based on a linear mixed model, which accounts for only additive genetic effects. Non-additive effects and complex nonlinear interactions among genes or loci are often neglected, leaving substantial potential for improving the predictive ability of traits. To address this limitation, we here propose a Multi-omics Annotation and Residual Split strategy-based deep learning model (MARS). Through comprehensive comparisons and evaluations against various linear and nonlinear models across multiple pig traits, we demonstrate that the residual split indirect strategy effectively mitigates overfitting and underfitting issues commonly observed in deep learning models, thereby enhancing predictive accuracy for complex traits. Moreover, by incorporating multi-omics annotation information within a hierarchical feature selection procedure, our results show that it improves computational efficiency without significant sacrifices in prediction performance. It is foreseeable that our developed MARS would facilitate the application of artificial intelligence technology and the publicly available big omics data in the coming future of pig breeding. Full article
(This article belongs to the Special Issue Genomic Selection in Pigs: Precision Breeding and Trait Optimization)
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