Improving genomic prediction in pigs by integrating multi-population data and prior knowledge
文献类型： 外文期刊
作者： Wang, Junliang 1 ; Lu, Yujin 1 ; Zhang, Wenjing 1 ; Cai, Xiaodian 1 ; Xie, Shuihua 3 ; Gao, Yahui 1 ; Li, Jiaqi 1 ; Lin, Changguang 4 ;
作者机构： 1.South China Agr Univ, Natl Engn Res Ctr Breeding Swine Ind,Coll Anim Sci, State Key Lab Swine & Poultry Breeding Ind, Guangdong Prov Key Lab Agroanim Genom & Mol Breedi, Guangzhou 510642, Peoples R China
2.Guangdong iPig Technol Co Ltd, Guangzhou 510470, Peoples R China
3.Agr Technol Extens Ctr Guangdong Prov, Guangzhou 510520, Peoples R China
4.Fujian Acad Agr Sci, Inst Anim Husb & Vet Med, Fuzhou 350013, Peoples R China
5.Fujian Guanghua Best Ecol Agr & Anim Husb Dev Co L, Youxi 365106, Fujian, Peoples R China
关键词： Genomic selection; Yorkshire pig; Joint evaluation; Prior biological knowledge; Reference population
期刊名称：BMC GENOMICS （ 影响因子：3.7； 五年影响因子：4.2 ）
ISSN： 1471-2164
年卷期： 2025 年 26 卷 1 期
页码：
收录情况： SCI
摘要： Genomic selection (GS) has become an essential tool for improving economically important traits in pigs. However, its accuracy depends heavily on the size and composition of the reference population. This study explores strategies for optimizing multi-population genomic evaluations by integrating prior biological knowledge and leveraging advanced genomic models. We assessed population similarities based on phenotypic distribution, linkage disequilibrium (LD) consistency, heritability, and genetic variance. Three genomic prediction models-GBLUP, bivariate GBLUP, and GFBLUP-were applied to evaluate the joint reference populations. The results indicated that differences in phenotypic means and genetic variance between populations significantly affected the prediction accuracy of joint evaluations, particularly for fat thickness traits. The GFBLUP model, integrating meta-GWAS priors, improved prediction accuracy when the genetic contributions were similar between target and reference populations. These findings highlight the importance of carefully selecting reference populations and integrating biological priors into genomic evaluations. The study offers valuable insights for optimizing genomic selection strategies in pig breeding programs.
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