Genomic Analysis of Reproductive Trait Divergence in Duroc and Yorkshire Pigs: A Comparison of Mixed Models and Selective Sweep Detection

杜洛克猪和约克夏猪繁殖性状分化的基因组分析:混合模型与选择性清除检测的比较

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Abstract

This study aimed to investigate population genetic differences related to reproductive traits between Duroc and Yorkshire (Dutch Large White) pigs using two approaches: linear mixed models that dissect additive and dominant effects, and selective sweep analysis. (1) Methods: Genome-wide single-nucleotide polymorphism (SNP) data of 3917 Duroc and 3217 Yorkshire pigs were analyzed. The first principal component (PC1) was used as a simulated phenotype to capture population-level variance. Additive and dominant genetic effects were partitioned and evaluated by using the combination of the linear mixed models (LMM) and ADDO's algorithm (LMM + ADDO). In parallel, selective sweep signals were detected using fixation index (F(ST)) and nucleotide diversity (θ(π)) analyses. A comparative assessment was then conducted between the LMM + ADDO and the selective sweep analysis results. Significant loci were annotated using quantitative trait loci (QTL) databases and the Ensembl genome browser. (2) Results: There are 39040 SNPs retained after quality control. Using the LMM + ADDO framework with PC1 as a simulated phenotype, a total of 632 significant SNPs and 184 candidate genes were identified. Notably, 587 SNPs and 171 genes were uniquely detected by the LMM + ADDO method and not among loci detected by the top 5% of F(ST) and θ(π) values. Key candidate genes associated with litter size included HSPG2, KAT6B, SAMD8, and LRMDA, while DLGAP1, MYOM1, and VTI1A were associated with teat number traits. (3) Conclusions: This study demonstrates the power of integrating additive and dominant effect modeling with population genetics approaches for the detection of genomic regions under selection. The findings provide novel insights into the genetic architecture of reproductive traits in pigs and have practical implications for understanding the inheritance of complex traits.

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