Fine-Mapping-Based Variant Prioritization and Genomic Prediction Enhance Genetic Analyses of Teat Traits in Pigs

Area of Science:

• Animal Genetics
• Genomic Prediction
• Quantitative Trait Loci (QTL) Discovery

Background:

• Identifying causal genetic variants for complex traits is crucial for animal breeding.
• Genome-wide association studies (GWAS) are widely used but can be limited by linkage disequilibrium (LD) and marginal effect assumptions.
• Accurate variant and gene prioritization is essential for understanding genetic architectures.

Purpose of the Study:

• To develop and compare SNP prioritization strategies (GWAS-based vs. fine-mapping-based) within a unified framework.
• To improve the selection of informative variants and candidate genes for pig teat-related traits.
• To explicitly model LD structure and genetic architectures for enhanced accuracy.

Main Methods:

• Proposed a unified framework for SNP prioritization using both GWAS and fine-mapping approaches.
• Applied strategies to three pig teat-related traits: total teat number, teat symmetry, and teat adequacy.
• Explicitly modeled linkage disequilibrium (LD) structure and genetic architectures.

Main Results:

• Fine-mapping substantially improved joint explanatory performance and prediction accuracy compared to GWAS prioritization.
• For total teat number, fine-mapping achieved a mean PCC of 0.6599 vs. 0.3755 for GWAS.
• For teat adequacy, fine-mapping increased the mean AUC from 0.7012 to 0.8547.
• Fine-mapping identified more coherent, trait-specific biological pathways and candidate genes.

Conclusions:

• Fine-mapping provides a more accurate and biologically meaningful framework for SNP and candidate gene prioritization.
• This approach enhances the identification of functionally relevant genes underlying complex traits.
• Recommends integrating fine-mapping into genetic analysis and breeding applications for improved outcomes.