Identifying causal genetic variants and candidate genes underlying complex traits remains a central challenge in animal breeding and genetics. Genome-wide association studies (GWAS) are widely used for this purpose. However, their reliance on marginal variant effects and sensitivity to linkage disequilibrium (LD) can lead to redundant and less accurate identification of variants or genes of biological relevance. Here, we propose SNP prioritization (GWAS-based and fine-mapping-based) strategies within a unified framework, designed to improve the selection of more informative variants and candidate genes by explicitly modeling LD structure and genetic architectures of three pig teat-related traits (total teat number, teat symmetry, and teat adequacy). While GWAS prioritization favored variants with strong marginal effects, fine-mapping substantially improved joint explanatory performance and prediction accuracy. For total teat number, the best-performing fine-mapping-derived SNP subset achieved a mean PCC of 0.6599 across 10-fold cross-validation, compared with 0.3755 for GWAS-based prioritization. Similarly, for teat adequacy, the highest mean AUC increased from 0.7012 (GWAS) to 0.8547 (fine-mapping). Moreover, fine-mapping-derived SNP sets identified more coherent and trait-specific biological pathways and functionally relevant candidate genes. Taken together, our findings demonstrate that fine-mapping provides a more accurate and biologically meaningful framework for SNP and candidate gene prioritization, supporting its integration into genetic analysis and breeding applications.