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Semen traits are crucial in commercial pig production since semen from boars is widely used in artificial insemination for both purebred and crossbred pig production. Revealing the genetic architecture of semen traits potentially promotes the efficiencies of improving semen traits through artificial selection. This study is aimed to identify candidate genes related to the semen traits in Duroc boars. First, we identified the genes that were significantly associated with three semen traits, including sperm motility (MO), sperm concentration (CON), and semen volume (VOL) in a Duroc boar population through a genome-wide association study (GWAS). Second, we performed a weighted gene co-expression network analysis (WGCNA). A total of 2, 3, and 20 single-nucleotide polymorphisms were found to be significantly associated with MO, CON, and VOL, respectively. Based on the haplotype block analysis, we identified one genetic region associated with MO, which explained 6.15% of the genetic trait variance. ENSSSCG18823 located within this region was considered as the candidate gene for regulating MO. Another genetic region explaining 1.95% of CON genetic variance was identified, and, in this region, B9D2, PAFAH1B3, TMEM145, and CIC were detected as the CON-related candidate genes. Two genetic regions that accounted for 2.23% and 2.48% of VOL genetic variance were identified, and, in these two regions, WWC2, CDKN2AIP, ING2, TRAPPC11, STOX2, and PELO were identified as VOL-related candidate genes. WGCNA analysis showed that, among these candidate genes, B9D2, TMEM145, WWC2, CDKN2AIP, TRAPPC11, and PELO were located within the most significant module eigengenes, confirming these candidate genes' role in regulating semen traits in Duroc boars. The identification of these candidate genes can help to better understand the genetic architecture of semen traits in boars. Our findings can be applied for semen traits improvement in Duroc boars.

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Identification of new semen trait-related candidate genes in Duroc boars through genome-wide association and weighted gene co-expression network analyses