Gestation length (GL) is a critical indicator of reproductive performance in sows and is closely associated with other reproductive traits, such as total number born (TNB) and number born alive (NBA). Despite its importance, the genetic mechanisms underlying GL and its impact on reproductive traits remain poorly understood. In this study, we investigated the relationship between GL and reproductive traits using 7013 farrowing records and conducted an imputed whole-genome sequence-based genome-wide association study (GWAS) for GL in first-parity sows, involving 3005 French Large White sows. Our findings revealed that the heritability of GL ranged from 0.22 to 0.26. Both excessively short and long GLs were associated with negative impacts on TNB, NBA, and other reproductive traits. A total of 64 SNPs exceeded the significance threshold, leading to the identification of two novel quantitative trait loci (QTLs) on chromosome 5 (QTL-1: 15.29–15.39 Mb and QTL-2: 12.86–12.94 Mb) and three promising candidate genes: TROAP, RFX4, and ADCY6. Gene ontology and KEGG pathway enrichment analyses revealed that these candidate genes are enriched in key biological processes, including ovarian steroidogenesis, the GnRH signaling pathway, and the regulation of cAMP biosynthesis, all of which are crucial for gestation and pregnancy maintenance. These findings improve our understanding of the genetic architecture of GL in sows and offer valuable genetic markers for enhancing reproductive efficiency in breeding programs.

Feed conversion ratio (FCR) is a key indicator of pig productivity, but its measurement is labor-intensive and time-consuming. This study aimed to construct a predictive model for cumulative feeding intake (CFI), which could help estimate FCR more efficiently and reduce the time and effort needed for measurements. This study included a total of 987 Yorkshire boars raised in specific pathogen-free environments, with feeding and growth data collected using automatic feeders. The segmented R package and Bayesian ridge regression (BRR) were used to build a predictive model for CFI. The results showed that the optimal body weight range for predicting FCR was 80–110 kg. The BRR model achieved 80% accuracy for CFI prediction, and FCR calculated from predicted CFI showed 81.4% similarity to the corrected FCR. The results clearly demonstrate that even with a limited training dataset, the BRR model has good predictive potential for FCR. The findings of this study could reduce the selection pressure on FCR traits, decrease production costs, and shorten measurement periods, ultimately benefiting the swine industry significantly.