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.

Intestinal probiotics significantly regulate the growth performance of their host, with their composition being influenced by various factors. While many studies have explored how gut microbiota composition affects growth traits such as body weight and BMI, the research on probiotics influenced by host genetic factors, and their subsequent impact on host growth performance, remains limited. To address this research gap, we collected fecal and tissue samples, as well as phenotypic data, from 193 Yunong black pigs at 280 days of age. We then sequenced and genotyped all 193 subjects using the 50K SNP BeadChip, yielding a comprehensive dataset for genetic and microbiome analyses. We then employed microbiome-wide association studies (MWAS), a meta-analysis, and microbiome-wide genetic association studies (MGWASs) to examine the relationship between host genetics, gut microbiota, and growth performance. Four key microbial taxa, namely Coprococcus, Blautia, Ruminococcaceae, and RF16, were identified as being significantly associated with body weight and BMI. The MGWAS analysis revealed that both Coprococcus and Ruminococcaceae were significantly associated with host genomic variations. A total of four important single nucleotide polymorphisms (SNPs) were mapped to two chromosomal regions, corresponding to three candidate genes. Among them, the candidate genes INPP4B, SCOC, and PABPC4L were identified as being related to the abundance of key microbes. This study provides new insights into the joint contributions of host genetics and probiotics to host growth traits, offering theoretical guidance and data support for the development of efficient and targeted breeding strategies.

Resilience usually refers to the ability of an animal to be minimally affected by disturbance or to quickly return to its pre-disturbance state. Pigs with strong resilience usually have better production performance and higher tolerance to common diseases. This study utilized feed intake records collected by a Feed Intake Recording Equipment from three pig breeds (Duroc: 823; Landrace: 582; and Yorkshire: 2032). Six resilience traits were constructed using the root mean square error (RMSE) of daily feed intake and feeding duration, cumulative feed intake, and cumulative feeding duration derived from ordinary least squares (OLS) regression, along with quantile regression (QR) estimates of daily feed intake and feeding duration. The correlations between these resilience traits and production traits were subsequently estimated. Single-trait genome-wide association studies (GWASs) were performed using imputed resequencing data to identify key genomic regions and candidate genes associated with resilience traits. The estimated heritability ranged from 0.103 to 0.267 for resilience traits and 0.293 to 0.560 for production traits. Moderate genetic correlations were observed among the resilience traits, while moderate to high genetic correlations were found between resilience traits and production traits. In particular, the traits RMSE_FI, RMSE_CFI, QR_FI, and QR_FD exhibited significant moderate to high correlations with most production traits. A genome-wide association study (GWAS) based on imputed whole-genome sequencing data was conducted to identify genomic regions associated with resilience traits in pigs. Using single-trait mixed linear models, 188 SNPs were identified and annotated to 44 candidate genes. Several of these genes (CD74, CSF1R, and HTR4) are involved in host immune responses and signal transduction pathways. These findings contribute to a better understanding of the genetic mechanisms underlying resilience in pigs and provide insights for enhancing genomic selection in pig breeding.

In terms of pig farming, pig gut microbes have a significant effect on farmers and the farm environment. However, it is still unclear which microbial composition is more likely to contribute to this effect. This study collected a total of 136 samples, including pigs’ faeces samples, farmers’ faeces samples, samples from individuals who had no contact with any type of farm animal (referred to as ‘non-exposed’ persons), and environmental dust samples (collected from inside and outside pig houses and the farm) from two pig farms, pig farm A and pig farm B. Whereafter, 16S rRNA sequencing and taxonomic composition analysis were performed. According to the study, compared to non-exposed persons, pig farmers had a significantly higher abundance of 7 genera. In addition, the farmers were grouped according to the duration of their occupational exposure, and it was shown that 4 genera, including Turicibacter, Terrisporobacter, and Clostridium_sensu_stricto_1, exhibited a rise in more frequent contact with pigs. As compared to outside the pig house, the environmental dust has a greater concentration of the 3 bacteria mentioned before. Therefore, these 3 microbes can be considered as co-occurring microbes that may exist both in humans and the environment. Also, the 3 co-occurring microbes are involved in the fermentation and production of short-chain fatty acids and their effectiveness decreased as distance from the farm increased. This study shows that the 3 microbes where pig farmers co-occur with the environment come from pig farms, which provides fresh ideas for preventing the spread of microbial aerosols in pig farms and reducing pollution.

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.