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.

Loin muscle area (LMA) is an important production trait in pigs and is highly correlated with lean meat percentage. However, the genetic architecture of LMA has not yet been fully elucidated. This study conducted genome-wide association studies (GWAS) and meta-analyses of LMA in Duroc (n = 337), Landrace (n = 662), and Yorkshire pigs (n = 3,176) using imputed whole-genome sequencing to identify new QTLs and candidate genes associated with LMA traits. A total of 108, 34, and 232 significant variants were identified in the Duroc, Landrace, and Yorkshire populations, respectively. The meta-analysis revealed 143 genome-wide significant SNPs and 276 suggestive SNPs, among which 213 were not identified in single population GWAS. Notably, 229 and 413 SNPs were located on SSC16 in the Yorkshire population and meta-analysis, respectively. Based on the 2-LOD drop-off interval, the SSC16 QTL in the Yorkshire population was further narrowed to a 679.835 kb interval (from 32.818 Mb to 33.498 Mb). The most significant variant within this QTL, 16_33228254 (P = 4.45 × 10^–9), explained 1.11% phenotypic variance, representing a potential novel locus for LMA. Further bioinformatics analysis determined seven promising candidate genes (NDUFS4, ARL15, FST, ADAM12, DAB2, PLPP1, and SGMS2) with biological processes such as myoblast fusion and positive regulation of transforming growth factor beta receptor signaling pathway. Among them, ARL15 was previously reported in LMA studies, while the other six genes represent novel candidate genes. These findings reveal potential functional genes and pathways associated with LMA, providing valuable insights for future genetic improvement in pigs.

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 this study, we used electronic feeding station observations of pigs to investigate their growth performance and feeding behavior. These pigs were raised in Xinda Livestock Company Ltd. in Henan, China, and followed the Chinese directive for the protection of farm animals. Electronic feeding stations are often used in pig breeding and for identifying loci associated with feed consumption. Moreover, they can also provide much valuable information on pig behavior that could be directly used to improve production efficiency. This study involves three different pig population feeding-intake data from 383 Yorkshire, 243 Landrace and 197 Duroc breeds to investigate their growth performance and feeding behavior. Similar patterns of growth performance (slow–fast–slow) were observed in the three breeds, while the Duroc have a slightly faster average growth rate throughout the whole test period. Study of the number of visits to the feeding station over 24 h detected two peaks of feeding intake activity at 08:00–09:00 and 14:00–15:00. During these two peak feeding times, lower feed intake and less occupation time were observed, implicating fiercer competition at these time periods. The effective intake time for Duroc, Landrace and Yorkshire populations was 19, 16 and 19 min, respectively, suggesting that the ultimate feed intake time for each individual can be set up to 20 min for each visit. Studies on the ADFI (average daily feed intake) showed a significant difference (P value = 0.000009) between seasons and consistent patterns for these three breeds. The present study provides a detailed survey on pig feed intake behaviors across different populations and feeding seasons.

Analysis of correlation between the early testable phenotypes of piglets and the final performance of pigs can serve the early selection for breeding. The objectives of this study were to estimate the genetic parameters for birth weight (BtW), age (AGE) and backfat thickness (BF) up to 115 kg BW and to analyse the relationships among these three traits, and to estimate the accuracy of using BtW to predict estimated breeding values (EBVs) of AGE and BF in Landrace and Duroc pigs. Data on 26 614 Landrace and 19 984 Duroc pigs, born between 2001 and 2018 were collected from the core breeding group of a farm. All pigs were recorded for phenotypes including BtW, AGE and BF. The factors affecting these three traits were analysed using R v4.2.0 Software. The population genetic parameters and breeding values of three traits were estimated by using a multi-trait animal model based on AI plate of DMU software. Heritabilities for BtW, AGE and BF were moderate to high for Landrace (0.437, 0.282and 0.137, respectively) and Duroc breeds (0.369, 0.279 and 0.148). BtW was genetically correlated with AGE and BF in Landrace (-0.213, 0.037) and Duroc (-0.214, 0.025). AGE was negatively genetically correlated with BF in both Landrace (-0.036) and Duroc (-0.057) pigs. The heritability of BtW, AGE and BF of Landrace pigs and Duroc pigs were 0.148, 0.182 and 0.075 and 0.168, 0.159 and 0.120, respectively, by taking into account of the litter effect. BtW was genetically correlated with AGE and BF in Landrace (-0.094, 0.002) and Duroc (-0.199, -0.052). AGE was negatively genetically correlated with BF in both Landrace (-0.034) and Duroc (-0.153) pigs. The variances between total individual BtW and AGE and BF were then used to predict the EBV of AGE and BF for individuals with AGE or BF phenotypes missing under 10-fold cross validation. Prediction accuracy was calculated as the Kendall tau-b correlation coefficient between EBVs and EBVs via 10-fold cross-validation. Prediction accuracy for AGE and BF was 0.655 and 0.611 in Landrace, 0.665 and 0.617 in Duroc. After incorporation of the litter effect, the prediction accuracy for AGE and BF increased to 0.690 and 0.665 in Landrace and to 0.705 and 0.649 in Duroc. So, the EBV of AGE and BF phenotypes missing individuals could be predicted by using the available phenotypic data and the easily measured BtW, and litter effect could boost the accuracy of prediction.