Recently, genome-wide association studies (GWAS) have been reported on various pig traits. We performed a GWAS to analyze 22 traits related to growth and fatness on two pig populations: a White Duroc × Erhualian F2 intercross population and a Chinese Sutai half-sib population. We identified 14 and 39 loci that displayed significant associations with growth and fatness traits at the genome-wide level and chromosome-wide level, respectively. The strongest association was between a 750 kb region on SSC7 (SSC for Sus scrofa) and backfat thickness at the first rib. This region had pleiotropic effects on both fatness and growth traits in F2 animals and contained a promising candidate gene HMGA1 (high mobility group AT-hook 1). Unexpectedly, population genetic analysis revealed that the allele at this locus that reduces fatness and increases growth is derived from Chinese indigenous pigs and segregates in multiple Chinese breeds. The second strongest association was between the region around 82.85 Mb on SSC4 and average backfat thickness. PLAG1 (pleiomorphic adenoma gene 1), a gene under strong selection in European domestic pigs, is proximal to the top SNP and stands out as a strong candidate gene. On SSC2, a locus that significantly affects fatness traits mapped to the region around the IGF2 (insulin-like growth factor 2) gene but its non-imprinting inheritance excluded IGF2 as a candidate gene. A significant locus was also detected within a recombination cold spot that spans more than 30 Mb on SSCX, which hampered the identification of plausible candidate genes. Notably, no genome-wide significant locus was shared by the two experimental populations; different loci were observed that had both constant and time-specific effects on growth traits at different stages, which illustrates the complex genetic architecture of these traits. We confirm several previously reported QTL and provide a list of novel loci for porcine growth and fatness traits in two experimental populations with Chinese Taihu and Western pigs as common founders. We showed that distinct loci exist for these traits in the two populations and identified HMGA1 and PLAG1 as strong candidate genes on SSC7 and SSC4, respectively.

Resequencing a number of individuals of various breeds as reference population and imputing the whole-genome sequences of individuals that were genotyped with medium-density chips to perform an association study is a very efficient strategy. Previously, we performed a genome-wide association study (GWAS) of lumbar number using 60K SNPs from the porcine Illumina chips in 418 Sutai pigs and did not detect any significant signals. Therefore, we imputed the whole-genome sequences of 418 Sutai individuals from 403 deeply resequenced reference individuals and performed association tests. We identified a quantitative trait locus (QTL) for lumbar number in SSC1 with a P value of 9.01E-18 that was close to the potential causative gene of NR6A1. The result of conditioning on the top SNP association test indicated that only one QTL was responsible for this trait in SSC1. The linkage disequilibrium (LD) drop test result for the condition of the reported potential causative mutation (c.575T > C missense mutation of NR6A1) indicated that this mutation was probably not the underlying mutation that affected lumbar number in our study. As the first trial of imputed whole-genome sequence GWAS in swine, this approach can be also powerful to investigate complex traits in pig like in human and cattle.

Motion and aggressive behaviors in pigs provide important information for the study of social hierarchies in pigs and can be used as a selection indicator for pig health and aggression parameters. However, relying only on visual observation or surveillance video to record the number of aggressive acts is time-consuming, labor-intensive, and lasts for only a short period of time. Manual observation is too short compared to the growth cycle of pigs, and complete recording is impractical in large farms. In addition, due to the complex process of assessing the intensity of pig aggression, manual recording is highly influenced by human subjective vision. In order to efficiently record pig motion and aggressive behaviors as parameters for breeding selection and behavioral studies, the videos and pictures were collected from typical commercial farms, with each unit including 8~20 pigs in 7~25 m² space; they were bred in stable social groups and a video was set up to record the whole day’s activities. We proposed a deep learning-based recognition method for detecting and recognizing the movement and aggressive behaviors of pigs by recording and annotating head-to-head tapping, head-to-body tapping, neck biting, body biting, and ear biting during fighting. The method uses an improved EMA-YOLOv8 model and a target tracking algorithm to assign a unique digital identity code to each pig, while efficiently recognizing and recording pig motion and aggressive behaviors and tracking them, thus providing statistics on the speed and duration of pig motion. On the test dataset, the average precision of the model was 96.4%, indicating that the model has high accuracy in detecting a pig’s identity and its fighting behaviors. The model detection results were highly correlated with the manual recording results (R² of 0.9804 and 0.9856, respectively), indicating that the method has high accuracy and effectiveness. In summary, the method realized the detection and identification of motion duration and aggressive behavior of pigs under natural conditions, and provided reliable data and technical support for the study of the social hierarchy of pigs and the selection of pig health and aggression phenotypes.

Background: It has been shown that hematological traits are strongly associated with the metabolism and the immune system in domestic pig. However, little is known about the genetic architecture of hematological traits. To identify quantitative trait loci (QTL) controlling hematological traits, we performed single marker Genome-wide association studies (GWAS) and haplotype analysis for 15 hematological traits in 495 Chinese Sutai pigs.Results: We identified 161 significant SNPs including 44 genome-wide significant SNPs associated with 11 hematological traits by single marker GWAS. Most of them were located on SSC2. Meanwhile, we detected 499 significant SNPs containing 154 genome-wide significant SNPs associated with 9 hematological traits by haplotype analysis. Most of the identified loci were located on SSC7 and SSC9.Conclusions: We detected 4 SNPs with pleiotropic effects on SSC2 by single marker GWAS and (or) on SSC7 by haplotype analysis. Furthermore, through checking the gene functional annotations, positions and their expression variation, we finally selected 7 genes as potential candidates. Specially, we found that three genes (TRIM58, TRIM26andTRIM21) of them originated from the same gene family and executed similar function of innate and adaptive immune. The findings will contribute to dissection the immune gene network, further identification of causative mutations underlying the identified QTLs and providing insights into the molecular basis of hematological trait in domestic pig.

High-quality whole-genome resequencing in large-scale pig populations with pedigree structure and multiple breeds would enable accurate construction of haplotype and robust selection-signature detection. Here, we sequence 740 pigs, combine with 149 of our previously published resequencing data, retrieve 207 resequencing datasets, and form a panel of worldwide distributed wild boars, aboriginal and highly selected pigs with pedigree structures, amounting to 1096 genomes from 43 breeds. Combining with their haplotype-informative reads and pedigree structure, we accurately construct a panel of 1874 haploid genomes with 41,964,356 genetic variants. We further demonstrate its valuable applications in GWAS by identifying five novel loci for intramuscular fat content, and in genomic selection by increasing the accuracy of estimated breeding value by 36.7%. In evolutionary selection, we detect MUC13 gene under a long-term balancing selection, as well as NPR3 gene under positive selection for pig stature. Our study provides abundant genomic variations for robust selection-signature detection and accurate haplotypes for deciphering complex traits in pigs.

The size and type of ears are important conformation characteristics that distinguish pig breeds. A significant quantitative trait locus (QTL) for ear size has been identified on SSC5 (SSC for Sus scrofa chromosome) but the underlying causative gene and mutation remain unknown. Thus, our aim was to identify the gene responsible for enlarged ears in pig. First, we narrowed down the QTL region on SSC5 to a 137.85-kb interval that harbors only the methionine sulfoxide reductase B3 (MSRB3) gene. Then, we identified a 38.7-kb copy number variation (CNV) that affects the last two exons of MSRB3 and could be the candidate causative mutation for this QTL. This CNV showed complete concordance with genotype at the QTL of the founder animals in a white Duroc × Erhualian F2 intercross and was found only in pigs from six Chinese indigenous breeds with large ears and from the Landrace breed with half-floppy ears. Moreover, it accounted for the significant association with ear size on SSC5 across the five pig populations tested. eQTL mapping revealed that this CNV was significantly associated with the expression of the microRNA (miRNA) miR-584-5p, which interacts with MSRB3, one of its target genes. In vivo and in vitro experiments confirmed that miR-584-5p inhibits the translation of MSRB3 mRNA. Taken together, these results led us to conclude that presence of the 38.7-kb CNV in the genome of some pig breeds affects ear size by altering the expression of miR-584-5p, which consequently hinders the expression of one of its target genes (e.g. MSRB3). Our findings shed insight into the underlying mechanism of development of external ears in mammals and contribute to a better understanding of how the presence of CNV can regulate gene expression.