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The carpal gland is an important exocrine gland in pigs, functioning mainly through diverticula that release strongly odorous secretions. The size of the carpal gland and the number of diverticula may be related to its function. To measure its size, the gland must be dissected from the foreleg, and this procedure is time-consuming and cumbersome. The diverticulum is the outlet of the carpal gland, and its number correlates with the carpal gland size, but the correlation coefficient between them is still unknown. To explore the relationship between the size and diverticular number of the carpal gland, 788 pigs from four populations were used in this study. The length, width, thickness, weight and volume of the carpal gland were measured, together with its diverticular number. The phenotypic correlation coefficient between each pair of the recorded traits was calculated, and a two-factor analysis of variance was used to test whether sex and population have effects on the phenotypes. The carpal gland is a flat small stick with a length of 1.8–8.6 cm, width of 0.6–2.5 cm, and thickness of 0.2–0.8 cm, and its specific gravity is 0.51–1.36. Each carpal gland has 0 to 10 diverticula, and 82.3% carpal glands have 2–6 diverticula. The diverticular number correlates positively with the weight (r = 0.57 and rs = 0.58, p < 2.2 × 10−16) and volume (r = 0.55 and rs = 0.56, p < 2.2 × 10−16). Population and sex only explain 20% of the phenotypic variance, suggesting that the 80% of carpal gland influences remain unknown. In conclusion, the diverticular number can be used as a proxy for the weightand volume of the carpal gland, because it is easy to count and has a significant positive correlation with the size of thecarpal gland.

Coloration is a crucial trait that allows species to adapt and survive in different environments. Wild boars exhibit alternating black (dark) and yellow (light) longitudinal stripes on their back during their infancy (juvenile stripes), and as adults, they transform into uniform wild-type coat color. Aiming to record the procedure of juvenile stripes disappearing, piglets (WD) with juvenile stripes were produced by crossing a wild boar with Duroc sows, and photos of their coat color were taken from 20 d to 220 d. The pigments in the hairs from the black and yellow stripes were determined. Furthermore, the differentially expressed genes between the black and yellow stripes were investigated in 5 WD with the age of 30 d using whole-transcriptome sequencing to explore the genetic mechanism of the juvenile stripes. The juvenile stripes started to disappear at about 70 d, and stripes were not distinguished with the naked eye at about 160 d; that is, the juvenile stripe completely disappeared. A hotspot of a differentially expressing (DE) region was found on chromosome 13, containing/covering 2 of 13 DE genes and 8 of 10 DE lncRNAs in this region. A network among ZIC4, ssc-miR-532-3p, and ENSSSCG00000056225 might regulate the formation of juvenile stripes. Altogether, this study provides new insights into spatiotemporal coat color pattern.

Abstract Background As pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts. Results We identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs. Conclusions We performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs.

Abstract Background Currently, meat cut traits are integrated in pig breeding objectives to gain extra profit. However, little is known about the heritability of meat cut proportions (MCP) and their correlations with other traits. The aims of this study were to assess the heritability and genetic correlation of MCP with carcass and meat quality traits using single nucleotide polymorphism chips and conduct a genome-wide association study (GWAS) to identify candidate genes for MCP. Results Seventeen MCP, 12 carcass, and seven meat quality traits were measured in 2012 pigs from four populations (Landrace; Yorkshire; Landrace and Yorkshire hybrid pigs; Duroc, and Landrace and Yorkshire hybrid pigs). Estimates of the heritability for MCP ranged from 0.10 to 0.55, with most estimates being moderate to high and highly consistent across populations. In the combined population, the heritability estimates for the proportions of scapula bone, loin, back fat, leg bones, and boneless picnic shoulder were 0.44 ± 0.04, 0.36 ± 0.04, 0.44 ± 0.04, 0.38 ± 0.04, and 0.39 ± 0.04, respectively. Proportion of middle cuts was genetically significantly positively correlated with intramuscular fat content and backfat depth. Proportion of ribs was genetically positively correlated with carcass oblique length and straight length (0.35 ± 0.08 to 0.45 ± 0.07) and negatively correlated with backfat depth (− 0.26 ± 0.10 to − 0.45 ± 0.10). However, weak or nonsignificant genetic correlations were observed between most MCP, indicating their independence. Twenty-eight quantitative trait loci (QTL) for MCP were detected by GWAS, and 24 new candidate genes related to MCP were identified, which are involved with growth, height, and skeletal development. Most importantly, we found that the development of the bones in different parts of the body may be regulated by different genes, among which HMGA1 may be the strongest candidate gene affecting forelimb bone development. Moreover, as previously shown, VRTN is a causal gene affecting vertebra number, and BMP2 may be the strongest candidate gene affecting hindlimb bone development. Conclusions Our results indicate that breeding programs for MCP have the potential to enhance carcass composition by increasing the proportion of expensive cuts and decreasing the proportion of inexpensive cuts. Since MCP are post-slaughter traits, the QTL and candidate genes related to these traits can be used for marker-assisted and genomic selection.