Multi-omic characterization of allele-specific regulatory variation in hybrid pigs.

Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.

Comprehensive Analysis of Placental DNA Methylation Changes and Fetal Birth Weight in Pigs.

Birth weight is a complex multifactorial trait relevant to health states and disease risks in later life. The placenta is essential for proper fetal growth and facilitates gas, nutrient, and waste exchange between the mother and developing fetus. How changes in placental DNA methylation affect fetal birth weight remains to be fully elucidated. In this study, we used whole-genome bisulfite sequencing and RNA sequencing to reveal a global map of DNA methylation and gene expression changes between the placentas of highest birth weight and lowest birth weight piglets in the same litters. The transcriptome analysis identified 1682 differential expressed genes and revealed key transcriptional properties in distinct placentas. We also identified key transcription factors that may drive the differences in DNA methylome patterns between placentas. The decrease in DNA methylation level in the promoter was associated with the transcriptional activation of genes associated with angiogenesis, extracellular matrix remodeling, and transmembrane transport. Our results revealed the regulatory role of DNA methylation in gene transcription activity leading to the differences in placental morphological structures and birth weights of piglets. These results could provide novel clues to clarify the underlying regulatory mechanisms of placental development and fetal growth.

Genome-wide detection of multiple variants associated with teat number in French Yorkshire pigs.

BACKGROUND: Teat number is a vital reproductive trait in sows, crucial for providing immunity and nutrition to piglets during lactation. However, "missing heritability" in Single Nucleotide Polymorphism (SNP)-based Genome-Wide Association Studies (GWAS) has led to an increasing focus on structural variations in the genetic analysis of complex biological traits. RESULTS: In this study, we generated a comprehensive CNV map in a population of French Yorkshire pigs (n = 644) and identified 429 CNVRs. Notably, 44% (189 CNVRs) of these were detected for the first time. Subsequently, we conducted GWAS for teat number in the French Yorkshire pig population using both 80K chip and its imputed data, as well as a GWAS analysis based on CNV regions (CNVR). Interestingly, 80K chip GWAS identified two SNPs located on Sus scrofa chromosome 5 (SSC5) that were simultaneously associated with Total Teat Number (TTN), Left Teat Number (LTN), and Right Teat Number (RTN). The leading SNP (WU_10.2_5_76130558) explained 3.33%, 2.69%, and 2.67% of the phenotypic variance for TTN, LTN, and RTN, respectively. Moreover, through imputed GWAS, we successfully identified 30 genetic variants associated with TTN located within the 73.22 -73.30 Mb region on SSC5. The two SNPs identified in the 80K chip GWAS were also located in this region. In addition, CNVR-based GWAS revealed three significant CNVRs associated with TTN. Finally, through gene annotation, we pinpointed two candidate genes, TRIM66 and PRICKLE1, which are related to diverse processes such as breast cancer and abnormal vertebral development. CONCLUSIONS: Our research provides an in-depth analysis of the complex genetic structure underlying teat number, contributing to the genetic enhancement of sows with improved reproductive performance and, ultimately, bolstering the economic benefits of swine production enterprises.

Wdr17 Regulates Cell Proliferation, Cell Cycle Progression and Apoptosis in Mouse Spermatocyte Cell Line.

We identified Wdr17 as a highly expressed gene in pachytene spermatocytes by transcriptomic analysis of mouse testis. Germ cell-deficient infertile mouse models had significantly reduced Wdr17 expression. We performed gene interference and overexpression in the mouse spermatocyte cell line GC-2spd(ts) and investigated how Wdr17 affects spermatocyte growth and development. Our results showed that Wdr17 suppression significantly decreased cell growth rate and increased cell apoptosis in GC-2spd(ts) cells. Wdr17 suppression also arrested the cell cycle at the G1 phase. On the contrary, Wdr17 overexpression significantly promoted cell proliferation and inhibited cell apoptosis in GC-2spd(ts) cells. More cells were enriched at the S stage with a concomitant reduction of cells at the G1 stage. Wdr17 promotes mouse spermatocyte proliferation by advancing cell cycle progression and inhibiting cell apoptosis, indicating its potential role in regulating spermatogenesis in the mouse.

Treatment of Donor Cells with Oxidative Phosphorylation Inhibitor CPI Enhances Porcine Cloned Embryo Development.

Somatic cell nuclear transfer (SCNT) technology holds great promise for livestock industry, life science and human biomedicine. However, the development and application of this technology is limited by the low developmental potential of SCNT embryos. The developmental competence of cloned embryos is influenced by the energy metabolic status of donor cells. The purpose of this study was to investigate the effects of CPI, an oxidative phosphorylation inhibitor, on the energy metabolism pathways of pig fibroblasts and the development of subsequent SCNT embryos. The results showed that treatment of porcine fibroblasts with CPI changed the cellular energy metabolic pathways from oxidative phosphorylation to glycolysis and enhanced the developmental ability of subsequent SCNT embryos. The present study establishes a simple, new way to improve pig cloning efficiency, helping to promote the development and application of pig SCNT technology.

Dynamic intrauterine crosstalk promotes porcine embryo implantation during early pregnancy.

Dynamic crosstalk between the embryo and mother is crucial during implantation. Here, we comprehensively profile the single-cell transcriptome of pig peri-implantation embryos and corresponding maternal endometrium, identifying 4 different lineages in embryos and 13 cell types in the endometrium. Cell-specific gene expression characterizes 4 distinct trophectoderm subpopulations, showing development from undifferentiated trophectoderm to polar and mural trophectoderm. Dynamic expression of genes in different types of endometrial cells illustrates their molecular response to embryos during implantation. Then, we developed a novel tool, ExtraCellTalk, generating an overall dynamic map of maternal-foetal crosstalk using uterine luminal proteins as bridges. Through cross-species comparisons, we identified a conserved RBP4/STRA6 pathway in which embryonic-derived RBP4 could target the STRA6 receptor on stromal cells to regulate the interaction with other endometrial cells. These results provide insight into the maternal-foetal crosstalk during embryo implantation and represent a valuable resource for further studies to improve embryo implantation.

Supplementation of SkQ1 Increases Mouse In Vitro Oocyte Maturation and Subsequent Embryonic Development by Reducing Oxidative Stress.

In vitro oocyte maturation (IVM) technology is important for assisted animal and human reproduction. However, the maturation rates and developmental potential of in vitro-matured oocytes are usually lower than those of in vivo-matured oocytes. Oxidative stress is a main factor that causes the lower maturation rates and quality of in vitro-matured oocytes. The purpose of this study was to investigate the effects of treatment with SkQ1, a mitochondria-targeted antioxidant, on mouse IVM and subsequent embryonic development. The results demonstrated that the supplementation of SkQ1 during IVM improves the maturation rates of mouse oocytes and the subsequent developmental competence of in vitro-fertilized embryos. The addition of SkQ1 to the IVM medium also decreased oxidative stress and apoptosis, and increased mitochondrial membrane potential in matured mouse oocytes. This study provides a new method through which to enhance the maturation rates and the quality of in vitro-matured mouse oocytes, thus promoting the application and development of assisted animal and human reproductive technology.

Calculating Volume of Pig Point Cloud Based on Improved Poisson Reconstruction.

Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95.

A deep learning-based approach for fully automated segmentation and quantitative analysis of muscle fibers in pig skeletal muscle.

Muscle fiber properties exert a significant influence on pork quality, with cross-sectional area (CSA) being a crucial parameter closely associated with various meat quality indicators, such as shear force. Effectively identifying and segmenting muscle fibers in a robust manner constitutes a vital initial step in determining CSA. This step is highly intricate and time-consuming, necessitating an accurate and automated analytical approach. One limitation of existing methods is their tendency to perform well on high signal-to-noise ratio images of intact, healthy muscle fibers but their lack of validation on more complex image datasets featuring significant morphological changes, such as the presence of ice crystals. In this study, we undertake the fully automatic segmentation of muscle fiber microscopic images stained with myosin adenosine triphosphate (mATPase) activity using a deep learning architecture known as SOLOv2. Our objective is to efficiently derive accurate measurements of muscle fiber size and distribution. Tests conducted on actual images demonstrate that our method adeptly handles the intricate task of muscle fiber segmentation, yielding quantitative results amenable to statistical analysis and displaying reliability comparable to manual analysis.

Structural and functional insights into yeast Tbf1 as an atypical telomeric repeat-binding factor.

Telomeric repeat-binding factor 1 (Tbf1) has a similar architecture as the TRF family of telomeric proteins and plays important roles in both telomere homeostasis and ribosome regulation. However, the molecular basis of why Tbf1 has such different functions compared to other TRFs remains unclear. Here, we present the crystal structures of the TRF homology (TRFH) and Myb-L domains from Schizosaccharomyces pombe Tbf1 (spTbf1). TRFH-mediated homodimerization is essential for spTbf1 stability. Importantly, spTbf1TRFH lacks the conserved docking motif for interactions with telomeric proteins, explaining why spTbf1 does not participate in the assembly of the shelterin complex. Finally, structural and biochemical analyses demonstrate that TRFH and Myb-L domains as well as the loop region of spTbf1 coordinate to recognize S. pombe telomeric double-stranded DNA. Overall, our findings provide structural and functional insights into how fungi Tbf1 acts as an atypical telomeric repeat-binding factor, which helps to understand the evolution of TRFH-containing telomeric proteins.

Porcine Granulosa-Cell-Derived Exosomes Enhance Oocyte Development: An In Vitro Study.

Recent studies have established that exosomes (EXs) derived from follicular fluid (FF) can promote oocyte development. However, the specific sources of these EXs and their regulatory mechanisms remain elusive. It is universally acknowledged that oocyte development requires signal communication between granulosa cells (GCs) and oocytes. However, the role of GC-secreted EXs and their functions are poorly understood. This study aimed to investigate the role of porcine granulosa-cell-derived exosomes (GC-EXs) in oocyte development. In this study, we constructed an in vitro model of porcine GCs and collected and identified GC-EXs. We confirmed that porcine GCs can secrete EXs and investigated the role of GC-EXs in regulating oocyte development by supplementing them to cumulus-oocyte complexes (COCs) cultured in vitro. Specifically, GC-EXs increase the cumulus expansion index (CEI), promote the expansion of the cumulus, alleviate reactive oxygen species (ROS), and increase mitochondrial membrane potential (MMP), resulting in improved oocyte development. Additionally, we conducted small RNA sequencing of GC-EXs and hypothesized that miR-148a-3p, the highest-expressed microRNA (miRNA), may be the key miRNA. Our study determined that transfection of miR-148a-3p mimics exerts effects comparable to the addition of EXs. Meanwhile, bioinformatics prediction, dual luciferase reporter gene assay, and RT-qPCR identified DOCK6 as the target gene of miR-148a-3p. In summary, our results demonstrated that GC-EXs may improve oocyte antioxidant capacity and promote oocyte development through miR-148a-3p by targeting DOCK6.

Protein Dynamic Landscape during Mouse Mammary Gland Development from Virgin to Pregnant, Lactating, and Involuting Stages.

The mammary gland undergoes significant physiological changes as it undergoes a transition from virgin to pregnancy, lactation, and involution. However, the dynamic role of proteins in regulating these processes during mouse mammary gland development has not been thoroughly explored. In this study, we collected mouse mammary gland tissues from mature virgins aged 8-10 weeks (V), day 16 of pregnancy (P16d), day 12 of lactation (L12d), day 1 of forced weaning (FW 1d), and day 3 of forced weaning (FW 3d) stages for analysis using DIA-based quantitative proteomics technology. A total of 3,312 proteins were identified, of which 843 were DAPs that were categorized into nine clusters based on their abundance changes across developmental stages. Notably, DAPs in cluster 2, which peaked at the L12d stage, were primarily associated with mammary gland development and lactation. The protein-protein interaction network revealed that the epidermal growth factor (EGF) was central to this cluster. Our study provides a comprehensive overview of the mouse mammary gland development proteome and identifies some important proteins, such as EGF, Janus kinase 1 (JAK1), and signal transducer and activator of transcription 6 (STAT6) that may serve as potential targets for future research to provide guidelines for a deeper understanding of the developmental biology of mammary glands.

RASGRP1 targeted by H3K27me3 regulates myoblast proliferation and differentiation in mice and pigs.

Skeletal muscle is not only the largest organ in the body that is responsible for locomotion and exercise but also crucial for maintaining the body's energy metabolism and endocrine secretion. The trimethylation of histone H3 lysine 27 (H3K27me3) is one of the most important histone modifications that participates in muscle development regulation by repressing the transcription of genes. Previous studies indicate that the RASGRP1 gene is regulated by H3K27me3 in embryonic muscle development in pigs, but its function and regulatory role in myogenesis are still unclear. In this study, we verify the crucial role of H3K27me3 in RASGRP1 regulation. The gain/loss function of RASGRP1 in myogenesis regulation is performed using mouse myoblast C2C12 cells and primarily isolated porcine skeletal muscle satellite cells (PSCs). The results of qPCR, western blot analysis, EdU staining, CCK-8 assay and immunofluorescence staining show that overexpression of RASGRP1 promotes cell proliferation and differentiation in both skeletal muscle cell models, while knockdown of RASGRP1 leads to the opposite results. These findings indicate that RASGRP1 plays an important regulatory role in myogenesis in both mice and pigs.

Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.

The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.

Gender Control of Mouse Embryos by Activation of TLR7/8 on X Sperm via Ligands dsRNA-40 and dsRNA-DR.

Gender control technologies are promising for enhancing the production efficiency of the farm animal industry, and preventing sex-linked hereditary diseases in humans. It has been shown that the X sperm of mammalian animals specifically expresses X-chromosome-derived toll-like receptor 7/8 (TLR7/8), and the activation of TLR7/8 on the X sperm by their agonist, R848, can separate X and Y sperm via the specific inhibition of X sperm motility. The use of R848-preselected sperm for fertilization resulted in sex-ratio-skewed embryos or offspring. In this study, we aimed to investigate whether two other TLR7/8 ligands, double-stranded RNA-40 (dsRNA-40) and double-stranded RNA-DR (dsRNA-DR), are also effective in the separation of mouse X and Y sperm and the subsequent generation of gender-ratio-skewed in vitro fertilization (IVF) embryos. Our results indicated that cholesterol modification significantly enhances the transfection of dsRNA-40 and dsRNA-DR into sperm cells. dsRNA-40 and dsRNA-DR incubation with mouse sperm could separate X and Y sperm by the specific suppression of X sperm motility by decreasing its ATP level and mitochondrial activity. The use of a dsRNA-40- or dsRNA-DR-preselected upper layer of sperm, which predominantly contains high-motility Y sperm, for IVF caused a male-biased sex ratio shift in resulting embryos (with 65.90-74.93% of embryos being male). This study develops a simple new method for the efficient separation of mammalian X and Y sperm, enabling the selective production of male or female progenies.

Protein Dynamic Landscape of Pig Embryos during Peri-Implantation Development.

Properly developed embryos are critical for successful embryo implantation. The dynamic landscape of proteins as executors of biological processes in pig peri-implantation embryos has not been reported so far. In this study, we collected pig embryos from days 9, 12, and 15 of pregnancy during the peri-implantation stage for a PASEF-based quantitative proteomic analysis. In total, approximately 8000 proteins were identified. These proteins were classified as stage-exclusive proteins and stage-specific proteins, respectively, based on their presence and dynamic abundance changes at each stage. Functional analysis showed that their roles are consistent with the physiological processes of corresponding stages, such as the biosynthesis of amino acids and peptides at P09, the regulation of actin cytoskeletal organization and complement activation at P12, and the vesicular transport at P15. Correlation analysis between mRNAs and proteins showed a general positive correlation between pig peri-implantation embryonic mRNAs and proteins. Cross-species comparisons with human early embryos identified some conserved proteins that may be important in regulating embryonic development, such as STAT3, AP2A1, and PFAS. Our study provides a comprehensive overview of the pig embryo proteome during implantation, fills gaps in relevant developmental studies, and identifies some important proteins that may serve as potential targets for future research.

DIA-based quantitative proteomic analysis of porcine endometrium in the peri-implantation phase.

The 12th day of gestation is a critical period for embryo loss and the beginning of imminent implantation in sows. Data independent acquisition (DIA) technology is one of the high-throughput, high-resolution and reproducible proteomics technologies for large-scale digital qualitative and quantitative research. The aim of this study was to identify and characterize the protein abundance landscape of Yorkshire pig endometrium on the 12th day of pregnancy (P12) and estrous cycle (C12) using DIA proteomics. A total of 1251 differentially abundant proteins (DAPs) were identified, of which 882 were up-regulated and 369 were down-regulated at P12. Functional enrichment analysis showed that the identified proteins were related to metabolism, biosynthesis and signaling pathways. Three proteins were selected for Western blot (WB) validation and the results were consistent with the DIA data. Further combined with transcriptome data, fibrinogen like 2 (FGL2) and S100 calcium binding protein A8 (S100A8) were verified to be highly abundant in the P12 endometrial epithelium. In summary, there were significantly different abundance of proteome profiles in C12 and P12 endometrium, suggesting that DAPs are associated with changes in endometrial receptivity, which laid the foundation for further research on related regulatory mechanisms. SIGNIFICANCE: The 12th day of gestation is an important point in the peri-implantation period of pigs, when the endometrium presents a receptive state under the stimulation of estrogen. DIA proteomics technology is an emerging protein identification technology in recent years, which can obtain protein information through comprehensive and unbiased scanning. In this study, DIA technology was used to characterize endometrial proteins in pigs during the peri-implantation period. The results showed that higher protein abundance was detected using the DIA technique, and some of these DAPs may be involved in regulating embryo implantation. This study will help to better reveal the related proteins involved in embryo implantation, and lay a foundation for further research on the mechanism of endometrial regulation of embryo implantation. SIGNIFICANCE OF THE STUDY: The 12th day of gestation is an important point in the peri-implantation period of pigs, when the endometrium presents a receptive state under the stimulation of estrogen. DIA proteomics technology is an emerging protein identification technology in recent years, which can obtain protein information through comprehensive and unbiased scanning. In this study, DIA technology was used to characterize endometrial proteins in pigs during the peri-implantation period. The results showed that higher protein abundance was detected using the DIA technique, and some of these DAPs may be involved in regulating embryo implantation. This study will help to better reveal the related proteins involved in embryo implantation, and lay a foundation for further research on the mechanism of endometrial regulation of embryo implantation.

Genome-wide association studies for loin muscle area, loin muscle depth and backfat thickness in DLY pigs.

This study aimed at identifying genes associated with loin muscle area (LMA), loin muscle depth (LMD) and backfat thickness (BFT). We performed single-trait and multi-trait genome-wide association studies (GWASs) after genotyping 685 Duroc × (Landrace × Yorkshire) (DLY) pigs using the Geneseek Porcine 50K SNP chip. In the single-trait GWASs, we identified two, eight and two significant SNPs associated with LMA, LMD and BFT, respectively, and searched genes within the 1 Mb region near the significant SNPs with relevant functions as candidate genes. Consequently, we identified one (DOCK5), three (PID1, PITX2, ELOVL6) and three (CCR1, PARP14, CASR) promising candidate genes for LMA, LMD and BFT, respectively. Moreover, the multi-trait GWAS identified four significant SNPs associated with the three traits. In conclusion, the GWAS analysis of LMA, LMD and BFT in a DLY pig population identified several associated SNPs and candidate genes, further deepening our understanding of the genetic basis of these traits, and they may be useful for marker-assisted selection to improve the three traits in DLY pigs.

Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs.

Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.

Genome-Wide Association Study for Somatic Skeletal Traits in Duroc × (Landrace × Yorkshire) Pigs.

The pig bone weight trait holds significant economic importance in southern China. To expedite the selection of the pig bone weight trait in pig breeding, we conducted molecular genetic research on these specific traits. These traits encompass the bone weight of the scapula (SW), front leg bone weight (including humerus and ulna) (FLBW), hind leg bone weight (including femur and tibia) (HLBW), and spine bone weight (SBW). Up until now, the genetic structure related to these traits has not been thoroughly explored, primarily due to challenges associated with obtaining the phenotype data. In this study, we utilized genome-wide association studies (GWAS) to discern single nucleotide polymorphisms (SNPs) and genes associated with four bone weight traits within a population comprising 571 Duroc × (Landrace × Yorkshire) hybrid pigs (DLY). In the analyses, we employed a mixed linear model, and for the correction of multiple tests, both the false discovery rate and Bonferroni methods were utilized. Following functional annotation, candidate genes were identified based on their proximity to the candidate sites and their association with the bone weight traits. This study represents the inaugural application of GWAS for the identification of SNPs associated with individual bone weight in DLY pigs. Our analysis unveiled 26 SNPs and identified 12 promising candidate genes (OPRM1, SLC44A5, WASHC4, NOPCHAP1, RHOT1, GLP1R, TGFB3, PLCB1, TLR4, KCNJ2, ABCA6, and ABCA9) associated with the four bone weight traits. Furthermore, our findings on the genetic mechanisms influencing pig bone weight offer valuable insights as a reference for the genetic enhancement of pig bone traits.