Multiomics Reveals the Microbiota and Metabolites Associated with Sperm Quality in Rongchang Boars.

In this study, we investigated the correlation between the composition and function of the gut microbiota and the semen quality of Rongchang boars. Significant differences in gut microbial composition between boars with high (group H) and low (group L) semen utilization rates were identified through 16S rRNA gene sequencing, with 18 differential microbes observed at the genus level. Boars with lower semen utilization rates exhibited a higher relative abundance of Treponema, suggesting its potential role in reducing semen quality. Conversely, boars with higher semen utilization rates showed increased relative abundances of Terrisporobacter, Turicibacter, Stenotrophomonas, Clostridium sensu stricto 3, and Bifidobacterium, with Stenotrophomonas and Clostridium sensu stricto 3 showing a significant positive correlation with semen utilization rates. The metabolomic analyses revealed higher levels of gluconolactone, D-ribose, and 4-pyridoxic acid in the H group, with 4 pyridoxic acid and D-ribose showing a significant positive correlation with Terrisporobacter and Clostridium sensu stricto 3, respectively. In contrast, the L group showed elevated levels of D-erythrose-4-phosphate, which correlated negatively with Bifidobacterium and Clostridium sensu stricto 3. These differential metabolites were enriched in the pentose phosphate pathway, vitamin B6 metabolism, and antifolate resistance, potentially influencing semen quality. These findings provide new insights into the complex interplay between the gut microbiota and boar reproductive health and may offer important information for the discovery of disease biomarkers and reproductive health management.

Maternal Hypermethylated Genes Contribute to Intrauterine Growth Retardation of Piglets in Rongchang Pigs.

The placenta is a crucial determinant of fetal survival, growth, and development. Deficiency in placental development directly causes intrauterine growth retardation (IUGR). IUGR can lead to fetal growth restriction and an increase in the mortality rate. The genetic mechanisms underlying IUGR development, however, remain unclear. In the present study, we integrated whole-genome DNA methylation and transcriptomic analyses to determine distinct gene expression patterns in various placental tissues to identify pivotal genes that are implicated with IUGR development. By performing RNA-sequencing analysis, 1487 differentially expressed genes (DEGs), with 737 upregulated and 750 downregulated genes, were identified in IUGR pigs (H_IUGR) compared with that in normal birth weight pigs (N_IUGR) (p < 0.05); furthermore, 77 miRNAs, 1331 lncRNAs, and 61 circRNAs were differentially expressed. The protein-protein interaction network analysis revealed that among these DEGs, the genes GNGT1, ANXA1, and CDC20 related to cellular developmental processes and blood vessel development were the key genes associated with the development of IUGR. A total of 495,870 differentially methylated regions were identified between the N_IUGR and H_IUGR groups, which included 25,053 differentially methylated genes (DMEs); moreover, the overall methylation level was higher in the H_IUGR group than in the N_IUGR group. Combined analysis showed an inverse correlation between methylation levels and gene expression. A total of 1375 genes involved in developmental processes, tissue development, and immune system regulation exhibited methylation differences in gene expression levels in the promoter regions and gene ontology regions. Five genes, namely, ANXA1, ADM, NRP2, SHH, and SMAD1, with high methylation levels were identified as potential contributors to IUGR development. These findings provide valuable insights that DNA methylation plays a crucial role in the epigenetic regulation of gene expression and mammalian development and that DNA-hypermethylated genes contribute to IUGR development in Rongchang pigs.

Multi-omics unveils tryptophan metabolic pathway as a key pathway influencing residual feed intake in Duroc swine.

The genetic trait of residual feed intake (RFI) holds considerable importance in the swine industry. Recent research indicates that the gut microbiota of pigs plays a pivotal role in the manifestation of the RFI trait. Nevertheless, the metabolic pathways involved in the functioning of these microorganisms remain elusive. Thus, based on the ranking of the RFI trait in Duroc pigs, the present study selected the top 10 and bottom 10 pigs as the experimental subjects. The distribution and metabolite differences of cecal microbiota were analyzed using 16S rRNA gene sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques. The low RFI cecal group was named LRC, and the high RFI cecal group was named HRC. The results indicate that the LRC group had lower RFI, feed conversion ratio (FCR), average daily feed intake (ADFI) (p < 0.001), and thinner backfat (p < 0.05) compared with the HRC group. We simultaneously recorded the foraging behavior as well, the LRC group had a significant increase in total time spent at the feeder per day (TPD) (p < 0.05) and a significant increase in average feed intake per mins (AFI) and the number of visits to the feeder per day (NVD) compared to the HRC group (p < 0.001). Clostridium_XVIII, Bulleidia, and Intestinimonas were significantly enriched in the LRC group (p < 0.01), while Sutterella, Fusobacterium, and Bacteroides were significantly increased in the HRC group (p < 0.01). In the metabolome, we detected 390 (248 metabolites up and 142 down in the LRC compared with HRC), and 200 (97 metabolites up and 103 down in the LRC compared with HRC) differential metabolites in positive and negative ionization modes. The comprehensive analysis found that in the LRC group, Escherichia and Eubacterium in the gut may increase serotonin content, respectively. Bacteroides may deplete serotonin. We suggest that the RFI may be partly achieved through tryptophan metabolism in gut microbes. In individuals with low RFI, gut microbes may enhance feed efficiency by enhancing host synthesis and metabolism of tryptophan-related metabolites.

Lactobacillus sp. participated in the adaptation of Rongchang piglets to cold stress.

Rongchang piglets were easily induced to cold stress and diarrhoea in the winter when raised in an open hog house. However, they also gradually recovered under mid-cold stress. Other studies have suggested gut microbiome might be involved in the host energy metabolism to relieve stress. To study how to adapt Rongchang piglets to cold stress by gut microbiome, thirty Rongchang piglets were randomly divided into a mild cold stress group and a control group for 30 consecutive days. The findings revealed that the piglets had low growth performance and a high diarrhoea rate and mortality rate during the first half of the cold treatment, but subsequently stabilised. The level of cortisol (COR) also displayed a similar trend. In the mild cold stress group, the relative abundance of Muribaculaceae significantly increased on day 15, and the predominant bacterial on day 30 was Lactobacillus sp. Our results indicated that the Rongchang piglet's production performance and health were impaired at the start of the mild cold stress. However, as time passed, the body could progressively adapt to the low temperature, and Lactobacillus sp. participated in this process. This study provides new insight into how to alleviate health damage caused by cold stress.

Genomic prediction of pig growth traits based on machine learning.

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.

CYP24A1 is associated with fetal mummification in pigs.

Mummified piglets are among the leading causes of fertility loss and severely hamper reproductive performance in pigs. However, the contributions of genomic variation to the emergence of mummified piglets (MUM) have rarely been studied. This study aims to (1) elucidate the genetic architecture of MUM in sows of parity 1 - 3 using a single-step genome-wide association study (ssGWAS). The ssGWAS involved genotyping-by-sequencing of Large White and Landrace pig breeds. (2) Explore the biological role of the candidate genes at the cellular level. A total of 185 and 48 genome-wide significant SNPs are associated with MUM in Large White and Landrace pigs, explaining 0.01-36.52% genetic variance for different significant loci, respectively. All the significant SNPs are parity-specific, and the numerous, consecutive significant loci likely generated the nine significant peaks in different parities. Multiple candidate genes (including CYP24A1, FBXO30, and ARHGEF28) are associated with fetal congenital and maternal diseases. Collectively, CYP24A1 regulation contributes to steady-state levels of embryo development genes. CYP24A1 is involved in reproduction and, immune and gestational disorders. Thus, it is associated with known newborn death traits and MUM in Large White sows. Altogether, these results improve the current understanding of the genetic architecture of MUM and expand the knowledge on genetic variations for selecting against mummified piglets in pig breeding.

Whole-genome sequence association study identifies cyclin dependent kinase 8 as a key gene for the number of mummified piglets.

OBJECTIVE: Pigs, an ideal biomedical model for human diseases, suffer from about 50% early embryonic and fetal death, a major cause of fertility loss worldwide. However, identifying the causal variant remains a huge challenge. This study aimed to detect single nucleotide polymorphisms (SNPs) and candidate genes for the number of mummified (NM) piglets using the imputed whole-genome sequence (WGS) and validate the potential candidate genes. METHODS: The imputed WGS was introduced from genotyping-by-sequencing (GBS) using a multi-breed reference population. We performed genome-wide association studies (GWAS) for NM piglets at birth from a Landrace pig populatiGWAS peak located on SSC11: 0.10 to 7.11 Mbp (Top SNP, SSC11:1,889,658 bp; p = 9.98E-13) was identified in cyclin dependent kinase on. A total of 300 Landrace pigs were genotyped by GBS. The whole-genome variants were imputed, and 4,252,858 SNPs were obtained. Various molecular experiments were conducted to determine how the genes affected NM in pigs. RESULTS: A strong GWAS peak located on SSC11: 0.10 to 7.11 Mbp (Top SNP, SSC11:1,889,658 bp; p = 9.98E-13) was identified in cyclin dependent kinase 8 (CDK8) gene, which plays a crucial role in embryonic retardation and lethality. Based on the molecular experiments, we found that Y-box binding protein 1 (YBX1) was a crucial transcription factor for CDK8, which mediated the effect of CDK8 in the proliferation of porcine ovarian granulosa cells via transforming growth factor beta/small mother against decapentaplegic signaling pathway, and, as a consequence, affected embryo quality, indicating that this pathway may be contributing to mummified fetal in pigs. CONCLUSION: A powerful imputation-based association study was performed to identify genes associated with NM in pigs. CDK8 was suggested as a functional gene for the proliferation of porcine ovarian granulosa cells, but further studies are required to determine causative mutations and the effect of loci on NM in pigs.

Pituitary-Gland-Based Genes Participates in Intrauterine Growth Restriction in Piglets.

Intrauterine growth restriction (IUGR) is a major problem associated with piglet growth performance. The incidence of IUGR is widespread in Rongchang pigs. The pituitary gland is important for regulating growth and metabolism, and research has identified genes associated with growth and development. The pituitary gland of newborn piglets with normal birth weight (NBW group, n = 3) and (IUGR group, n = 3) was collected for transcriptome analysis. A total of 323 differentially expression genes (DEGs) were identified (|log2(fold-change)| > 1 and q value < 0.05), of which 223 were upregulated and 100 were downregulated. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that the DEGs were mainly related to the extracellular matrix, regulation of the multicellular organismal process, tissue development and angiogenesis, which participate in the growth and immune response in IUGR piglets. Moreover, 7 DEGs including IGF2, THBS1, ITGA1, ITGA8, EPSTI1, FOSB, and UCP2 were associated with growth and immune response. Furthermore, based on the interaction network analysis of the DEGs, two genes, IGF2 and THBS1, participated in cell proliferation, embryonic development and angiogenesis. IGF2 and THBS1 were also the main genes participating in the IUGR. This study identified the core genes involved in IUGR in piglets and provided a reference for exploring the effect of the pituitary gland on piglet growth.

Genome-wide association studies identified loci associated with both feed conversion ratio and residual feed intake in Yorkshire pigs.

Feed occupies a significant proportion in the production cost of pigs, and the feed efficiency (FE) in pigs is of utmost economic importance. Hence, the objective of this study is to identify single-nucleotide polymorphisms (SNPs) and candidate genes associated with FE-related traits, including feed conversion ratio (FCR) and residual feed intake (RFI). A genome-wide association study was conducted for FCR and RFI in 169 Yorkshire pigs using whole-genome sequencing data. A total of 23 and 33 suggestive significant SNPs (P < 1 × 10-6) were detected for FCR and RFI, respectively. However, none of SNPs achieved the genome-wide significance threshold (P < 5 × 10-8). Importantly, three common SNPs (SSC7:7987268, SSC13:42350250, and SSC13:42551718) were associated with both FCR and RFI. Additionally, the NEDD9 gene related to FCR and RFI traits was overlapped. This study detected novel SNPs on SSC7 and SSC13 common for FCR and RFI. These results provide new insights into the genetic mechanisms and candidate genes of FE-related traits in pigs.

Comparative metabolome profiling of serum and urine from sows with a high prevalence of piglet mummification and normal sows at different stages of pregnancy.

The number of live births is a critical indicator of the performance of sows and is also a significant factor in determining the economic benefits of pig breeding. Mummified piglets are an important challenge affecting production efficiency in the pig industry. However, the value of metabolomics in unraveling the mechanisms of piglet mummification has not yet been established. This study aimed to investigate the serum and urine metabolomes of sows to identify biomarkers of piglet mummification. During gestation (35th, 56th, 77th, and 98th), serum and urine samples were collected from eight pigs from each group. To assess changes in metabolite classes in serum and urine from sows with a high incidence of mummified piglets and normal sows, a combination of liquid chromatography-mass spectrometry-based metabolomics profiling approach was used. The identified metabolites were involved in taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, and bile secretion. A total of six potential markers related to piglet mummification were screened, including hypotaurine, taurodeoxycholic acid, taurocholic acid, arginine, glutamic acid, and proline. These metabolites are expected to be novel biomarkers of piglet mummification, although their use requires further validation.

Combining computer vision score and conventional meat quality traits to estimate the intramuscular fat content using machine learning in pigs.

Intramuscular fat content (IMF%) is an important factor that affects the quality of pork. The traditional testing method (Soxhlet extraction) is accurate; however, it has a long preprocessing time. In this study, a total of 1481 photographs of 200 pigs' loin muscles were used to obtain a computer vision score (IIMF %). Then, actual IMF%, meat color, marbling score, pH value, and drip loss of 200 pigs were measured. Stepwise regression (SR) and gradient boosting machine (GBM) were used to construct the estimation model of IMF%. The results showed that the correlation coefficients between IMF% and IIMF%, marbling score, backfat thickness, percentage of moisture (POM), and pH value were 0.68, 0.64, 0.48, 0.45, and 0.25, respectively. The model accuracies of SR and GBM base on residuals distribution were 0.875 and 0.89, respectively. This study presents a method for estimating IMF% using computer vision technology and meat quality traits.

Epigenome-wide association studies of meat traits in Chinese Yorkshire pigs highlights several DNA methylation loci and genes.

In this study, we aimed to identified CpG sites at which DNA methylation levels are associated with meat quality traits in 140 Yorkshire pigs, including pH at 45 min (pH45min), pH at 24 h (pH24h), drip loss (DL), meat redness value (a*), yellowness (b*) and lightness (L*). Genome-wide methylation levels were measured in muscular tissue using reduced representation bisulfite sequencing (RRBS). Associations between DNA methylation levels and meat quality traits were examined using linear mixed-effect models that were adjusted for gender, year, month and body weight. A Bonferroni-corrected p-value lower than 7.79 × 10 - 8 was considered statistically significant threshold. Eight CpG sites were associated with DL, including CpG sites annotated to RBM4 gene (cpg301054, cpg301055, cpg301058, cpg301059, cpg301066, cpg301072 and cpg301073) and NCAM1 gene (cpg1802985). Two CpG sites were associated with b*, including RNFT1 and MED13 (cpg2272837) and TRIM37 gene (cpg2270611). Five CpG sites were associated with L*, including GSDMA and LRRC3C gene (cpg2252750) and ENSSSCG00000043539 and IRX1 gene (cpg2820178, cpg2820179, cpg2820181 and cpg2820182). No significant associations were observed with pH45min, pH24h or a*. We reported associations of meat quality traits with DNA methylation and identified some candidate genes associated with these traits, such as NCAM1, MED13 and TRIM37 gene. These results provide new insight into the epigenetic molecular mechanisms of meat quality traits in pigs.

Detecting the selection signatures in Chinese Duroc,Landrace, Yorkshire, Liangshan, and Qingyu pigs.

Here we used two kinds of chips data from 5 pig breeds, Chinese Duroc (DD), Landrace (LL), Yorkshire (YY), Liangshan (LS), and Qingyu pigs (QY) in China to identify genes which show evidence of selection during domestication. Four breed pairs, LS-YY, QY-YY, DD-YY, and LL-YY pair, were performed to detect selection signatures using the Fst method. Then we identified a list of genes that played key roles in domestication and artificial selection. For example, the PTPRM gene was shared in LS-YY, QY-YY, and DD-YY pairs and it regulates a variety of cellular processes including cell growth, differentiation as signaling molecules. The HACD3 gene was shared in QY-YY and DD-YY pairs, and the HACD3 protein is involved in the production of very long-chain fatty acids of different chain lengths. Besides, the MYH11 gene that related to muscle contraction was found in LS-YY and LL-YY pair. These results suggested that genes related to immunity, disease resistance, and metabolism were subjected to strong selection pressure in Chinese domestic pigs in the progress of domestication and evolution; however, genes related to appearance, production performance, and reproduction were undergone strong artificial selection in commercial pig breeds.

A combined GWAS approach reveals key loci for socially-affected traits in Yorkshire pigs.

Socially affected traits in pigs are controlled by direct genetic effects and social genetic effects, which can make elucidation of their genetic architecture challenging. We evaluated the genetic basis of direct genetic effects and social genetic effects by combining single-locus and haplotype-based GWAS on imputed whole-genome sequences. Nineteen SNPs and 25 haplotype loci are identified for direct genetic effects on four traits: average daily feed intake, average daily gain, days to 100 kg and time in feeder per day. Nineteen SNPs and 11 haplotype loci are identified for social genetic effects on average daily feed intake, average daily gain, days to 100 kg and feeding speed. Two significant SNPs from single-locus GWAS (SSC6:18,635,874 and SSC6:18,635,895) are shared by a significant haplotype locus with haplotype alleles 'GGG' for both direct genetic effects and social genetic effects in average daily feed intake. A candidate gene, MT3, which is involved in growth, nervous, and immune processes, is identified. We demonstrate the genetic differences between direct genetic effects and social genetic effects and provide an anchor for investigating the genetic architecture underlying direct genetic effects and social genetic effects on socially affected traits in pigs.

Differential DNA methylation analysis reveals key genes in Chinese Qingyu and Landrace pigs.

The Chinese Qingyu pig is a typical domestic fatty pig breed and an invaluable indigenous genetic resource in China. Compared with the Landrace pig, the Qingyu pig has unique meat characteristics, including muscle development, intramuscular fat, and other meat quality traits. At present, few studies have explored epigenetic differences due to DNA methylation between the Qingyu pig and the Landrace pig. In this study, 30 Qingyu pigs and 31 Landrace pigs were subjected to reduced representation bisulfite sequencing (RRBS). Genome-wide differential DNA methylation analysis was conducted. Six genomic regions, including regions on Sus scrofa chromosome (SSC) 1: 266.09-274.23 Mb, SSC5: 0.88-10.68 Mb, SSC8: 41.23-48.51 Mb, SSC12: 45.43-54.38 Mb, SSC13: 202.15-207.95 Mb, and SSC14: 126.43-139.85 Mb, were regarded as key regions that may be associated with phenotypic differences between the Qingyu pig and the Landrace pig. Furthermore, according to further analysis, five differentially methylated genes (ADCY1, FUBP3, GRIN2B, KIT, and PIK3R6) were identified as key candidate genes that might be associated with meat characteristics. Our findings provide new insights into the differences in DNA methylation between the Qingyu pig and the Landrace pig. These results enrich the epigenetic research of the Chinese Qingyu pig.

Genome-wide DNA methylation analysis in Chinese Chenghua and Yorkshire pigs.

BACKGROUND: The Chinese Chenghua pig (CHP) is a typical Chinese domestic fatty pig breed with superior meat quality characteristics, while the Yorkshire pig (YP) has the characteristics of fast growth and a high rate of lean meat. Long term natural selection and artificial selection resulted in great phenotypic differences between the two breeds, including growth, development, production performance, meat quality, and coat color. However, genome-wide DNA methylation differences between CHP and YP remain unclear. RESULTS: DNA methylation data were generated for muscle tissues of CHP and YP using reduced representation bisulfite sequencing (RRBS). In this study, a total of 2,416,211 CpG sites were identified. Besides, the genome-wide DNA methylation analysis revealed 722 differentially methylated regions (DMRs) and 466 differentially methylated genes (DMGs) in pairwise CHP vs. YP comparison. Six key genomic regions (Sus scrofa chromosome (SSC)1:253.47-274.23 Mb, SSC6:148.71-169.49 Mb, SSC7:0.25-9.86 Mb, SSC12:43.06-61.49 Mb, SSC14:126.43-140.95 Mb, and SSC18:49.17-54.54 Mb) containing multiple DMRs were identified, and differences of methylation patterns in these regions may be related to phenotypic differences between CHP and YP. Based on the functional analysis of DMGs, 8 DMGs (ADCY1, AGBL4, EXOC2, FUBP3, PAPPA2, PIK3R1, MGMT and MYH8) were considered as important candidate genes associated with muscle development and meat quality traits in pigs. CONCLUSIONS: This study explored the difference in meat quality between CHP and YP from the epigenetic point of view, which has important reference significance for the local pork industry and pork food processing.

Transcriptome analysis reveals key genes and pathways associated with piglet fetal mummification.

China has the largest pork consumption worldwide. However, the high incidence of piglet fetal mummification (3%-5%) is an important factor that causes the slow improvement of pig reproductive capacity, and the genetic mechanism is still unclear. This study aimed to identify candidate genes associated with piglet fetal mummification. RNA-seq technology was used to compare transcriptome profiling of blood from healthy and mummified piglets at different stages of pregnancy (35, 56, 77, and 98 days). A total of 137-420 differentially expressed genes (DEGs) were detected at each stage. Seven DEGs were significantly differentially expressed at various stages. IL-9R, TLR8, ABLIM3, FSH-α, ASCC1, PRKCZ, and GCK may play important roles in the course of piglet fetal mummification. The differential genes we identified between the groups were mainly enriched in immune and inflammation regulation, while others were mainly enriched in reproduction. Considering the function of candidate genes, IL-9R and TLR8 were suggested as the most promising candidate genes involved in mummified piglet traits. We speculate that during pregnancy, it may be the combined effects of the above-mentioned inflammation, immune response, and reproduction-related signaling pathways that affect the occurrence of mummified piglets and further affect pig reproduction.

Whole-genome sequencing association analysis reveals the genetic architecture of meat quality traits in Chinese Qingyu pigs.

The Chinese Qingyu pig breed is an invaluable indigenous genetic resource. However, few studies have investigated the genetic architecture of meat quality traits in Qingyu pigs. Here, 30 purebred Qingyu pigs were subjected to whole-genome sequencing. After quality control, 18 436 759 SNPs were retained. Genome-wide association studies (GWAS) were then performed for meat pH and color at three postmortem time points (45 min, 24 h, and 48 h) using single-marker regression analysis. In total, 11 and 69 SNPs were associated with meat pH and color of the longissimus thoracis muscle (LTM), respectively, while 54 and 29 SNPs were associated with meat pH and color of the semimembranosus muscle (SM), respectively. Seven SNPs associated with pork pH were shared by all three postmortem time points. Several candidate genes for meat traits were identified, including four genes (CXXC5, RYR3, BNIP3, and MYCT1) related to skeletal muscle development, regulation of Ca2+ release in the muscle, and anaerobic respiration, which are promising candidates for selecting superior meat quality traits in Qingyu pigs. To our knowledge, this is the first study investigating the postmortem genetic architecture of pork pH and color in Qingyu pigs. Our findings further the current understanding of the genetic factors influencing meat quality.

GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs.

The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R 2) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.

Whole-genome re-sequencing association study for direct genetic effects and social genetic effects of six growth traits in Large White pigs.

Socially affected traits are affected by direct genetic effects (DGE) and social genetic effects (SGE). DGE and SGE of an individual directly quantify the genetic influence of its own phenotypes and the phenotypes of other individuals, respectively. In the current study, a total of 3,276 Large White pigs from different pens were used, and each pen contained 10 piglets. DGE and SGE were estimated for six socially affected traits, and then a GWAS was conducted to identify SNPs associated with DGE and SGE. Based on the whole-genome re-sequencing, 40 Large White pigs were genotyped and 10,501,384 high quality SNPs were retained for single-locus and multi-locus GWAS. For single-locus GWAS, a total of 54 SNPs associated with DGE and 33 SNPs with SGE exceeded the threshold (P < 5.00E-07) were detected for six growth traits. Of these, 22 SNPs with pleiotropic effects were shared by DGE and SGE. For multi-locus GWAS, a total of 72 and 110 putative QTNs were detected for DGE and SGE, respectively. Of these, 5 SNPs with pleiotropic effects were shared by DGE and SGE. It is noteworthy that 2 SNPs (SSC8: 16438396 for DGE and SSC17: 9697454 for SGE) were detected in single-locus and multi-locus GWAS. Furthermore, 15 positional candidate genes shared by SGE and DGE were identified because of their roles in behaviour, health and disease. Identification of genetic variants and candidate genes for DGE and SGE for socially affected traits will provide a new insight to understand the genetic architecture of socially affected traits in pigs.