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.

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.

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.