Dataset Information

An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs

ABSTRACT: Understanding the genetic factors behind meat quality traits is of great significance to animal breeding and production. We previously conducted a genome-wide association study (GWAS) for meat quality traits in a White Duroc × Erhualian F2 pig population using Illumina porcine 60K SNP data. Here, we further investigate the functional candidate genes and their network modules associated with meat quality traits by integrating transcriptomics and GWAS information. Quantitative trait transcript (QTT) analysis, gene expression QTL (eQTL) mapping, and weighted gene co-expression network analysis (WGCNA) were performed using the digital gene expression (DGE) data from 493 F2 pig’s muscle and liver samples. Among the quantified 20,108 liver and 23,728 muscle transcripts, 535 liver and 1,014 muscle QTTs corresponding to 416 and 721 genes, respectively, were found to be significantly (p < 5 × 10−4) correlated with 22 meat quality traits measured on longissiums dorsi muscle (LM) or semimembranosus muscle (SM). Transcripts associated with muscle glycolytic potential (GP) and pH values were enriched for genes involved in metabolic process. There were 42 QTTs (for 32 genes) shared by liver and muscle tissues, of which 10 QTTs represent GP- and/or pH-related genes, such as JUNB, ATF3, and PPP1R3B. Furthermore, a genome-wide eQTL mapping revealed a total of 3,054 eQTLs for all annotated transcripts in muscle (p < 2.08 × 10−5), including 1,283 cis-eQTLs and 1771 trans-eQTLs. In addition, WGCNA identified five modules relevant to glycogen metabolism pathway and highlighted the connections between variations in meat quality traits and genes involved in energy process. Integrative analysis of GWAS loci, eQTL, and QTT demonstrated GALNT15/GALNTL2 and HTATIP2 as strong candidate genes for drip loss and pH drop from postmortem 45 min to 24 h, respectively. Our findings provide valuable insights into the genetic basis of meat quality traits and greatly expand the number of candidate genes that may be valuable for future functional analysis and genetic improvement of meat quality.

SUBMITTER: Liu X

PROVIDER: S-EPMC8567094 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Simple Summary Due to the complexity of sperm cell reproduction and maturation, the genetic structure of semen traits remains largely unknown. In our study, we used weighted single-step GWAS to detect genetic regions and further candidate genes related to semen traits in Duroc boars. This study provides in-depth understanding of the genetic structure of semen traits and the biological information provided by gene networks, and can be applied to speed up the genetic process of semen traits in boars. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. Abstract Semen traits play a key role in the pig industry because boar semen is widely used in purebred and crossbred pigs. The production of high-quality semen is crucial to ensuring a good result in artificial insemination. With the wide application of artificial insemination in the pig industry, more and more attention has been paid to the improvement of semen traits by genetic selection. The purpose of this study was to identify the genetic regions and candidate genes associated with semen traits of Duroc boars. We used weighted single-step GWAS to identify candidate genes associated with sperm motility, sperm progressive motility, sperm abnormality rate and total sperm count in Duroc pigs. In Duroc pigs, the three most important windows for sperm motility—sperm progressive motility, sperm abnormality rate, and total sperm count—explained 12.45%, 9.77%, 15.80%, and 12.15% of the genetic variance, respectively. Some genes that are reported to be associated with spermatogenesis, testicular function and male fertility in mammals have been detected previously. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. We found a common candidate gene, STRA8, in sperm motility and sperm progressive motility, and common candidate genes ZSWIM7, TEKT3 and UBB in sperm motility and sperm abnormality rate, which confirms the hypothesis of gene pleiotropy. Gene network enrichment analysis showed that STRA8, UBB and CATSPER1 were enriched in the common biological process and participated in male meiosis and spermatogenesis. The SNPs of candidate genes can be given more weight in genome selection to improve the ability of genome prediction. This study provides further insight into the understanding the genetic structure of semen traits in Duroc boars.

Project description:BackgroundIn recent years, there has been increased interest in the study of the molecular processes that affect semen traits. In this study, our aim was to identify quantitative trait loci (QTL) regions associated with four semen traits (motility, progressive motility, number of sperm cells per ejaculate and total morphological defects) in two commercial pig lines (L1: Large White type and L2: Landrace type). Since the number of animals with both phenotypes and genotypes was relatively small in our dataset, we conducted a weighted single-step genome-wide association study, which also allows unequal variances for single nucleotide polymorphisms. In addition, our aim was also to identify candidate genes within QTL regions that explained the highest proportions of genetic variance. Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same semen traits across lines.ResultsWe identified QTL regions that explained up to 10.8% of the genetic variance of the semen traits on 12 chromosomes in L1 and 11 chromosomes in L2. Sixteen QTL regions in L1 and six QTL regions in L2 were associated with two or more traits within the population. Candidate genes SCN8A, PTGS2, PLA2G4A, DNAI2, IQCG and LOC102167830 were identified in L1 and NME5, AZIN2, SPATA7, METTL3 and HPGDS in L2. No regions overlapped between these two lines. However, the gene network analysis for progressive motility revealed two genes in L1 (PLA2G4A and PTGS2) and one gene in L2 (HPGDS) that were involved in two biological processes i.e. eicosanoid biosynthesis and arachidonic acid metabolism. PTGS2 and HPGDS were also involved in the cyclooxygenase pathway.ConclusionsWe identified several QTL regions associated with semen traits in two pig lines, which confirms the assumption of a complex genetic determinism for these traits. A large part of the genetic variance of the semen traits under study was explained by different genes in the two evaluated lines. Nevertheless, the gene network analysis revealed candidate genes that are involved in shared biological pathways that occur in mammalian testes, in both lines.

Project description:The alteration in skeletal muscle fiber is a critical factor affecting livestock meat quality traits and human metabolic diseases. Long non-coding RNAs (lncRNAs) are a diverse class of non-coding RNAs with a length of more than 200 nucleotides. However, the mechanisms underlying the regulation of lncRNAs in skeletal muscle fibers remain elusive. To understand the genetic basis of lncRNA-regulated skeletal muscle fiber development, we performed a transcriptome analysis to identify the key lncRNAs affecting skeletal muscle fiber and meat quality traits on a pig model. We generated the lncRNA expression profiles of fast-twitch Biceps femoris (Bf) and slow-twitch Soleus (Sol) muscles and identified the differentially expressed (DE) lncRNAs using RNA-seq and performed bioinformatics analyses. This allowed us to identify 4581 lncRNA genes among six RNA libraries and 92 DE lncRNAs between Bf and Sol which are the key candidates for the conversion of skeletal muscle fiber types. Moreover, we detected the expression patterns of lncRNA MSTRG.42019 in different tissues and skeletal muscles of various development stages. In addition, we performed a correlation analyses between the expression of DE lncRNA MSTRG.42019 and meat quality traits. Notably, we found that DE lncRNA MSTRG.42019 was highly expressed in skeletal muscle and its expression was significantly higher in Sol than in Bf, with a positive correlation with the expression of Myosin heavy chain 7 (MYH7) (r = 0.6597, p = 0.0016) and a negative correlation with meat quality traits glycolytic potential (r = -0.5447, p = 0.0130), as well as drip loss (r = -0.5085, p = 0.0221). Moreover, we constructed the lncRNA MSTRG.42019-mRNAs regulatory network for a better understanding of a possible mechanism regulating skeletal muscle fiber formation. Our data provide the groundwork for studying the lncRNA regulatory mechanisms of skeletal muscle fiber conversion, and given the importance of skeletal muscle fiber types in muscle-related diseases, our data may provide insight into the treatment of muscular diseases in humans.

Dataset Information

An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets