The feed conversion ratio (FCR) is an important productive trait that greatly affects profits in the pig industry. Elucidating the genetic mechanisms underpinning FCR may promote more efficient ...improvement of FCR through artificial selection. In this study, we integrated a genome-wide association study (GWAS) with transcriptome analyses of different tissues in Yorkshire pigs (YY) with the aim of identifying key genes and signalling pathways associated with FCR.
A total of 61 significant single nucleotide polymorphisms (SNPs) were detected by GWAS in YY. All of these SNPs were located on porcine chromosome (SSC) 5, and the covered region was considered a quantitative trait locus (QTL) region for FCR. Some genes distributed around these significant SNPs were considered as candidates for regulating FCR, including TPH2, FAR2, IRAK3, YARS2, GRIP1, FRS2, CNOT2 and TRHDE. According to transcriptome analyses in the hypothalamus, TPH2 exhibits the potential to regulate intestinal motility through serotonergic synapse and oxytocin signalling pathways. In addition, GRIP1 may be involved in glutamatergic and GABAergic signalling pathways, which regulate FCR by affecting appetite in pigs. Moreover, GRIP1, FRS2, CNOT2, and TRHDE may regulate metabolism in various tissues through a thyroid hormone signalling pathway.
Based on the results from GWAS and transcriptome analyses, the TPH2, GRIP1, FRS2, TRHDE, and CNOT2 genes were considered candidate genes for regulating FCR in Yorkshire pigs. These findings improve the understanding of the genetic mechanisms of FCR and may help optimize the design of breeding schemes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The lack of integrated analysis of genome-wide association studies (GWAS) and 3D epigenomics restricts a deep understanding of the genetic mechanisms of meat-related traits. With the application of ...techniques as ChIP-seq and Hi-C, the annotations of cis-regulatory elements in the pig genome have been established, which offers a new opportunity to elucidate the genetic mechanisms and identify major genetic variants and candidate genes that are significantly associated with important economic traits. Among these traits, loin muscle depth (LMD) is an important one as it impacts the lean meat content. In this study, we integrated cis-regulatory elements and genome-wide association studies (GWAS) to identify candidate genes and genetic variants regulating LMD.
Five single nucleotide polymorphisms (SNPs) located on porcine chromosome 17 were significantly associated with LMD in Yorkshire pigs. A 10 kb quantitative trait locus (QTL) was identified as a candidate functional genomic region through the integration of linkage disequilibrium and linkage analysis (LDLA) and high-throughput chromosome conformation capture (Hi-C) analysis. The BMP2 gene was identified as a candidate gene for LMD based on the integrated results of GWAS, Hi-C meta-analysis, and cis-regulatory element data. The identified QTL region was further verified through target region sequencing. Furthermore, through using dual-luciferase assays and electrophoretic mobility shift assays (EMSA), two SNPs, including SNP rs321846600, located in the enhancer region, and SNP rs1111440035, located in the promoter region, were identified as candidate SNPs that may be functionally related to the LMD.
Based on the results of GWAS, Hi-C, and cis-regulatory elements, the BMP2 gene was identified as an important candidate gene regulating variation in LMD. The SNPs rs321846600 and rs1111440035 were identified as candidate SNPs that are functionally related to the LMD of Yorkshire pigs. Our results shed light on the advantages of integrating GWAS with 3D epigenomics in identifying candidate genes for quantitative traits. This study is a pioneering work for the identification of candidate genes and related genetic variants regulating one key production trait (LMD) in pigs by integrating genome-wide association studies and 3D epigenomics.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The single-step genomic best linear unbiased prediction (SSGBLUP) method is a popular approach for genetic evaluation with high-density genotype data. To solve the problem that pedigree and genomic ...relationship matrices refer to different base populations, a single-step genomic method with metafounders (MF-SSGBLUP) was put forward. The aim of this study was to compare the predictive ability and bias of genomic evaluations obtained with MF-SSGBLUP and standard SSGBLUP. We examined feed conversion ratio (FCR) and average daily gain (ADG) in DanBred Landrace (LL) and Yorkshire (YY) pigs using both univariate and bivariate models, as well as the optimal weighting factors (omega), which represent the proportions of the genetic variance not captured by markers, for ADG and FCR in SSGBLUP and MF-SSGBLUP. In general, SSGBLUP and MF-SSGBLUP showed similar predictive abilities and bias of genomic estimated breeding values (GEBV). In the LL population, the predictive ability for ADG reached 0.36 using uni- or bi-variate SSGBLUP or MF-SSGBLUP, while the predictive ability for FCR was highest (0.20) for the bivariate model using MF-SSGBLUP, but differences between analyses were very small. In the YY population, predictive ability for ADG was similar for the four analyses (up to 0.35), while the predictive ability for FCR was highest (0.36) for the uni- and bi-variate MF-SSGBLUP analyses. SSGBLUP and MF-SSGBLUP exhibited nearly the same bias. In general, the bivariate models had lower bias than the univariate models. In the LL population, the optimal omega for ADG was ~ 0.2 in the univariate or bivariate models using SSGBLUP or MF-SSGBLUP, and the optimal omega for FCR was 0.70 and 0.55 for SSGBLUP and MF-SSGBLUP, respectively. In the YY population, the optimal omega ranged from 0.25 to 0. 35 for ADG across the four analyses and from 0.10 to 0.30 for FCR. Our results indicate that MF-SSGBLUP performed slightly better than SSGBLUP for genomic evaluation. There was little difference in the optimal weighting factors (omega) between SSGBLUP and MF-SSGBLUP. Overall, the bivariate model using MF-SSGBLUP is recommended for single-step genomic evaluation of ADG and FCR in DanBred Landrace and Yorkshire pigs.
Feed cost is the main factor affecting the economic benefits of pig industry. Improving the feed efficiency (FE) can reduce the feed cost and improve the economic benefits of pig breeding ...enterprises. Liver is a complex metabolic organ which affects the distribution of nutrients and regulates the efficiency of energy conversion from nutrients to muscle or fat, thereby affecting feed efficiency. MicroRNAs (miRNAs) are small non-coding RNAs that can regulate feed efficiency through the modulation of gene expression at the post-transcriptional level. In this study, we analyzed miRNA profiling of liver tissues in High-FE and Low-FE pigs for the purpose of identifying key miRNAs related to feed efficiency. A total 212~221 annotated porcine miRNAs and 136~281 novel miRNAs were identified in the pig liver. Among them, 188 annotated miRNAs were co-expressed in High-FE and Low-FE pigs. The 14 miRNAs were significantly differentially expressed (DE) in the livers of high-FE pigs and low-FE pigs, of which 5 were downregulated and 9 were upregulated. Kyoto Encyclopedia of Genes and Genomes analysis of liver DE miRNAs in high-FE pigs and low-FE pigs indicated that the target genes of DE miRNAs were significantly enriched in insulin signaling pathway, Gonadotropin-releasing hormone signaling pathway, and mammalian target of rapamycin signaling pathway. To verify the reliability of sequencing results, 5 DE miRNAs were randomly selected for quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The qRT-PCR results of miRNAs were confirmed to be consistent with sequencing data. DE miRNA data indicated that liver-specific miRNAs synergistically acted with mRNAs to improve feed efficiency. The liver miRNAs expression analysis revealed the metabolic pathways by which the liver miRNAs regulate pig feed efficiency.
Feed cost is the main factor affecting the economic benefits of pig industry. Improving the feed efficiency (FE) can reduce the feed cost and improve the economic benefits of pig breeding ...enterprises. Liver is a complex metabolic organ which affects the distribution of nutrients and regulates the efficiency of energy conversion from nutrients to muscle or fat, thereby affecting feed efficiency. MicroRNAs (miRNAs) are small non-coding RNAs that can regulate feed efficiency through the modulation of gene expression at the post-transcriptional level. In this study, we analyzed miRNA profiling of liver tissues in High-FE and Low-FE pigs for the purpose of identifying key miRNAs related to feed efficiency. A total 212~221 annotated porcine miRNAs and 136~281 novel miRNAs were identified in the pig liver. Among them, 188 annotated miRNAs were co-expressed in High-FE and Low-FE pigs. The 14 miRNAs were significantly differentially expressed (DE) in the livers of high-FE pigs and low-FE pigs, of which 5 were downregulated and 9 were upregulated. Kyoto Encyclopedia of Genes and Genomes analysis of liver DE miRNAs in high-FE pigs and low-FE pigs indicated that the target genes of DE miRNAs were significantly enriched in insulin signaling pathway, Gonadotropin-releasing hormone signaling pathway, and mammalian target of rapamycin signaling pathway. To verify the reliability of sequencing results, 5 DE miRNAs were randomly selected for quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The qRT-PCR results of miRNAs were confirmed to be consistent with sequencing data. DE miRNA data indicated that liver-specific miRNAs synergistically acted with mRNAs to improve feed efficiency. The liver miRNAs expression analysis revealed the metabolic pathways by which the liver miRNAs regulate pig feed efficiency.
Abstract
Semen traits are crucial in commercial pig production since semen from boars is widely used in artificial insemination for both purebred and crossbred pig production. Revealing the genetic ...architecture of semen traits potentially promotes the efficiencies of improving semen traits through artificial selection. This study is aimed to identify candidate genes related to the semen traits in Duroc boars. First, we identified the genes that were significantly associated with three semen traits, including sperm motility (MO), sperm concentration (CON), and semen volume (VOL) in a Duroc boar population through a genome-wide association study (GWAS). Second, we performed a weighted gene co-expression network analysis (WGCNA). A total of 2, 3, and 20 single-nucleotide polymorphisms were found to be significantly associated with MO, CON, and VOL, respectively. Based on the haplotype block analysis, we identified one genetic region associated with MO, which explained 6.15% of the genetic trait variance. ENSSSCG00000018823 located within this region was considered as the candidate gene for regulating MO. Another genetic region explaining 1.95% of CON genetic variance was identified, and, in this region, B9D2, PAFAH1B3, TMEM145, and CIC were detected as the CON-related candidate genes. Two genetic regions that accounted for 2.23% and 2.48% of VOL genetic variance were identified, and, in these two regions, WWC2, CDKN2AIP, ING2, TRAPPC11, STOX2, and PELO were identified as VOL-related candidate genes. WGCNA analysis showed that, among these candidate genes, B9D2, TMEM145, WWC2, CDKN2AIP, TRAPPC11, and PELO were located within the most significant module eigengenes, confirming these candidate genes’ role in regulating semen traits in Duroc boars. The identification of these candidate genes can help to better understand the genetic architecture of semen traits in boars. Our findings can be applied for semen traits improvement in Duroc boars.
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