Haplotype Block Analysis Reveals Candidate Genes and QTLs for Meat Quality and Disease Resistance in Chinese Jiangquhai Pig Breed

Oyelami, Favour Oluwapelumi; Zhao, Qi; Xu, Zhong; Zhang, Zhe; Sun, Hao; Zhang, Zhenyang; Ma, Peipei; Wang, Qishan; Pan, Yuchun

doi:10.3389/fgene.2020.00752

Cited by 12 publications

(4 citation statements)

References 102 publications

(121 reference statements)

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“…While the relationship between UNC93A (unc-93 homolog A) and the lipid metabolism in muscle has yet to be explored, a study in mice determined that the expression of this putative solute carrier responded to nutrient availability [ 52 ]. In addition, UNC93A was also mentioned as a candidate gene in a quantitative trait loci (QTL) study for meat quality and disease resistance in the Chinese Jiangquhai pig breed [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis

Valdés-Hernández,

Folch,

Crespo-Piazuelo

et al. 2024

Genet Sel Evol

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Background Intramuscular fat (IMF) content and its fatty acid (FA) composition are typically controlled by several genes, each with a small effect. In the current study, to pinpoint candidate genes and putative regulators involved in FA composition, we performed a multivariate integrative analysis between intramuscular FA and transcriptome profiles of porcine longissimus dorsi (LD) muscle. We also carried out a combination of network, regulatory impact factor (RIF), in silico prediction of putative target genes, and functional analyses to better support the biological relevance of our findings. Results For this purpose, we used LD RNA-Seq and intramuscular FA composition profiles of 129 Iberian × Duroc backcrossed pigs. We identified 378 correlated variables (13 FA and 365 genes), including six FA (C20:4n-6, C18:2n-6, C20:3n-6, C18:1n-9, C18:0, and C16:1n-7) that were among the most interconnected variables in the predicted network. The detected FA-correlated genes include genes involved in lipid and/or carbohydrate metabolism or in regulation of IMF deposition (e.g., ADIPOQ, CHUK, CYCS, CYP4B1, DLD, ELOVL6, FBP1, G0S2, GCLC, HMGCR, IDH3A, LEP, LGALS12, LPIN1, PLIN1, PNPLA8, PPP1R1B, SDR16C5, SFRP5, SOD3, SNW1, and TFRC), meat quality (GALNT15, GOT1, MDH1, NEU3, PDHA1, SDHD, and UNC93A), and transport (e.g., EXOC7 and SLC44A2). Functional analysis highlighted 54 over-represented gene ontology terms, including well-known biological processes and pathways that regulate lipid and carbohydrate metabolism. RIF analysis suggested a pivotal role for six transcription factors (CARHSP1, LBX1, MAFA, PAX7, SIX5, and TADA2A) as putative regulators of gene expression and intramuscular FA composition. Based on in silico prediction, we identified putative target genes for these six regulators. Among these, TADA2A and CARHSP1 had extreme RIF scores and present novel regulators in pigs. In addition, the expression of TADA2A correlated (either positively or negatively) with C20:4n-6, C18:2n-6, C20:3n-6, C18:1n-9, and that of CARHSP1 correlated (positively) with the C16:1n-7 lipokine. We also found that these two transcription factors share target genes that are involved in lipid metabolism (e.g., GOT1, PLIN1, and TFRC). Conclusions This integrative analysis of muscle transcriptome and intramuscular FA profile revealed valuable information about key candidate genes and potential regulators for FA and lipid metabolism in pigs, among which some transcription factors are proposed to control gene expression and modulate FA composition differences.

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Section: Discussionmentioning

confidence: 99%

Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis

Valdés-Hernández,

Folch,

Crespo-Piazuelo

et al. 2024

Genet Sel Evol

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“…Genome-wide association analysis (GWAS) is a comprehensive approach that explores genetic variation—particularly single nucleotide polymorphisms (SNPs)—across the entire genome to identify variants potentially associated with specific traits. In livestock breeding, GWAS serves as a valuable tool for identifying economically important traits such as growth, carcass [ 5 , 6 , 7 ], reproduction [ 8 , 9 ], immunity [ 10 , 11 ] and flesh quality [ 12 , 13 ], among other candidate genes studied. While GWAS is commonly employed to identify genetic markers associated with complex traits in livestock, it has some limitations, such as low reproducibility [ 14 ] and a high incidence of false positives [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs

Zhou

Yin

Wang

et al. 2023

Genes

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The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations—500 Canadian, 295 Danish, and 1500 American large white pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as CNTN1—which has been linked to weight loss in mice and is potentially influential for AGE100—and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes—namely, PDZRN4, LIPM, and ANKRD22—which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality.

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“…Genome-wide association studies (GWAS) have emerged as an efficient method for dissecting the genetic mechanisms of complex traits. Recently, GWAS have been widely employed to identify the candidate genes on traits in pigs, such as growth [ 7 , 8 , 9 ], reproduction [ 10 , 11 , 12 ], meat quality [ 13 , 14 , 15 ], and disease resistance [ 16 , 17 ]. The variant detection power of GWAS is affected by marker density.…”

Section: Introductionmentioning

confidence: 99%

GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data

Wang

Shi

et al. 2022

IJMS

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Total number born (TNB), number of stillborn (NSB), and gestation length (GL) are economically important traits in pig production, and disentangling the molecular mechanisms associated with traits can provide valuable insights into their genetic structure. Genotype imputation can be used as a practical tool to improve the marker density of single-nucleotide polymorphism (SNP) chips based on sequence data, thereby dramatically improving the power of genome-wide association studies (GWAS). In this study, we applied Beagle software to impute the 50 K chip data to the whole-genome sequencing (WGS) data with average imputation accuracy (R2) of 0.876. The target pigs, 2655 Large White pigs introduced from Canadian and French lines, were genotyped by a GeneSeek Porcine 50K chip. The 30 Large White reference pigs were the key ancestral individuals sequenced by whole-genome resequencing. To avoid population stratification, we identified genetic variants associated with reproductive traits by performing within-population GWAS and cross-population meta-analyses with data before and after imputation. Finally, several genes were detected and regarded as potential candidate genes for each of the traits: for the TNB trait: NOTCH2, KLF3, PLXDC2, NDUFV1, TLR10, CDC14A, EPC2, ORC4, ACVR2A, and GSC; for the NSB trait: NUB1, TGFBR3, ZDHHC14, FGF14, BAIAP2L1, EVI5, TAF1B, and BCAR3; for the GL trait: PPP2R2B, AMBP, MALRD1, HOXA11, and BICC1. In conclusion, expanding the size of the reference population and finding an optimal imputation strategy to ensure that more loci are obtained for GWAS under high imputation accuracy will contribute to the identification of causal mutations in pig breeding.

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Haplotype Block Analysis Reveals Candidate Genes and QTLs for Meat Quality and Disease Resistance in Chinese Jiangquhai Pig Breed

Cited by 12 publications

References 102 publications

Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis

Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis

Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs

GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data

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