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Genome-wide analysis of expression QTL (eQTL) and allele-specific expression (ASE) in pig muscle identifies candidate genes for meat quality traits

Authors :
Yan Liu
Xiaolei Liu
Zhiwei Zheng
Tingting Ma
Ying Liu
Huan Long
Huijun Cheng
Ming Fang
Jing Gong
Xinyun Li
Shuhong Zhao
Xuewen Xu
Source :
Genetics Selection Evolution, Vol 52, Iss 1, Pp 1-11 (2020)
Publication Year :
2020
Publisher :
BMC, 2020.

Abstract

Abstract Background Genetic analysis of gene expression level is a promising approach for characterizing candidate genes that are involved in complex economic traits such as meat quality. In the present study, we conducted expression quantitative trait loci (eQTL) and allele-specific expression (ASE) analyses based on RNA-sequencing (RNAseq) data from the longissimus muscle of 189 Duroc × Luchuan crossed pigs in order to identify some candidate genes for meat quality traits. Results Using a genome-wide association study based on a mixed linear model, we identified 7192 cis-eQTL corresponding to 2098 cis-genes (p ≤ 1.33e-3, FDR ≤ 0.05) and 6400 trans-eQTL corresponding to 863 trans-genes (p ≤ 1.13e-6, FDR ≤ 0.05). ASE analysis using RNAseq SNPs identified 9815 significant ASE-SNPs in 2253 unique genes. Integrative analysis between the cis-eQTL and ASE target genes identified 540 common genes, including 33 genes with expression levels that were correlated with at least one meat quality trait. Among these 540 common genes, 63 have been reported previously as candidate genes for meat quality traits, such as PHKG1 (q-value = 1.67e-6 for the leading SNP in the cis-eQTL analysis), NUDT7 (q-value = 5.67e-13), FADS2 (q-value = 8.44e-5), and DGAT2 (q-value = 1.24e-3). Conclusions The present study confirmed several previously published candidate genes and identified some novel candidate genes for meat quality traits via eQTL and ASE analyses, which will be useful to prioritize candidate genes in further studies.

Details

Language :
German, English, French
ISSN :
12979686
Volume :
52
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Genetics Selection Evolution
Publication Type :
Academic Journal
Accession number :
edsdoj.7142cc8815c340aa93b3862b77c16a04
Document Type :
article
Full Text :
https://doi.org/10.1186/s12711-020-00579-x