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Updated: Oct 14, 2025

A Guide to Examining Intramuscular Fat Formation and its Cellular Origin in Skeletal Muscle
Published on: May 26, 2022
Trait correlated expression combined with eQTL and ASE analyses identified novel candidate genes affecting
Yan Liu1,2,3, Huan Long1,2, Simin Feng1,2
1Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
Background:
Intramuscular fat (IMF) content is a determining factor for meat taste. The Luchuan pig is a fat-type local breed in southern China that is famous for its desirable meat quality due to high IMF, however, the crossbred offspring of Luchuan sows and Duroc boars displayed within-population variation on meat quality, and the reason remains unknown.
Results:
In the present study, we identified 212 IMF-correlated genes (FDR ≤ 0.01) using correlation analysis between gene expression level and the value of IMF content. The IMF-correlated genes were significantly enriched in the processes of lipid metabolism and mitochondrial energy metabolism, as well as the AMPK/PPAR signaling pathway. From the IMF-correlated genes, we identified 99 genes associated with expression quantitative trait locus (eQTL) or allele-specific expression (ASE) signals, including 21 genes identified by both cis-eQTL and ASE analyses and 12 genes identified by trans-eQTL analysis. Genome-wide association study (GWAS) of IMF identified a significant QTL on SSC14 (p-value = 2.51E-7), and the nearest IMF-correlated gene SFXN4 (r = 0.28, FDR = 4.00E-4) was proposed as the candidate gene. Furthermore, we highlighted another three novel IMF candidate genes, namely AGT, EMG1, and PCTP, by integrated analysis of GWAS, eQTL, and IMF-gene correlation analysis.
Conclusions:
The AMPK/PPAR signaling pathway together with the processes of lipid and mitochondrial energy metabolism plays a vital role in regulating porcine IMF content. Trait correlated expression combined with eQTL and ASE analysis highlighted a priority list of genes, which compensated for the shortcoming of GWAS, thereby accelerating the mining of causal genes of IMF.
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