Abstract
Feed efficiency (FE) is a key trait in pig production, as improvement in FE has positive economic and environmental impact. FE is a complex phenotype and testing animals for FE is costly. Therefore, there has been a desire to find functionally relevant single nucleotide polymorphisms (SNPs) as biomarkers, to improve our biological understanding of FE as well as accuracy of genomic prediction for FE. We have performed a cis- and trans- eQTL (expression quantitative trait loci) analysis, in a population of Danbred Durocs (N = 11) and Danbred Landrace (N = 27) using both a linear and ANOVA model based on muscle tissue RNA-seq. We analyzed a total of 1425x19179 or 2.7x107 Gene-SNP combinations in eQTL detection models for FE. The 1425 genes were from RNA-Seq based differential gene expression analyses using 25880 genes related to FE and additionally combined with mitochondrial genes. The 19179 SNPs were from applying stringent quality control and linkage disequilibrium filtering on genotype data using a GGP Porcine HD 70k SNP array. We applied 1000 fold bootstrapping and enrichment analysis to further validate and analyze our detected eQTLs. We identified 13 eQTLs with FDR < 0.1, affecting several genes found in previous studies of commercial pig breeds. Examples include MYO19, CPT1B, ACSL1, IER5L, CPT1A, SUCLA2, CSRNP1, PARK7 and MFF. The bootstrapping results showed statistically significant enrichment (p-value<2.2x10-16) of eQTLs with p-value < 0.01 in both cis and trans-eQTLs. Enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This included transcription factors (p-value = 1.0x10-13), DNA-binding (GO:0003677, p-value = 8.9x10-14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2x10-16), negative regulation of expression (GO:0010629, p-value<2.2x10-16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans eQTLs.
Highlights
The biological background of complex traits is expressed through molecular processes triggered by a combination of genetics, epigenetics and the environment. [1]
In the analysis was done using both the modelANOVA (ANOVA) model, we observed a spike of high p-values (Fig 2b and 2d), which may have been due to issues with model assumptions, but as we tested a large number of eQTLs, it was not practical to do model diagnostics on each eQTL
This did not mean individual ANOVA based eQTLs could not be valid, but we should be careful with drawing results based on the overall distribution
Summary
The biological background of complex traits is expressed through molecular processes triggered by a combination of genetics, epigenetics and the environment. [1]. One way of tackling this issue, is identify correlations between genetics and gene expression, identifying a direct effect of genetic variation This allows for a straightforward interpretation of the effect of genetic variation based on pathway and functional knowledge of implicated genes. This can be done through the identification of eQTLs, mapping genetic variants that influence gene expression patterns of genes in various tissues, originally termed as systems genetics [5, 6]. The usage of both genetic and transcriptomic information, combined with pathway and phenotype data can be a powerful way of identifying biomarkers for traits of interest
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