Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

Abstract

12Feed efficiency (FE) is a key trait in pig production, as it has both high economic and 13 environmental impact. FE is a challenging phenotype to study, as it is complex and affected by 14 many factors, such as metabolism, growth and activity level. Furthermore, testing for FE is 15 expensive, as it requires costly equipment to measure feed intake of individual animals, making 16 FE biomarkers valuable. Therefore, there has been a desire to find single nucleotide 17 polymorphisms (SNPs) as biomarkers, to assist with improved selection and improve our 18 biological understanding of FE. We have done a cis-and trans-eQTL (expressed quantitative 19 trait loci) analysis, in a population of Danbred Durocs (N=11) and Danbred Landrace (N=27) 20 using both a linear and Anova model. We used bootstrapping and enrichment analysis to 21 validate and analyze our detected eQTLs. We identified 15 eQTLs with FDR < 0.01, affecting 22 several genes found in previous studies of commercial pig breeds. Examples include IFI6, 23 PRPF39, TMEM222, CSRNP1,PARK7 and MFF. The bootstrapping results showed statistically 24 significant enrichment of eQTLs with p-value < 0.01 (p-value < 2.2x0 -16 ) in both cis and trans-25 eQTLs. Based on this, enrichment analysis of top trans-eQTLs revealed high enrichment for 26 gene categories and gene ontologies associated with genomic context and expression regulation.27 This includes transcription factors (p-value=1.0x10 -13 ), DNA-binding (GO:0003677, p-28 value=8.9x10 -14 ), DNA-binding transcription factor activity (GO:0003700,) nucleus gene 29 (GO:0005634, p-value<2.2x10 -16 ), positive regulation of expression (GO:0010628), negative 30 regulation of expression (GO:0010629, p-value<2.2x10 -16 ). These results would be useful for 31 future genome assisted breeding of pigs to improve FE, and in the improved understanding of 32 the functional mechanism of trans-eQTLs. 33 34 42 variation based on pathway and functional knowledge of implicated genes. This can be done through 43 the identification of expressed quantitative loci (eQTL), mapping genetic variants that influence gene 44 expression patterns of genes in various tissues, originally termed as systems genetics (5, 6). The usage 45 of both the genetic and the transcriptomic information, combined with pathway and phenotype data 46 can be a powerful way of identifying biomarkers for traits of interest. There are however, several 47 challenges with eQTL analysis. Firstly, if one wanted to map all possible SNP-gene pairs in a modern 48data set, which typically has thousands of expressed genes and at the minimum several tens of 49 thousands of SNP, the total amount of tests will be at least in the order of 10 8 . This can pose 50 computational challenges, but even worse, multiple testing problems. This is especially relevant as a 51 cursory search of the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) for 52 RNA-seq studies reveals most studies having less than 100 samples. Therefore, it is important to have 53 strategies for these ...