Abstract
BACKGROUND: The identification of genetic variation underlying desired phenotypes is one of the main challenges of current livestock genetic research. High-throughput transcriptome sequencing (RNA-Seq) offers new opportunities for the detection of transcriptome variants (SNPs and short indels) in different tissues and species. In this study, we used RNA-Seq on Milk Sheep Somatic Cells (MSCs) with the goal of characterizing the genetic variation within the coding regions of the milk transcriptome in Churra and Assaf sheep, two common dairy sheep breeds farmed in Spain. RESULTS: A total of 216,637 variants were detected in the MSCs transcriptome of the eight ewes analyzed. Among them, a total of 57,795 variants were detected in the regions harboring Quantitative Trait Loci (QTL) for milk yield, protein percentage and fat percentage, of which 21.44% were novel variants. Among the total variants detected, 561 (2.52%) and 1,649 (7.42%) were predicted to produce high or moderate impact changes in the corresponding transcriptional unit, respectively. In the functional enrichment analysis of the genes positioned within selected QTL regions harboring novel relevant functional variants (high and moderate impact), the KEGG pathway with the highest enrichment was "protein processing in endoplasmic reticulum". Additionally, a total of 504 and 1,063 variants were identified in the genes encoding principal milk proteins and molecules involved in the lipid metabolism, respectively. Of these variants, 20 mutations were found to have putative relevant effects on the encoded proteins. CONCLUSIONS: We present herein the first transcriptomic approach aimed at identifying genetic variants of the genes expressed in the lactating mammary gland of sheep. Through the transcriptome analysis of variability within regions harboring QTL for milk yield, protein percentage and fat percentage, we have found several pathways and genes that harbor mutations that could affect dairy production traits. Moreover, remarkable variants were also found in candidate genes coding for major milk proteins and proteins related to milk fat metabolism. Several of the SNPs found in this study could be included as suitable markers in genotyping platforms or custom SNP arrays to perform association analyses in commercial populations and apply genomic selection protocols in the dairy production industry.