Relation between the variations in the number of copies of the genome with the expression of genes associated to the fatty acid profile of beef Nellore

• Grant number: 17/06603-0
• Support type: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): September 01, 2017
• Effective date (End): August 31, 2020
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal researcher: Fernando Sebastián Baldi Rey
• Grantee: Marcos Vinícius Antunes de Lemos
• Home Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil

Abstract

Fatty acids (FA) in bovine meat can contribute to human health and have received considerable attention in recent years. Several studies, using taurine breeds, show an existence of genetic variability and, therefore, a power of genetic improvement in the composition of fatty acids in beef cattle. For this type of characteristics with the genomic information, through so-called genomic selection is recommended. CNV (Copy number variation) is a source of structural genomic variation and is increasingly used in genomic studies. The information obtained in the SNPs arrangements that CNVs are investigated in large-scale studies. Knowledge of the abundance and distribution of CNVs and their association with phenotypes, as well as their effects when located in or near interagency regions, are of great interest. However, few studies have investigated the relationship/effect of CNVs on gene expression in cattle. Thus, the objective of this study is to evaluate the effect of variations in the number of copies of the Nellore breed genome on the expression of genes related to meat fatty acid metabolism in the Longissimus thoracis (LT) muscle. Genotypic data of 3,794 animals will be used for the inference of CNVs and meat samples from 48 Nellore animals for the study of gene expression. In the genotyping of the animals, a panel with approximately 777,000 SNPs (Illumina BovineHD Beadchip) was and genotyping data will be made to detect CNV with PennCNV software. The FA profiles were analyzed in LT samples using gas chromatography, with a 100 m capillary column. RNA total was extracted from each sample from 100 mg of frozen LT muscle, then assemble the aligned fragments of each sample and then find the differentially expressed genes (DEG) for each FA. The Ingenuity Pathway Analysis (IPA) software will be used to evaluate the list of DEGs for canonical over-representation. To identify expressed CNV genes, the seq-RNA data will be processed using the UPC function of the SCAN.UPC package in the "R" software. CNV validation will be performed in 20 animals using the quantitative real-time PCR (qPCR) technique. CNV regions that act significantly on gene expression will serve as study objects for future studies of fine mapping and genomic association.