The availability of high-throughput SNP (single nucleotide polymorphism) arrays genotyping technology has allowed the identification of genome regions that underlie the phenotypic variability through genome-wide association studies (GWAS). For complex traits, a disadvantage of GWAS is their association with multiple variants of small effects, which explain only a small proportion of the genetic variation. To overcome this problem, genetical genomic studies have used the abundance of transcripts by messenger RNA (mRNA) profiling combined with genotype data to identify expression quantitative trait loci related to the phenotypic variability. However, these studies have summarized only theassociation among phenotype and common variants without causal direction between them. The aim of this study is to search for causal structures through the integration of phenotypic, genotypic and gene expression information for carcass and meat traits in Nelore cattle. Longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), marbling score (MB) and cooking losses (CL) traits will be used.Genotyping was performed using BovineHD BeadChip (Illumina ® , Inc., San Diego, CA, USA) and GeneSeek ® Genomic Profiler Indicus HD - GGP75Ki (Neogen Corporation, Lincoln, NE, USA). The RNA sequencing was performed using HiSeq 2500 System(Illumina ® , Inc., San Diego, CA, USA) to produce 2x100 base pairs paired-end reads. Gene-phenotype network will be inferred, in a multiple-step procedure, using causal structure learning approaches implemented in the bnlearn R package.
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