The meat produced in Brazil from Zebu has organoleptic characteristics that are not well accepted in the most demanding markets. The lack of uniformity in the age at slaughter, the lack of subcutaneous fat and the marbling of meat in not satisfactory quantities, have great influence on tenderness, color and palatability of the final product. Despite this, the breeding for meat tenderness and fat thickness has not been practiced, especially because these traits are of late expression, and / or require the slaughter of animals, or because of the cost of measurement . In recent years, after the sequencing of the human genome and several species of domestic animals, several works about the application of genomic data has been published in the. Currently, were available many tools that enable to obtain genomic information, genetic markers (SNPs) on a large scale in several animal species. The use of the panel of markers of single nucleotide polymorphisms (SNPs) of high density, for genetic evaluation, will enable significant genetic gains, particularly in characteristics of difficult and / or high cost of measurement, as is the case of beef tenderness and fat thickness. Despite the great advances in molecular biology techniques that allow the genotyping of animals, are still being developed methodologies and models that enable the application of genomic data in genetic improvement programs. The objective of this work is to check the association between genetic markers (SNPs) with the meat tenderness and fat thickness in Nelore cattle. In this study will be used data for characteristics of meat tenderness and fat thickness of approximately 800 males Nellore finished in feedlot with nearly two years of age, progeny of nearly 50 bulls from six herds, all with performance and pedigree information available. In association analysis to estimate the effect of each SNP, will be used a linear mixed model, using the R software, since this program have specific routines (package SNPassoc) that allow to study the association between several SNPs and linear traits using the restricted maximum likelihood methodology. For the analysis, the model includes the random effect of sire and the fixed effect of contemporary group and the age of the animal at slaughter. The estimate of each SNP will be included in the model as the regression of phenotypic value of the animal on the number of copies of an allele of the marker. There will be made a multiple regression analysis, considering the most significant SNPs obtained from simple regression analysis.
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