Genome-wide association study of principal components of growth traits and visual scores in Nellore cattle

Abstract

Principal component analysis (PCA) is used to extract important information from multivariate data which is expressed as a set of new variables called principal components (PCs). The PCs can show associations with a biological meaning among the traits, usually not observed in the original data. Genome-wide association studies (GWAS) can be used as an important tool to identify genomic regions possibly associated with these PCs with biological meaning. The objective of this study is to use genome-wide association analysis to identify genomic regions associated with principal components of growth traits and visual scores in Nellore cattle. Besides the genome-wide association analysis, functional enrichment analyses will be performed to characterize the genomic regions, and the candidate genes will be analyzed for all categories, biological processes, molecular functions, and cellular components. Records from Aliança Nellore dataset will be used with information of, approximately, 600,000 animals at birth, 500,000 at weaning and 330,000 at yearling, from 246 farms located in different regions of Brazil and Paraguay. Principal component analysis will be performed by reparameterization of multivariate mixed model equation. GWAS will be conducted considering the weighted single step GBLUP (wssGBLUP) and Bayes C methods using as "phenotype" predicted breeding values for the principal components, provided by quantitative analysis. It is expected that the results contribute to investigate important genomic regions associated to the principal components with biological meaning, which could assist the selection process. (AU)