Application of kernel regressions in single step model (ssGBLUP) to predict genomic values using productive important traits in Nellore cattle

• Grant number: 15/13084-3
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: September 03, 2015
• End date: June 02, 2016
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Lucia Galvão de Albuquerque
• Grantee: Rafael Espigolan
• Supervisor: Ignacio Aguilar
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Institution abroad: Instituto Nacional de Investigación Agropecuaria Las Brujas (INIA Las Brujas), Uruguay
• Associated to the scholarship: 14/00779-0 - Semi-parametric statistical models for complex traits analysis using genomic data from Nelore cattle, BP.DR

Abstract

A large amount of genomic data is now available for identification and selection of genetically superior individuals, with potential to increase the accuracy of prediction of breeding values and, thus, the efficiency of animal breeding programs. Several statistical methods have been proposed for use in genomic data. Although several studies are being conducted, is still very limited number of studies determining the ability of prediction with statistical parametric and semi- parametric models with a set of real genomic data. Thus, in the proposed project, the prediction ability of genomic values with parametric models GBLUP (Genomic Best Linear Unbiased Predictor), ssGBLUP (Single Step Genomic Best Linear Unbiased Predictor) and the semi-parametric model RKHS (Reproducing Kernel Hilbert Spaces regression) will be studied using a set of genomic data for growth, carcass and meat traits in Nellore cattle. With the results, we hope to obtain important and relevant information in the genetic evaluation of commercial cattle populations. (AU)