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Imputation of molecular markers in Canchim cattle

Grant number: 13/02175-2
Support Opportunities:Scholarships abroad - Research Internship - Master's degree
Start date: April 01, 2013
End date: September 30, 2013
Field of knowledge:Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
Principal Investigator:Danísio Prado Munari
Grantee:Tatiane Cristina Seleguim Chud
Supervisor: Flavio S. Schenkel
Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
Institution abroad: University of Guelph, Canada  
Associated to the scholarship:12/21891-8 - Imputation of molecular markers in Canchim Cattle, BP.MS

Abstract

The development of new panels to genotyping in high density, containing thousands SNPS (single nucleotide polymorphisms) markers made the genomic selection an attractive and promising selections method. However, these dense marker panels are expensive, complicating the genotyping of all selection candidates. A strategy to reduce these costs would be using low-density marker panels, combined with the use of imputation methods. Imputation consists of predicting missing genotypes in animals genotyped with a low density panel by using information from a reference population genotyped with a higher density panel. Thus, the goal of the study will be compare imputation methods in Canchim Cattle aiming future application of imputed markers for genomic selection. Genomic information from 400 Canchim Cattle animals, obtained by high density panels (777,962 markers) will be used. The animals will be divided in two different groups, called reference and imputation population. For the imputation group low-density marker panels in different scenarios will be simulated. The imputation of genotypes will be conducted by two softwares; FImpute (version 2) and BEAGLE. To determine accuracy imputation, imputed genotypes will be compared with the true markers present in original HD panels. (AU)

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