Strategies of genotypes imputation for genome-wide association studies in Nellore cattle

Author(s)

Fabiane de Lima Silva

• Document type: Doctoral Thesis
• Press: Piracicaba.
• Institution: Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALA/BC)
• Defense date: 2013-11-07

Examining board members

Luiz Lehmann Coutinho; Rachel Santos Bueno; Carlos Tadeu dos Santos Dias; Luciana Correia de Almeida Regitano; Millor Fernandes do Rosário

• Advisor: Luiz Lehmann Coutinho

Abstract

Temperament in cattle is generally defined as the reaction of an animal in response to contact with human, usually attributed to the fear. Animals agitated are harder to manage in farms. Studies in the literature reported that temperamental traits have been found to influence the productive performance of herds, due to its correlation with other traits such as carcass quality, daily gains, pregnancy rate and feed efficiency. However, almost nothing is known regarding the genetic landscape controlling temperamental animal variation in cattle. With the advances in genotyping technologies for high density genotyping platforms to markers of single nucleotide polymorphisms (SNP), there was an increase in research in the areas of genomic selection (GS) and genome-wide association studies (GWAS). However, genotyping many animals with a high-density marker panel can be expensive and economically unfeasible. An alternative in this context is to use a lower density marker panel on a larger number of animals, and impute the missing genotypes. In this context two studies were developed. In the first study, the objective was to identify chromosomal regions associated with temperament traits in Nellore cattle. The temperamental traits evaluated in this study were: exit velocity (VS) and the median score temperament (EC_mediana) assessed in 599 and 575 animals, respectively. All animals were genotyped with Illumina BeadChip BovineHD (800K), and two GWAS statistical models were applied. The first model used was genomic single step (ssGBLUP) in which the effects of SNP are derived from the genomic prediction values of the animals. The second was linear mixed model, similar to the previous model, but a series of models, one for each SNP (single regression), were performed. In both models were included the effects of contemporary group as fixed, the animal\'s age as a covariate effect and polygenic animal and permanent environment as random effect. Variance components were estimated by restricted maximum likelihood. The heritability coefficients showed low magnitude estimative of 0.02 and 0.05 for VS and EC_mediana, respectively. Different chromosomal regions were associated with the traits studied in this population, according with the models used, and moreover to contribute in the understanding of the genetic architecture of these traits. In the second study, the objective was to evaluate the accuracy of imputation in a same population, using two panels of low-density (3K and 6K) and one medium-density (50K) panel markers imputed up to higher density of 800K. The animals genotyped with 800K markers panel were split into a reference population and validation population. The validation population the animals had markers masked to Illumina chip 3K, 6K e 50K. Imputation from 3K, 6K and 50K up to 770K markers was performed using fastPHASE and Beagle. The software fastPHASE had higher imputation accuracy compared to Beagle. The 6K and 50K panels showed higher accuracy. (AU)

• FAPESP's process: 10/20432-4 - Strategies of imputation of genotypes of SNPs markers for association studies of Nellore breed
• Grantee: Fabiane de Lima Silva
• Support Opportunities: Scholarships in Brazil - Doctorate