Seleção e associação genômica para precocidade sexual em bovinos da raça Nelore

• Autor(a) principal: Irano, Natalia [UNESP]
• Data de Publicação: 2015
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/135910; http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/15-02-2016/000858103.pdf
• topic: Bovino; Genômica; Melhoramento genetico; Bovino - Reprodução; Genetica animal; Genomics

The objective of this study was to perform genome-wide association to detect chromosomal regions associated with indicator traits of sexual precocity of Nellore cattle, and evaluating methodologies for prediction of genomic values to use for selection of these traits. Data from Nellore animals belonging to farms integrating animal breeding programs of DeltaGen® and Paint® (CRV Lagoa), were used. Age at first calving (AFC), the occurrence of early pregnancy of heifers (EP) and scrotal circumference (SC) were used as traits associated with sexual precocity. After quality control and consistency of phenotypic data, information of 68,170; 72,675 and 83,911 animals with phenotype, and of 1,738; 1,770 and 1,680 genotypes for AFC, EP and SC, respectively, and 412,993 SNPs, remained for analysis. In chapter 2, the estimates of the SNP effects were obtained using the single-step method (WssGBLUP). All animals were used applying single trait animal model to predict the genetic values and, subsequently, the solutions of the SNP effects were obtained from these genetic values. The 10 windows of 150 SNPs that captured the greatest proportion of variance explained by markers were identified. The 10 windows with greater effect obtained for EP are located on chromosomes 5, 6, 7, 14, 18, 21 and 27 and together explained 7.91% of the total genetic variance. For SC, these windows are on chromosomes 4, 8, 11, 13, 14, 19, 22 and 23, explaining 6.78% of the total variance. With GWAS analysis it was possible to identify chromosomal regions associated with EP and SC. Identifying these regions enables better understanding and evaluation of these traits, besides indicating candidate genes for future research studies of causal mutations. In Chapter 3, two multi-step methods were used to estimate the marker effects - GBLUP and IBLASSO; besides the single-step method (ssGBLUP). Observed phenotype was used as the dependent variable to estimate the genomic value ...