Enrichment of genotyping panels for the genomic selection of special traits in broiler chicken

• Ano de defesa: 2020
• Autor(a) principal: Salvian, Mayara
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Broiler chicken; Frango de corte; Genomic breeding value; Genomic selection; Seleção genômica; Sequenciamento; Sequencing; SNP; Valor genético genômico
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11139/tde-12082020-153615/

Traditional animal breeding programs have considerably modified chicken production in Brazil. However, the intensive selection process over the years brought negative consequences in poultry production, such as increased of the abdominal fat deposition, resulting in difficulties in the industrial processing and depreciation of the final product. In recent years, technological advances in molecular genetics and bioinformatics fields have made genomic selection (GS), using molecular markers (Single Nucleotide Polymorphisms - SNP), and more recently the whole-genome sequencing (WGS), an important tool to increase the genetic gain in animal breeding, especially for complex traits and traits which are difficult to measure. The aims of this work were to estimate the genetic values and compare the genomic predictions using a high-density SNP panel (HD - 600K) and whole-genome sequencing (WGS) dataset through different marker densities. Organs (heart, liver, gizzard and lungs) and carcass (breast, thigh, drumstick) information of 2,000 animals derived from a TT broiler line belonging to the Animal Breeding Program from Embrapa Swine and Poultry were used in further analysis. Subsequently, genomic predictions were performed using pedigree- based BLUP (PBLUP), single-step genomic BLUP (ssGBLUP) and BayesC models using various densities of SNP and variants imputed from whole-genome sequence. Genomic predictions were better when the genomic information was added in the analyses. However, our results showed no benefit of using WGS data compared to HD array data when ssGBLUP or BayesC approaches were applied. Besides that, the use of array data with lower densities (~74.000 SNPs can provide significant results at a low cost.