W387
Advances on Bos indicus (Nellore) Genomics: Integrating Genomic Selection and GWAS Information

Date: Saturday, January 10, 2015
Time: 2:00 PM
Room: Town and Country
Jose Fernando Garcia , Faculdade de Ciências Agrárias e Veterinárias, UNESP - Univ. Estadual Paulista, Sao Paulo, Brazil
Yuri Tani Utsunomiya , Faculdade de Ciências Agrárias e Veterinárias, UNESP - Univ. Estadual Paulista, Araçatuba, Brazil
Paolo Ajmone Marsan , Inst. of Zootechnics, Università Cattolica del S. Cuore, Piacenza, Italy
Tad S. Sonstegard , Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD
The Zebu Genome Consortium , The Zebu Genome Consortium, AL
To better understand the genetic differences between Bos taurus (more adapted to temperate climates) and the tropically adapted Bos indicus, and detect genomic regions potentially under divergent selection, we’ve been analyzing over 770,000 SNP across the genome of several breeds using different methods (such as Fst and linkage disequilibrium). More specifically, the Nellore cattle play an important role in beef production in tropical systems and there is great interest in determining if genomic selection can contribute to accelerate genetic improvement of production and fertility in this breed. We present the first results of the implementation of genomic prediction in a Bos indicus (Nellore) population. Additionally, the characterization of genomic regions affecting productive and reproductive traits can contribute to the identification of diagnostic markers for performance, and uncover molecular mechanisms underlying complex aspects of bovine biology. We report several genome-wide scans for chromosome segments explaining differences in scrotal circumference, carcass conformation, meat quality and fertility in 2,300 Nellore (Bos indicus) genotyped using Bovine HD Illumina SNP chip. Finally, a straightforward frequentist approach based on the Stouffer Method, to combine P-values across different tests for evidence of recent positive selection in common variations, as well as strategies for extracting biological information from the detected signals, were described and applied to high density SNP data generated from dairy and beef cattle (taurine and indicine).