Genomic information for genetic evaluation and mating design in commercial Nellore cattle populations

• Grant number: 18/18921-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: February 01, 2019
• End date: January 31, 2022
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Joanir Pereira Eler
• Grantee: Rafael Espigolan
• Host Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil

Abstract

The objective of this project is to evaluate the application of Best Linear Unbiased Predictor (BLUP) and single step Genomic Best Linear Unbiased Predictor (ssGBLUP) methods using scenarios with different proportions of genotyped animals with missing pedigree and estimate the inbreeding coefficients for mating designs considering distinct proportions of multiple sires involving a set of simulated data and in a commercial beef cattle population. A commercial beef cattle population (CBCP) will be simulated considering different proportions of young animals with unknown sires and maternal grandsires. In addition, a breeding program population (BPP) with phenotypic records, complete pedigree and genomic information (20% of genotyped animals) will be simulated. The information obtained in this study will be useful to establish genetic linkages with the use of genomic information (matrix G) between the commercial herd and the breeding program population, using information from genotyped young animals. The phenotypes and genotypes data will be simulated using the QMSim version 1.10 software. Ten replicates will be performed considering a trait with heritability equal to 0.34 (in order to simulate the trait weight at 450 days, W450) according to estimates of real data and phenotypic variance of 1.0. A genome with a total length of 2,333cM, 735,293 markers, and 7,000 quantitative trait loci (QTL) will be simulated and it will be assumed that QTLs explain 100% of the genetic variance. All markers will be bi-allelic, mimicking SNPs present in bovine commercial panels. Minor allele frequencies (MAF) will be assumed equal for all markers and QTL alleles. QTL allele effects will be sampled from a gamma distribution with a shape parameter equal to 0.4. To evaluate the BLUP and ssGBLUP methods in a CBCP considering different population structures and genotyping strategies, the A matrix will be created using different proportions of young animals (calves) with unknown sires (25, 50, and 75%) and maternal grandsires (0, 25, 50, and 100%). In this population, calves belonging to the last three generation will be genotyped in a proportion of 25, 50, 75, and 100%. For the real data, data from Nellore cattle belonging to farms, which participates in the Nellore Brazil breeding program, will be used. For the W450 analysis, a database containing approximately 90,000 records and 15,000 genotyped animals will be used. Genotypic and phenotypic records will be used to evaluate the same scenarios as those proposed for the simulated data. The accuracies and bias will be calculated in each simulated and real scenario. The inbreeding coefficient based on the pedigree (FPED) and genotype (FGRM and FROH) data will be estimated and evaluated using different mating designs. The information obtained in this study will be useful to evaluate and design strategies regarding the impact of the genomic information upon genetic evaluations under different scenarios in CBCP, identifying more accurately genetically superior animals.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SILVA, R. P.; ESPIGOLAN, R.; BERTON, M. P.; STAFUZZA, N. B.; SANTOS, F. S.; NEGREIROS, M. P.; SCHUCHMANN, R. K.; RODRIGUEZ, J. D.; LOBO, R. B.; BANCHERO, G.; et al. Genetic parameters and genomic regions associated with calving ease in primiparous Nellore heifers. LIVESTOCK SCIENCE, v. 240, OCT 2020. (18/18921-9, 17/10493-5, 19/10438-0)