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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Accounting for population structure in selective cow genotyping strategies

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Author(s):
Perez, Bruno C. [1] ; Balieiro, Julio C. C. [2] ; Carvalheiro, Roberto [3] ; Tirelo, Fabio [4] ; Oliveira Junior, Gerson A. [5] ; Dementshuk, Juliana M. [6] ; Eler, Joanir P. [7] ; Ferraz, Jose B. S. [7] ; Ventura, Ricardo V. [2]
Total Authors: 9
Affiliation:
[1] Univ Sao Paulo, Fac Zootecnia & Engn Alimentos, Pirassununga - Brazil
[2] Univ Sao Paulo, Fac Med Vet & Zootecnia, Pirassununga - Brazil
[3] Univ Estadual Paulista, Dept Zootecnia, Jaboticabal - Brazil
[4] Google Inc, Toronto, ON - Canada
[5] Univ Guelph, Dept Anim Biosci, Ctr Genet Improvement Livestock, Guelph, ON - Canada
[6] Univ Fed Rio Grande do Sul, Dept Zootecnia, Porto Alegre, RS - Brazil
[7] Univ Sao Paulo, GMAB, Dept Ciencias Vet, FZEA, Pirassununga - Brazil
Total Affiliations: 7
Document type: Journal article
Source: JOURNAL OF ANIMAL BREEDING AND GENETICS; v. 136, n. 1, p. 23-39, JAN 2019.
Web of Science Citations: 0
Abstract

The objective of the present study was to investigate the impact of considering population structure in cow genotyping strategies over the accuracy and bias of genomic predictions. A small dairy cattle population was simulated to address these objectives. Based on four main traditional designs (random, top-yield, extreme-yield and top-accuracy cows), different numbers (1,000; 2,000 and 5,000) of cows were sampled and included in the reference population. Traditional designs were replicated considering or not population structure and compared among and with a reference population containing only bulls. The inclusion of cows increased accuracy in all scenarios compared with using only bulls. Scenarios accounting for population structure when choosing cows to the reference population slightly outperformed their traditional versions by yielding higher accuracy and lower bias in genomic predictions. Building a cow-based reference population from groups of related individuals considering the frequency of individuals from those same groups in the validation population yielded promising results with applications on selection for expensive- or difficult-to-measure traits. Methods here presented may be easily implemented in both new or already established breeding programs, as they improved prediction and reduced bias in genomic evaluations while demanding no additional costs. (AU)

FAPESP's process: 16/19514-2 - Development of a genomic database for Nellore cattle and computational tools for implementing large-scale studies
Grantee:Ricardo Vieira Ventura
Support Opportunities: Research Grants - Young Investigators Grants