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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.)

Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis

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Author(s):
Vargas, Giovana [1] ; Schenkel, Flavio Schramm [2] ; Brito, Luiz Fernando [2] ; de Rezende Neves, Haroldo Henrique [3] ; Munari, Danisio Prado [4, 5] ; Boligon, Arione Augusti [6] ; Carvalheiro, Roberto [1, 4]
Total Authors: 7
Affiliation:
[1] Sao Paulo State Univ UNESP, Sch Agr & Vet Sci, Dept Anim Sci, Via Acesso Prof Paulo Donato Castellane, BR-14884900 Jaboticabal, SP - Brazil
[2] Univ Guelph, Dept Anim Biosci, Ctr Genet Improvement Livestock, 50 Stone Rd East, Guelph, ON N1G 2W1 - Canada
[3] Gensys Associated Consultants, BR-90680000 Porto Alegre, RS - Brazil
[4] Natl Council Sci & Technol Dev, BR-71605001 Brasilia, DF - Brazil
[5] Sao Paulo State Univ UNESP, Sch Agr & Vet Sci, Dept Exact Sci, Via Acesso Prof Paulo Donato Castellane, BR-14884900 Jaboticabal, SP - Brazil
[6] Univ Fed Pelotas, Dept Anim Sci, BR-96160000 Pelotas, RS - Brazil
Total Affiliations: 6
Document type: Journal article
Source: LIVESTOCK SCIENCE; v. 217, p. 37-43, NOV 2018.
Web of Science Citations: 0
Abstract

Principal component analysis (PCA) is used to summarize important information from multivariate data in sets of new variables named principal components (PCs). In animal breeding, these new composite variables can be used to study the associations among multiple traits using the magnitude and direction of the PCA coefficients (in the eigenvectors) for each trait. Phenotypic data from 355 524 Nellore animals were used to estimate genetic parameters and explore the relationship among growth (weaning and post-weaning weight gain), visual score (weaning and yearling conformation, finishing precocity and muscling) and reproductive (scrotal circumference) traits using PCA. Genetic parameters were estimated by multi-trait analysis using a mixed linear animal model. The eigen-decomposition of the additive genetic (co)variance matrix (A(T) matrix) obtained using multi-trait analysis were used to calculate the PCs. In addition, PCA using the (co)variance matrix of the breeding values (EBVs) from single- and multi-trait analyses were investigated for comparison purposes. The direct heritability estimates for the weaning and yearling traits ranged from 0.17 (birth-to-weaning weight gain and conformation) to 0.21 (finishing precocity) and from 0.18 (weaning-to-yearling weight gain) to 0.46 (scrotal circumference), respectively. Genetic correlations estimated among all analyzed traits were positive (favorable) ranging from 0.15 (conformation at weaning and scrotal circumference) to 0.96 (finishing precocity and muscling at weaning). The first three PCs from multi-trait analysis using the eigen-decomposition of the A(T) matrix, explained 87.11% of the total additive genetic variance for the traits. The first PC (PC1) had negative and relatively similar coefficients for all traits, the second PC (PC2) contrasted the animals with early or late biotype, and the third PC (PC3) characterized a contrast between weaning and yearling traits. Our findings suggest that the PCA could be explored in breeding programs to select Nellore cattle to tailor selection towards specific PC, targeting, for instance, faster growth and precocious biotype. (AU)

FAPESP's process: 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle
Grantee:Lucia Galvão de Albuquerque
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/03331-9 - Genome-wide association study of principal components of growth traits and visual scores in Nellore cattle
Grantee:Giovana Vargas
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 15/25449-6 - Genomic association and prediction of principal components of growth traits and visual scores in Nelore cattle
Grantee:Giovana Vargas
Support type: Scholarships in Brazil - Doctorate