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Investigating the persistence of accuracy of genomic predictions over time in broilers

Authors
  • Hidalgo, Jorge1
  • Lourenco, Daniela1
  • Tsuruta, Shogo1
  • Masuda, Yutaka1
  • Breen, Vivian2
  • Hawken, Rachel2
  • Bermann, Matias1
  • Misztal, Ignacy1
  • 1 Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602 , (United States)
  • 2 Cobb-Vantress Inc., Siloam Springs, AR 72761 , (United States)
Type
Published Article
Journal
Journal of Animal Science
Publisher
Oxford University Press
Publication Date
Aug 11, 2021
Volume
99
Issue
9
Identifiers
DOI: 10.1093/jas/skab239
PMID: 34378776
PMCID: PMC8420680
Source
PubMed Central
Keywords
Disciplines
  • AcademicSubjects/SCI00960
License
Unknown

Abstract

Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees and 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over 7 years, of which 154,318 from the last 4 years were genotyped. Training populations were constructed adding 1 year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first 3 years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of 1 year of genotypes in addition to 4 years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included 5 or 2 years of data, and a decrease of ~7% was observed when the training population included only 1 year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared with training sets without genomic data. The two most recent years of pedigree, phenotypic, and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.

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