Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values

K Kaseja*, S Mucha, Ed Smith, John Yates, G Banos, JE Conington

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
84 Downloads (Pure)


The impact of inclusion of genome-wide genotypes into breeding value predictions for UK Texel sheep is addressed in this article. The main aim was to investigate the level of change in the accuracy values for EBVs when information
from animal genotypes is incorporated into the genetic evaluations. New genetic
parameters for a range of lamb growth, carcass composition and health traits are described and applied in the estimation of conventional breeding values (EBVs) for almost 822,000 animals as well as genomic breeding values (gEBVs) after adding 10,143 genotypes. Principal component analyses showed that there are no major distinct groups; hence, the population is mainly homogenous and genetically well-linked. Results suggested that the highest change in accuracy was observed for the animals that are not phenotyped but have good links to the reference population. This was seen especially for the lowly heritable health traits thereby proving that the use of genotypes in breeding values estimation may accelerate the genetic gain by producing more accurate values especially for young, un-phenotyped animals.
Original languageEnglish
Pages (from-to)462-471
Number of pages10
JournalJournal of Animal Breeding and Genetics
Issue number4
Early online date1 Apr 2023
Publication statusPrint publication - Jul 2023

Bibliographical note

© 2023 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.


  • accuracy change
  • animal welfare
  • breeding values
  • genetic parameters
  • genomic selection
  • meat sheep
  • sheep breeding
  • single-step
  • Sheep/genetics
  • Genomics
  • Genotype
  • Phenotype
  • Animals
  • Models, Genetic
  • Polymorphism, Single Nucleotide
  • Red Meat
  • Genome


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