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Validation with single-step SNPBLUP shows that evaluations can continue using a single mean of genotyped individuals, even with multiple breeds.

Michael AldridgeJérémie VandenplasPascal DuenkJohn HenshallRachel HawkenMario P L Calus
Published in: Genetics, selection, evolution : GSE (2023)
Based on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.
Keyphrases
  • genome wide
  • genetic diversity
  • high throughput
  • copy number
  • big data
  • gene expression
  • dna methylation
  • artificial intelligence
  • deep learning
  • adverse drug