Reduction in accuracy of genomic prediction for ordered categorical data compared
to continuous observations |
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Authors: | Kadir Kizilkaya Rohan L Fernando Dorian J Garrick |
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Institution: | 1.Department of Animal Science, Iowa State University, Ames IA 50011, USA;2.Department of Animal Science, Adnan Menderes University, Aydin 09100, Turkey;3.Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand |
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Abstract: | BackgroundAccuracy of genomic prediction depends on number of records in the training
population, heritability, effective population size, genetic architecture,
and relatedness of training and validation populations. Many traits have
ordered categories including reproductive performance and susceptibility or
resistance to disease. Categorical scores are often recorded because they
are easier to obtain than continuous observations. Bayesian linear
regression has been extended to the threshold model for genomic prediction.
The objective of this study was to quantify reductions in accuracy for
ordinal categorical traits relative to continuous traits.MethodsEfficiency of genomic prediction was evaluated for heritabilities of 0.10,
0.25 or 0.50. Phenotypes were simulated for 2250 purebred animals using 50
QTL selected from actual 50k SNP (single nucleotide polymorphism) genotypes
giving a proportion of causal to total loci of.0001. A Bayes C
π threshold model simultaneously fitted all 50k markers
except those that represented QTL. Estimated SNP effects were utilized to
predict genomic breeding values in purebred (n = 239) or multibreed (n =
924) validation populations. Correlations between true and predicted genomic
merit in validation populations were used to assess predictive ability.ResultsAccuracies of genomic estimated breeding values ranged from 0.12 to 0.66 for
purebred and from 0.04 to 0.53 for multibreed validation populations based
on Bayes C π linear model analysis of the simulated underlying
variable. Accuracies for ordinal categorical scores analyzed by the Bayes C
π threshold model were 20% to 50% lower and ranged from
0.04 to 0.55 for purebred and from 0.01 to 0.44 for multibreed validation
populations. Analysis of ordinal categorical scores using a linear model
resulted in further reductions in accuracy.ConclusionsThreshold traits result in markedly lower accuracy than a linear model on the
underlying variable. To achieve an accuracy equal or greater than for
continuous phenotypes with a training population of 1000 animals, a 2.25
fold increase in training population size was required for categorical
scores fitted with the threshold model. The threshold model resulted in
higher accuracies than the linear model and its advantage was greatest when
training populations were smallest. |
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