title: Reduction in accuracy of genomic prediction for ordered categorical data compared to continuous observations.
abstract: BACKGROUND
  Accuracy 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.
  
  
  METHODS
  Efficiency 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.
  
  
  RESULTS
  Accuracies 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.
  
  
  CONCLUSIONS
  Threshold 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.
authors.forename: Kadir
authors.initials: K
authors.lastname: Kizilkaya
authors.forename: Rohan L
authors.initials: RL
authors.lastname: Fernando
authors.affiliation: Department of Animal Science, Iowa State University, Ames IA 50011, USA. dorian@iastate.edu.
authors.forename: Dorian J
authors.initials: DJ
authors.lastname: Garrick
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D003198:Computer Simulation; D005819:Genetic Markers; D023281:Genomics / Q000379:methods*; D005838:Genotype; D016014:Linear Models; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011156:Population Density; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results; D012641:Selection, Genetic
chemical_list: D005819:Genetic Markers