Reduction in accuracy of genomic prediction for ordered categorical data compared to continuous observations.

Kadir Kizilkaya,Rohan L Fernando,Dorian J Garrick

doi:10.1186/1297-9686-46-37

Kadir Kizilkaya, Rohan L Fernando + Show 1 more

Open Access

https://doi.org/10.1186/1297-9686-46-37

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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.

Highlights

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
Wolc et al [7] studied the evaluation of accuracy of genomic estimated breeding values (GEBV) for economically important traits measured at early or late ages in a closed population of layer chickens over five successive generations using a Bayes Cπ linear model, and found that accuracy of GEBV increased with the size of the training data, moreso for traits with low estimates of π and high heritability
These findings indicate that a 2.25 fold increase in the size of the training population was sufficient to obtain a similar accuracy of GEBV for continuous and ordinal categorical phenotypes within purebred Angus validation (PV) and MV validation populations

Summary

Introduction

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. The factors that affect the accuracy of genomic prediction by Bayesian linear regression models have been studied using simulated [8,9,10,11] and field [6,12] data analyses in purebred (PB) and multibreed (MB) populations Results from those studies have demonstrated that accuracy of genomic estimated breeding values (GEBV) depend on the number of records in the training population, the heritability of the trait, the effective population size, the size of the genome, the density of markers, the genetic architecture of the trait, and the extent of relatedness between training and validation populations [1,11,13]. Gianola and Foulley [17], and Harville and Mee [18] developed the threshold mixed effects model, which has become popular for pedigree-based genetic evaluation of ordinal categorical traits

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