Effect of Prior Distributions on Accuracy of Genomic Breeding Values for Two Dairy Traits

Abstract

The ideal method to estimate direct genomic values (DGV) would calculate the conditional mean of the breeding value given the genotype of individuals at each quantitative traits locus (QTL). In this study we compare accuracies of DGV obtained using three different prior distributions of the single-nucleotide polymorphism (SNP) effects (normal, Student’s t and double-exponential) in simulated data, to understand the extent of reduction in DGV accuracy when the prior distribution does not match the true distribution of QTL effects. We then apply the methods in a real dataset of 1149 Australian Holstein-Friesian bulls, both to find the prior distribution that is most robust across traits and to make interpretations about the true distribution of QTL effects. Methods using normal and Student’s t prior distributions had fixed hyper-parameters, whereas hyper-parameters for double-exponential prior distribution were conditional to the data. Using the Student’s t distribution for the prior distribution of SNP effects gave the largest estimates of SNP effects in the presence of QTL with large effects in both simulated and real data, and achieved the best accuracies of DGV in both datasets. The double-exponential distribution resulted in higher shrinkage of SNP effect estimates, even when a large true effect was present. The normal distribution resulted in the greatest degree of shrinkage of estimated effects, and gave the lowest accuracies. The amount of information of the data analyzed might still be inadequate to estimate these hyper-parameters accurately. A Student’s t distribution with fixed hyper-parameters was the best approximation of the QTL distribution for the two dairy traits analyzed.

KeyWords:

• Genomic selection
• Prior distribution
• Accuracy
• Holstein

Acknowledgments

The authors wish to thank Phil Bowman for his help in coding the scripts. Gustavo de los Campos and Christian Maltecca are acknowledged for their help in setting up the Bayesian Lasso methods. ELN was funded by AGRISYSTEM PhD fellowship (13^th Cycle) and by grants SELMOL and INNOVAGEN of the Ministry of Agricultural, Food and Forestry Policies (MIPAAF, Italy).