Abstract
BackgroundThe reliability of whole-genome prediction models (WGP) based on using high-density single nucleotide polymorphism (SNP) panels critically depends on proper specification of key hyperparameters. A currently popular WGP model labeled BayesB specifies a hyperparameter π, that is `loosely used to describe the proportion of SNPs that are in linkage disequilibrium (LD) with causal variants. The remaining markers are specified to be random draws from a Student t distribution with key hyperparameters being degrees of freedom v and scale s2.MethodsWe consider three alternative Markov chain Monte Carlo (MCMC) approaches based on the use of Metropolis-Hastings (MH) to estimate these key hyperparameters. The first approach, termed DFMH, is based on a previously published strategy for which s2 is drawn by a Gibbs step and v is drawn by a MH step. The second strategy, termed UNIMH, substitutes MH for Gibbs when drawing s2 and further collapses or marginalizes the full conditional density of v. The third strategy, termed BIVMH, is based on jointly drawing the two hyperparameters in a bivariate MH step. We also tested the effect of misspecification of s2 for its effect on accuracy of genomic estimated breeding values (GEBV), yet allowing for inference on the other hyperparameters.ResultsThe UNIMH and BIVMH strategies had significantly greater (P < 0.05) computational efficiencies for estimating v and s2 than DFMH in BayesA (π = 1) and BayesB implementations. We drew similar conclusions based on an analysis of the public domain heterogeneous stock mice data. We also determined significant drops (P < 0.01) in accuracies of GEBV under BayesA by overspecifying s2, whereas BayesB was more robust to such misspecifications. However, understating s2 was compensated by counterbalancing inferences on v in BayesA and BayesB, and on π in BayesB.ConclusionsSampling strategies based solely on MH updates of v and s2, and collapsed representations of full conditional densities can improve the computational efficiency of MCMC relative to the use of Gibbs updates. We believe that proper inferences on s2, v and π are vital to ensure that the accuracy of GEBV is maximized when using parametric WGP models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0092-x) contains supplementary material, which is available to authorized users.
Highlights
The reliability of whole-genome prediction models (WGP) based on using high-density single nucleotide polymorphism (SNP) panels critically depends on proper specification of key hyperparameters
Inferences on s2 using each of the three sampling strategies DFMH, UNIMH and BIVMH were compared under both BayesA [See Additional file 2: Figure S1] and BayesB [See Additional file 2: Figure S2] specifications
This was as anticipated, since BayesB requires inference on one more hyperparameter (π) and potentially greater Monte Carlo error for the same number of cycles; inference on s2 and v is essentially based on information from only non-zero SNP effects, which is appreciably less than the effective total number of SNPs used for estimating s2 and v in BayesA [3]
Summary
The reliability of whole-genome prediction models (WGP) based on using high-density single nucleotide polymorphism (SNP) panels critically depends on proper specification of key hyperparameters. A currently popular WGP model labeled BayesB specifies a hyperparameter π, that isloosely used to describe the proportion of SNPs that are in linkage disequilibrium (LD) with causal variants. Yang et al Genetics Selection Evolution (2015) 47:13 is somewhat complicated by varying degrees of LD across the genome These hyperparameters (v, s2 and π) are relevant in that they partly characterize the genetic architecture of traits, but more importantly depend upon the density or characteristics of the SNPs used in the analyses [3]
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