Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle

Joonho Lee,Deukhwan Lee,Kyungdo Park,Rohan Fernando,Hao Cheng,Dorian Garrick,Jack Dekkers,Bruce Golden

doi:10.1186/s12711-016-0279-9

Abstract

BackgroundGenomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals.MethodsCarcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation.ResultsMethods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms.ConclusionsSingle-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases.

Highlights

Genomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes
These two results suggest that carcass weight (CWT) is influenced by a few quantitative trait loci (QTL) that explain a large proportion of the genetic variance
For marbling score (MAR), the windows showed smaller effects than those for back-fat thickness (BFT) and eye-muscle area (EMA) with the most significant window explaining less than 0.3% of the genetic variance. These results show that, for BFT, EMA and MAR, many QTL each with a small effect are widely distributed across the whole genome, which is consistent with the infinitesimal model

Summary

Introduction

Genomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals. The singlestep genomic BLUP (SSGBLUP) method uses a relationship matrix that is computed from marker and pedigree information. Fernando et al [7] proposed a class of single-step Bayesian regression methods (SSBR) to extend SSGBLUP to incorporate BayesB-like models for SNP effects (SSBR-B). SSBR methods may promise higher prediction accuracies and provide computational benefits when many animals are genotyped. In SSGBLUP, the distribution of marker effects conditional on the variance of marker effects is assumed univariate normal, whereas in SSBR, the prior for marker effects can follow a t-distribution, a double exponential distribution or mixture distributions, which may be advantageous in some situations

Methods

Results

Discussion

Conclusion