Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Lee, Joonho; Cheng, Hao; Garrick, Dorian; Golden, Bruce; Dekkers, Jack; Park, Kyungdo; Lee, Deukhwan; Fernando, Rohan
• Is Part Of: Genetics, selection, evolution. Volume 49:Issue 1(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-12
• Subjects: Livestock -- Breeding -- Periodicals; Animal genetics -- Periodicals; Livestock -- Genetics -- Periodicals; Evolution -- Periodicals; 576.505
• DOI: 10.1186/s12711-016-0279-9
• Languages: English
• ISSNs: 1297-9686

Abstract

Abstract Background 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. Methods Carcass 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. Results Methods 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. Conclusions Single-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases.