Lee Joonho, Cheng Hao, Garrick Dorian, Golden Bruce, Dekkers Jack, Park Kyungdo, Lee Deukhwan, Fernando Rohan
Department of Animal Science, Iowa State University, Ames, IA, 50011, USA.
Department of Statistics, Iowa State University, Ames, IA, 50011, USA.
Genet Sel Evol. 2017 Jan 4;49(1):2. doi: 10.1186/s12711-016-0279-9.
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.
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.
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.
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.
贝叶斯A和贝叶斯B的基因组预测使用的训练数据包括具有表型和基因型的动物。单步方法允许将来自非基因型亲属的额外信息纳入分析。单步基因组最佳线性无偏预测(SSGBLUP)方法使用根据标记和系谱信息计算的亲缘关系矩阵,其中缺失的基因型被隐含地估算。单步贝叶斯回归(SSBR)将SSGBLUP扩展到类似贝叶斯B的模型,对非基因型个体使用显式估算的基因型。
胴体记录包括988头经基因分型的韩牛阉牛,有35,882个单核苷酸多态性(SNP),以及1438头未进行基因分型的阉牛,对它们测量了背膘厚度(BFT)、胴体重(CWT)、眼肌面积和大理石花纹评分(MAR)。使用交叉验证比较了基于单性状系谱的最佳线性无偏预测(BLUP)、仅使用基因型个体的贝叶斯方法、SSGBLUP和SSBR方法。
当对仅基因型个体或基因型和非基因型个体的相同表型数据进行建模时,使用基因组信息的方法总是优于基于系谱的BLUP。对于BFT和MAR,单步方法的准确性高于贝叶斯B、贝叶斯C和贝叶斯Cπ。单步方法的准确性提高幅度,BFT为+0.06至+0.09,MAR为+0.05至+0.07。对于CWT,SSBR总是优于仅使用基因型个体的相应贝叶斯方法。然而,尽管SSGBLUP纳入了非基因型个体的信息,但对于CWT,SSGBLUP的预测准确性低于贝叶斯C(π = 0.9999)和贝叶斯B(π = 0.98),因为对于这个特定性状,单核苷酸多态性效应的混合先验有好处。
单步方法是结合基因型和非基因型动物进行预测的首选方法。在某些情况下,替代先验使SSBR优于SSGBLUP。