Departamento de Producción Animal ETSI Agrónomos-Universidad Politécnica de Madrid, Madrid, Spain.
J Dairy Sci. 2013 Sep;96(9):6047-58. doi: 10.3168/jds.2013-6793. Epub 2013 Jun 28.
The aim of this study was to evaluate different-density genotyping panels for genotype imputation and genomic prediction. Genotypes from customized Golden Gate Bovine3K BeadChip [LD3K; low-density (LD) 3,000-marker (3K); Illumina Inc., San Diego, CA] and BovineLD BeadChip [LD6K; 6,000-marker (6K); Illumina Inc.] panels were imputed to the BovineSNP50v2 BeadChip [50K; 50,000-marker; Illumina Inc.]. In addition, LD3K, LD6K, and 50K genotypes were imputed to a BovineHD BeadChip [HD; high-density 800,000-marker (800K) panel], and with predictive ability evaluated and compared subsequently. Comparisons of prediction accuracy were carried out using Random boosting and genomic BLUP. Four traits under selection in the Spanish Holstein population were used: milk yield, fat percentage (FP), somatic cell count, and days open (DO). Training sets at 50K density for imputation and prediction included 1,632 genotypes. Testing sets for imputation from LD to 50K contained 834 genotypes and testing sets for genomic evaluation included 383 bulls. The reference population genotyped at HD included 192 bulls. Imputation using BEAGLE software (http://faculty.washington.edu/browning/beagle/beagle.html) was effective for reconstruction of dense 50K and HD genotypes, even when a small reference population was used, with 98.3% of SNP correctly imputed. Random boosting outperformed genomic BLUP in terms of prediction reliability, mean squared error, and selection effectiveness of top animals in the case of FP. For other traits, however, no clear differences existed between methods. No differences were found between imputed LD and 50K genotypes, whereas evaluation of genotypes imputed to HD was on average across data set, method, and trait, 4% more accurate than 50K prediction, and showed smaller (2%) mean squared error of predictions. Similar bias in regression coefficients was found across data sets but regressions were 0.32 units closer to unity for DO when genotypes were imputed to HD density. Imputation to HD genotypes might produce higher stability in the genomic proofs of young candidates. Regarding selection effectiveness of top animals, more (2%) top bulls were classified correctly with imputed LD6K genotypes than with LD3K. When the original 50K genotypes were used, correct classification of top bulls increased by 1%, and when those genotypes were imputed to HD, 3% more top bulls were detected. Selection effectiveness could be slightly enhanced for certain traits such as FP, somatic cell count, or DO when genotypes are imputed to HD. Genetic evaluation units may consider a trait-dependent strategy in terms of method and genotype density for use in the genome-enhanced evaluations.
本研究旨在评估不同密度的基因分型面板在基因型推断和基因组预测方面的性能。定制的 Golden Gate Bovine3K BeadChip [LD3K；低密度 (LD) 3000 个标记 (3K)；Illumina Inc.，圣地亚哥，加利福尼亚州]和 BovineLD BeadChip [LD6K；6000 个标记 (6K)；Illumina Inc.] 面板的基因型被推断为 BovineSNP50v2 BeadChip [50K；50000 个标记；Illumina Inc.]。此外，LD3K、LD6K 和 50K 基因型被推断为 BovineHD BeadChip [HD；高密度 80 万个标记 (800K) 面板]，随后评估和比较了预测能力。使用随机提升和基因组 BLUP 进行预测准确性的比较。在西班牙荷斯坦牛群体中选择了四个性状：产奶量、脂肪百分比 (FP)、体细胞计数和开放天数 (DO)。用于推断和预测的 50K 密度训练集包括 1632 个基因型。从 LD 到 50K 的推断测试集包含 834 个基因型，基因组评估测试集包含 383 头公牛。在 HD 上进行基因分型的参考群体包括 192 头公牛。使用 BEAGLE 软件（http://faculty.washington.edu/browning/beagle/beagle.html）进行的推断对于重建密集型 50K 和 HD 基因型非常有效，即使使用较小的参考群体，也有 98.3%的 SNP 正确推断。在 FP 的情况下，随机提升在预测可靠性、均方误差和顶级动物的选择效果方面优于基因组 BLUP。然而，对于其他性状，方法之间没有明显的差异。LD 和 50K 基因型的推断没有差异，而在平均数据集中，HD 基因型的推断平均比 50K 预测更准确，预测的均方误差更小（2%）。在不同的数据集之间发现了相似的回归系数偏差，但当基因型被推断为 HD 密度时，回归系数更接近 1 单位，回归系数为 0.32 个单位。当基因型被推断为 HD 密度时，对于 DO，更多（2%）的顶级公牛被正确分类。当使用原始的 50K 基因型时，顶级公牛的正确分类增加了 1%，当那些基因型被推断为 HD 时，检测到的顶级公牛增加了 3%。当基因型被推断为 HD 时，某些性状（如 FP、体细胞计数或 DO）的选择效果可能会略有提高。遗传评估单位可能会考虑根据方法和基因型密度制定与基因组增强评估相关的策略。
