The impact of genetic relationship information on genomic breeding values in German Holstein cattle.
The impact of genetic relationship information on genomic breeding values in German Holstein cattle.
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DOI：
10.1186/1297-9686-42-5
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发表时间：
2010-02-19
期刊：
影响因子：
--
通讯作者：
Thaller G
中科院分区：
文献类型：
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作者：
Habier D;Tetens J;Seefried FR;Lichtner P;Thaller G
单核苷酸多态性（SNPs）捕获的加性遗传关系对基因组育种值（GEBV）的准确性的影响已被证明，但最近对荷斯坦牛种群数据的研究忽略了这一事实。然而，由于连锁不平衡（LD），这是相当持久的几代人的影响和准确性的GEBV，必须知道实施未来的育种计划。用于调查这些问题的数据集包括3，863头德国荷斯坦公牛的54，001个SNP基因型，它们的血统和产奶量，脂肪产量，蛋白质产量和体细胞评分的女儿产量偏差。交叉验证方法，其中最大的加性遗传关系（amax）之间的公牛在训练和验证控制。通过贝叶斯模型平均法（BayesB）和使用基因组关系矩阵（G-BLUP）的动物模型估计GEBV。通过使用GEBV的准确性和基于家系的BLUP-EBV的准确性的回归方法估计LD引起的GEBV的准确性。通过贝叶斯B和G-BLUP获得的GEBV的准确性随着分析的所有性状的最大值的降低而降低。对于G-BLUP和较小的训练规模，准确性的衰减往往更大。由于LD，BayesB和G-BLUP之间的差异在准确性方面变得明显，其中随着训练规模的增加，BayesB明显优于G-BLUP。当前选择候选者的GEBV准确性由于相对于训练数据的不同加性-遗传关系而变化。未来候选人的准确性可能低于以前的研究报告，因为当选择GEBV时，将无法获得近亲的信息。贝叶斯模型平均方法利用LD信息大大优于G-BLUP，因此是最有前途的方法。交叉验证应考虑数据中的家族结构，以允许在动物和植物育种中基于基因组的长期育种计划。
The impact of additive-genetic relationships captured by single nucleotide polymorphisms (SNPs) on the accuracy of genomic breeding values (GEBVs) has been demonstrated, but recent studies on data obtained from Holstein populations have ignored this fact. However, this impact and the accuracy of GEBVs due to linkage disequilibrium (LD), which is fairly persistent over generations, must be known to implement future breeding programs. The data set used to investigate these questions consisted of 3,863 German Holstein bulls genotyped for 54,001 SNPs, their pedigree and daughter yield deviations for milk yield, fat yield, protein yield and somatic cell score. A cross-validation methodology was applied, where the maximum additive-genetic relationship (amax) between bulls in training and validation was controlled. GEBVs were estimated by a Bayesian model averaging approach (BayesB) and an animal model using the genomic relationship matrix (G-BLUP). The accuracy of GEBVs due to LD was estimated by a regression approach using accuracy of GEBVs and accuracy of pedigree-based BLUP-EBVs. Accuracy of GEBVs obtained by both BayesB and G-BLUP decreased with decreasing amax for all traits analyzed. The decay of accuracy tended to be larger for G-BLUP and with smaller training size. Differences between BayesB and G-BLUP became evident for the accuracy due to LD, where BayesB clearly outperformed G-BLUP with increasing training size. GEBV accuracy of current selection candidates varies due to different additive-genetic relationships relative to the training data. Accuracy of future candidates can be lower than reported in previous studies because information from close relatives will not be available when selection on GEBVs is applied. A Bayesian model averaging approach exploits LD information considerably better than G-BLUP and thus is the most promising method. Cross-validations should account for family structure in the data to allow for long-lasting genomic based breeding plans in animal and plant breeding.