Extension of the bayesian alphabet for genomic selection.
Extension of the bayesian alphabet for genomic selection.
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DOI：
10.1186/1471-2105-12-186
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发表时间：
2011-05-23
影响因子：
3
通讯作者：
Garrick DJ
中科院分区：
文献类型：
--
作者：
Habier D;Fernando RL;Kizilkaya K;Garrick DJ
为了解决贝叶斯A和贝叶斯B关于先验超参数影响的缺点，并将SNP具有零效应的先验概率π视为未知，开发了两种用于基因组预测的贝叶斯方法，BayesCπ和BayesDπ。利用模拟情景和来自北美荷斯坦公牛的真实的数据，比较了这两种方法在QTL数目推断和基因组育种值估计（GEBV）准确性方面的差异。与Bayes D π相比，Bayes C π的π估计值对模拟QTL的数量和训练数据大小敏感，并提供有关遗传结构的信息。产奶量和乳脂率的QTL效应大于蛋白质产量和体细胞评分。贝叶斯A和贝叶斯B的缺点并不影响GEBV的准确性。其他贝叶斯方法的准确性相似。贝叶斯A是一个很好的选择GEBV与真实的数据。BayesCπ的计算时间比BayesDπ短，而我们的BayesA实现的计算时间最长。总的来说，考虑到计算工作量，QTL数量的不确定性（影响替代方法的GEBV准确性），以及对数量性状相关QTL数量的根本兴趣，我们认为BayesCπ具有常规应用的优点。
Two Bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls. Estimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesCπ than for BayesDπ, and longest for our implementation of BayesA. Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesCπ has merit for routine applications.
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DOI：
10.1186/1297-9686-42-5
发表时间：
2010-02-19
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Habier D;Tetens J;Seefried FR;Lichtner P;Thaller G
通讯作者：
Thaller G
影响因子：
3.3
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
通讯作者：
Dekkers, J. C. M.
影响因子：
3.3
作者：
Gianola, Daniel;de los Campos, Gustavo;Fernando, Rohan
通讯作者：
Fernando, Rohan
影响因子：
3.3
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
通讯作者：
Dekkers, J. C. M.
DOI：
10.1186/1297-9686-41-55
发表时间：
2009-12-31
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Garrick DJ;Taylor JF;Fernando RL
通讯作者：
Fernando RL