Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data.
Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data.
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DOI：
10.1534/g3.113.007096
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发表时间：
2013-09-04
期刊：
影响因子：
--
通讯作者：
Sillanpää MJ
中科院分区：
文献类型：
--
作者：
Kärkkäinen HP;Sillanpää MJ
由于全基因组分子标记集的可用性增加以及大样本个体基因分型成本降低，基因组估计育种值已成为植物和动物育种的重要资源。贝叶斯方法用于育种价值估计已被证明是准确和有效的；然而，不断增加的数据集对参数估计算法提出了很高的要求。尽管对于连续高斯特征的贝叶斯模型有相当数量的快速估计算法可用，但对于离散或截除表型的相应模型却缺乏。在这项工作中，我们考虑了对贝叶斯多位点关联模型和贝叶斯基因组最佳线性无偏预测的二值、序数和截尾高斯观测值的阈值方法，并提出了在这些模型下参数估计的高速广义期望最大化算法。用模拟数据和实际数据对该方法进行了验证。我们的示例分析表明，使用有序分类或删节高斯数据集中存在的额外信息，而不是将数据分为病例对照观察，可以提高贝叶斯多位点关联模型或贝叶斯基因组最佳线性无偏预测预测的基因组育种值的准确性。此外，算例分析表明，在剔除高斯数据的情况下，正确的阈值模型比直接使用的高斯模型更准确，而在二值或有序数据的情况下，阈值模型的优越性无法得到证实。
Because of the increased availability of genome-wide sets of molecular markers along with reduced cost of genotyping large samples of individuals, genomic estimated breeding values have become an essential resource in plant and animal breeding. Bayesian methods for breeding value estimation have proven to be accurate and efficient; however, the ever-increasing data sets are placing heavy demands on the parameter estimation algorithms. Although a commendable number of fast estimation algorithms are available for Bayesian models of continuous Gaussian traits, there is a shortage for corresponding models of discrete or censored phenotypes. In this work, we consider a threshold approach of binary, ordinal, and censored Gaussian observations for Bayesian multilocus association models and Bayesian genomic best linear unbiased prediction and present a high-speed generalized expectation maximization algorithm for parameter estimation under these models. We demonstrate our method with simulated and real data. Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction. Furthermore, the example analyses indicate that the correct threshold model is more accurate than the directly used Gaussian model with a censored Gaussian data, while with a binary or an ordinal data the superiority of the threshold model could not be confirmed.
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影响因子：
3.3
作者：
de los Campos, Gustavo;Naya, Hugo;Cotes, Jose Miguel
通讯作者：
Cotes, Jose Miguel
DOI：
10.1109/tpami.2003.1227989
发表时间：
2003-09-01
影响因子：
23.6
作者：
Figueiredo, MAT
通讯作者：
Figueiredo, MAT
影响因子：
1.9
作者：
Hackett, CA;Weller, JI
通讯作者：
Weller, JI
影响因子：
3.3
作者：
Hallander, Jon;Waldmann, Patrik;Sillanpaa, Mikko J.
通讯作者：
Sillanpaa, Mikko J.
DOI：
10.1534/g3.111.001453
发表时间：
2012-04
期刊：
G3 (Bethesda, Md.)
影响因子：
--
作者：
Cleveland MA;Hickey JM;Forni S
通讯作者：
Forni S