Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data.
Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data.
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DOI：
10.1534/genetics.115.177204
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发表时间：
2015-10
期刊：
影响因子：
3.3
通讯作者：
Gianola D
中科院分区：
文献类型：
--
作者：
Hu Y;Morota G;Rosa GJ;Gianola D
利用分子遗传信息预测复杂性状是数量遗传学研究的一个活跃领域。在后基因组时代，许多类型的基因组学（例如，转录组学、表观基因组学、甲基组学和蛋白质组学）数据变得越来越可用。因此，评估这些大量信息在复杂性状预测中的效用是有意义的。DNA甲基化是DNA分子的共价变化而不影响其潜在序列，是表观遗传修饰的一种可量化形式。我们使用甲基化信息预测拟南芥株高（PH）非参数，使用再生核希尔伯特空间（RKHS）回归。此外，我们使用不同的标准来选择较小的探针组，以评估如何在预测中使用代表性探针而不是使用所有探针，这可以减轻计算负担并降低实验成本。甲基化信息被用来描述个体之间的表观遗传相似性，通过一个核矩阵，并预测PH值使用此相似性矩阵的性能是相当不错的。预测相关性达到0.53，并且当仅使用预选探针进行预测时达到相同的值。我们创建了一个内核，模仿基因组最佳线性无偏预测（G-BLUP）中的基因组关系矩阵，并估计在这个特定的数据集中，表观遗传变异占表型方差的65%。我们的研究结果表明，甲基化信息可以在复杂性状的全基因组预测，它可能有助于提高复杂性状的理解时，表观遗传学正在检查。
Prediction of complex traits using molecular genetic information is an active area in quantitative genetics research. In the postgenomic era, many types of -omic (e.g., transcriptomic, epigenomic, methylomic, and proteomic) data are becoming increasingly available. Therefore, evaluating the utility of this massive amount of information in prediction of complex traits is of interest. DNA methylation, the covalent change of a DNA molecule without affecting its underlying sequence, is one quantifiable form of epigenetic modification. We used methylation information for predicting plant height (PH) in Arabidopsis thaliana nonparametrically, using reproducing kernel Hilbert spaces (RKHS) regression. Also, we used different criteria for selecting smaller sets of probes, to assess how representative probes could be used in prediction instead of using all probes, which may lessen computational burden and lower experimental costs. Methylation information was used for describing epigenetic similarities between individuals through a kernel matrix, and the performance of predicting PH using this similarity matrix was reasonably good. The predictive correlation reached 0.53 and the same value was attained when only preselected probes were used for prediction. We created a kernel that mimics the genomic relationship matrix in genomic best linear unbiased prediction (G-BLUP) and estimated that, in this particular data set, epigenetic variation accounted for 65% of the phenotypic variance. Our results suggest that methylation information can be useful in whole-genome prediction of complex traits and that it may help to enhance understanding of complex traits when epigenetics is under examination.