Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data.
Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data.
复制标题
DOI：
10.1186/1297-9686-45-17
复制
发表时间：
2013-06-13
期刊：
影响因子：
--
通讯作者：
Gianola D
中科院分区：
文献类型：
--
作者：
Morota G;Koyama M;Rosa GJ;Weigel KA;Gianola D
有争议的是，基因型和表型可能以功能形式联系在一起，而数量遗传学中标准的线性加性模型不能很好地解决这些问题。因此，开发统计学习模型，从所有可用的分子信息，能够捕捉复杂的遗传网络结构预测表型值是非常重要的。贝叶斯核岭回归是一种非参数预测模型。其本质是创建一个基于空间距离的关系矩阵，称为内核。虽然建立模型的所有单核苷酸多态性基因型配置的集合是有限的，但过去的研究主要使用高斯核。我们试图研究的扩散内核，这是专门开发的离散标记输入模型的性能，使用荷斯坦牛和小麦的数据。这个核可以被看作是高斯核的离散化。扩散核的预测能力与非空间距离为基础的加性基因组关系内核的荷斯坦数据，但优于后者在小麦数据。然而，扩散核和高斯核之间的性能差异可以忽略不计。它的结论是，扩散核捕捉总遗传方差的能力并不比高斯核，至少对于这些数据。虽然扩散核作为基函数的选择可能有潜力用于全基因组预测，我们的研究结果表明，嵌入到非欧几里德度量空间的遗传标记预测的影响非常小。我们的研究结果表明，使用黑箱高斯内核是合理的，因为它的连接到扩散内核和其类似的预测性能。
Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance.
登录
查看更多内容
影响因子：
1.5
作者：
Long, Nanye;Gianola, Daniel;Gonzalez-Recio, Oscar
通讯作者：
Gonzalez-Recio, Oscar
通过贝叶斯神经网络预测复杂的定量性状：与泽西奶牛和小麦的案例研究。
影响因子：
2.9
作者：
Gianola D;Okut H;Weigel KA;Rosa GJ
通讯作者：
Rosa GJ
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：
de los Campos, G.;Gianola, D.;Rosa, G. J. M.
通讯作者：
Rosa, G. J. M.
影响因子：
3.3
作者：
Gianola, Daniel;van Kaam, Johannes B. C. H. M.
通讯作者：
van Kaam, Johannes B. C. H. M.