Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat.
Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat.
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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。
DOI：
10.1186/1471-2156-12-87
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发表时间：
2011-10-07
期刊：
影响因子：
2.9
通讯作者：
Rosa GJ
中科院分区：
文献类型：
--
作者：
Gianola D;Okut H;Weigel KA;Rosa GJ
在研究基因组数据和复杂表型之间的关联时，可能存在不适合参数统计建模的关系。这种关联主要使用单标记和贝叶斯线性回归模型进行研究，这些模型在分布上有所不同，但假设了加性遗传，而忽略了相互作用和非线性。当相互作用被包括在模型中时，它们的影响已经线性进入。基于在标记和径向基函数上再现核希尔伯特空间回归来预测数量性状的非参数方法越来越受到关注。人工神经网络（ANN）提供了另一种选择，因为它们作为复杂函数的通用逼近器，可以捕捉预测器和响应之间的非线性关系，以及自适应学习的变量之间的相互作用。人工神经网络是分析受隐型基因作用影响的性状的有趣候选者。我们研究了用于预测泽西奶牛产奶量和小麦自交系产量的两个数据集的表型的各种贝叶斯神经网络架构。对于泽西奶牛，预测变量来源于297头奶牛的家系和分子标记（35,798个单核苷酸多态性，SNPS）信息。小麦数据代表599个品系，每个品系有1279个标记。当使用系谱时，预测脂肪、牛奶和蛋白质产量的能力较低，但当使用snp时，无论训练的人工神经网络如何，预测效果都更好。小麦的预测能力甚至更好，因为这种性状是平均的，而不是奶牛的个体表型。非线性神经网络在两种数据集的预测能力上都优于线性模型，但在小麦方面表现得更为明显。结果表明，在未知数量超过样本量的情况下，神经网络可能有助于利用高维基因组信息预测复杂性状。人工神经网络可以自适应捕获非线性。这可能是有用的，当预测表型是至关重要的。
In the study of associations between genomic data and complex phenotypes there may be relationships that are not amenable to parametric statistical modeling. Such associations have been investigated mainly using single-marker and Bayesian linear regression models that differ in their distributions, but that assume additive inheritance while ignoring interactions and non-linearity. When interactions have been included in the model, their effects have entered linearly. There is a growing interest in non-parametric methods for predicting quantitative traits based on reproducing kernel Hilbert spaces regressions on markers and radial basis functions. Artificial neural networks (ANN) provide an alternative, because these act as universal approximators of complex functions and can capture non-linear relationships between predictors and responses, with the interplay among variables learned adaptively. ANNs are interesting candidates for analysis of traits affected by cryptic forms of gene action. We investigated various Bayesian ANN architectures using for predicting phenotypes in two data sets consisting of milk production in Jersey cows and yield of inbred lines of wheat. For the Jerseys, predictor variables were derived from pedigree and molecular marker (35,798 single nucleotide polymorphisms, SNPS) information on 297 individually cows. The wheat data represented 599 lines, each genotyped with 1,279 markers. The ability of predicting fat, milk and protein yield was low when using pedigrees, but it was better when SNPs were employed, irrespective of the ANN trained. Predictive ability was even better in wheat because the trait was a mean, as opposed to an individual phenotype in cows. Non-linear neural networks outperformed a linear model in predictive ability in both data sets, but more clearly in wheat. Results suggest that neural networks may be useful for predicting complex traits using high-dimensional genomic information, a situation where the number of unknowns exceeds sample size. ANNs can capture nonlinearities, adaptively. This may be useful when prediction of phenotypes is crucial.
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影响因子：
1.5
作者：
Long, Nanye;Gianola, Daniel;Gonzalez-Recio, Oscar
通讯作者：
Gonzalez-Recio, Oscar
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：
Alados, I;Mellado, JA;Alados-Arboledas, L
通讯作者：
Alados-Arboledas, L
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.