A multiple-phenotype imputation method for genetic studies.
A multiple-phenotype imputation method for genetic studies.
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DOI：
10.1038/ng.3513
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发表时间：
2016-04
期刊：
影响因子：
30.8
通讯作者：
Marchini J
中科院分区：
文献类型：
--
作者：
Dahl A;Iotchkova V;Baud A;Johansson Å;Gyllensten U;Soranzo N;Mott R;Kranis A;Marchini J
遗传关联研究产生了丰富的生物学发现。然而，这些方法大多一次分析一个性状和一个SNP，因此无法捕获这些数据集的潜在复杂性。复杂的高维数据集的联合基因型-表型分析代表了超越简单GWAS的重要途径，具有巨大的潜力。向高维表型的转变将带来许多新的统计问题。在本文中，我们解决了缺失的表型在研究中与任何水平的样本之间的相关性的核心问题。我们提出了一个多表型混合模型，并使用计算效率高的变分贝叶斯算法来拟合模型。在来自一系列生物体和性状类型的各种模拟和真实的数据集上，我们证明了我们的方法优于统计学和机器学习文献中现有的最先进方法，并且可以增强关联信号。
Genetic association studies have yielded a wealth of biologic discoveries. However, these have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of these datasets. Joint genotype-phenotype analyses of complex, high-dimensional datasets represent an important way to move beyond simple GWAS with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. In this paper we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real datasets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.