Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks.
Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks.
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DOI：
10.1016/j.isci.2022.104553
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发表时间：
2022-07-15
期刊：
影响因子：
5.8
通讯作者：
Crawford, Lorin
中科院分区：
文献类型：
--
作者：
Cheng, Wei;Ramachandran, Sohini;Crawford, Lorin
在本文中，我们提出了一种新的方法，变量选择使用贝叶斯神经网络的集合，重点是量化的不确定性，其中变量被选择。受统计遗传学中精细映射应用的启发，我们将我们的框架称为“单效应神经网络集成”（ESNN），它通过考虑基因型数据中的非线性结构（例如，显性效应）并具有模拟离散表型的能力（例如，病例对照研究）。通过广泛的模拟，我们证明了我们的方法能够产生校准后验摘要，如可信集和后验包含概率，特别是对于具有相关变体驱动的显著比例的非加性变异的遗传结构的性状。最后，我们使用真实的数据来证明ESNN框架改进了现有技术，用于识别各种复杂性状的真实效应变量。使用单效应神经网络集成的非线性遗传精细映射通过可信集对变量选择的不确定性进行后验量化改进了模拟中可信集的覆盖率和变量选择的功效数据分析揭示了非线性地促进表型变异的变异遗传学;生物信息学;人工智能
In this paper, we propose a new approach for variable selection using a collection of Bayesian neural networks with a focus on quantifying uncertainty over which variables are selected. Motivated by fine-mapping applications in statistical genetics, we refer to our framework as an “ensemble of single-effect neural networks” (ESNN) which generalizes the “sum of single effects” regression framework by both accounting for nonlinear structure in genotypic data (e.g., dominance effects) and having the capability to model discrete phenotypes (e.g., case-control studies). Through extensive simulations, we demonstrate our method’s ability to produce calibrated posterior summaries such as credible sets and posterior inclusion probabilities, particularly for traits with genetic architectures that have significant proportions of non-additive variation driven by correlated variants. Lastly, we use real data to demonstrate that the ESNN framework improves upon the state of the art for identifying true effect variables underlying various complex traits. Nonlinear genetic fine-mapping using an ensemble of single-effect neural networks Posterior quantification of uncertainty for variable selection via credible sets Improved coverage of credible sets and power for variable selection in simulations Data analyses reveal variants that nonlinearly contribute to phenotypic variation Genetics; Bioinformatics; Artificial intelligence
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影响因子：
64.8
作者：
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Marchini J
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DOI：
10.1098/rstb.2005.1669
发表时间：
2005-07-29
影响因子：
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作者：
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Felsenstein, J