一种三组非局部模型：结合异质数据源以识别与帕金森病相关的基因
A Three-groups Non-local Model for Combining Heterogeneous Data Sources to Identify Genes Associated with Parkinson's Disease
AI总结：
本文提出一种三组非局部模型，通过整合不同实验类型的数据，提高识别帕金森病相关基因的准确性与鲁棒性。
摘要：
我们通过结合不同实验类型的资料来识别帕金森病（PD）相关基因。每个实验单独来看可能不足以区分一些重要的基因与偶然基因，但通过使用所提出的统计框架整合实验数据，可以增加统计效力。我们所提出的统计模型家族的基本构建块是一个分层三组分布混合模型。每个基因被概率性地归类为与PD无关的零组、有害组或有益组。该三组形式有两个关键特征。通过使用狄利克雷分布分配组归属的先验概率，所得到的后验组概率会自动考虑同时分析许多基因时固有的多重性。通过在组标签条件下构建实验结果的模型，任何数量的数据模态都可以在一个连贯的概率模型中整合，从而在不同实验类型之间共享信息。这两个特征导致了简洁的推断，具有较少的假阳性，同时提高了检测信号的效力。模拟显示，我们的三组方法至少与常用的GWAS和RNA-seq工具表现相当，在某些情况下表现更佳。我们应用所提出的方法于公开的GWAS和RNA-seq数据集，发现了一些潜在的治疗靶点基因。
英文摘要：
We seek to identify genes involved in Parkinson's Disease (PD) by combining information across different experiment types. Each experiment, taken individually, may contain too little information to distinguish some important genes from incidental ones. However, when experiments are combined using the proposed statistical framework, additional power emerges. The fundamental building block of the family of statistical models that we propose is a hierarchical three-group mixture of distributions. Each gene is modeled probabilistically as belonging to either a null group that is unassociated with PD, a deleterious group, or a beneficial group. This three-group formalism has two key features. By apportioning prior probability of group assignments with a Dirichlet distribution, the resultant posterior group probabilities automatically account for the multiplicity inherent in analyzing many genes simultaneously. By building models for experimental outcomes conditionally on the group labels, any number of data modalities may be combined in a single coherent probability model, allowing information sharing across experiment types. These two features result in parsimonious inference with few false positives, while simultaneously enhancing power to detect signals. Simulations show that our three-groups approach performs at least as well as commonly-used tools for GWAS and RNA-seq, and in some cases it performs better. We apply our proposed approach to publicly-available GWAS and RNA-seq datasets, discovering novel genes that are potential therapeutic targets.
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