Collaborative Research: Combining Heterogeneous Data Sources to Identify Genetic Modifiers of Diseases

合作研究：结合异质数据源来识别疾病的遗传修饰因素

• 批准号: 2309825
• 负责人: Benjamin Shaby
• 金额: $ 25万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309825&HistoricalAwards=false
• 关键词: Collaborative; Research; Combining; Heterogeneous; Data

One of the most important approaches to understanding the causes and finding treatments for human disease is the study of human genetics. Genome Wide Association Studies (GWAS) have been used to identify regions of the genome associated with common diseases and disorders. However, genetic variants identified through these approaches usually explain only a small fraction of their known heritability and have yielded a poor record of finding disease-causing variants. This project will develop tools to combine GWAS with other sources of data, such as family-based genetic studies that identify important rare variants and transcriptomic or proteomic studies that capture gene expression signatures in disease, to find genetic modifiers that would be entirely missed using GWAS alone.This project will identify genes involved in disease progression by combining information across different experimental types. The fundamental building block of the family of statistical models that will be employed is a hierarchical three-group mixture of distributions. Each gene is modeled probabilistically as belonging to either a null group that is unassociated with disease progression, a deleterious group that is associated with negative disease outcomes, or a beneficial group that is associated with positive disease outcomes. This three-group formalism has two key features. First, 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. Second, 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. The model disease for applying the combined analysis approach will be Parkinson?s Disease (PD). Genomic sequences from PD and control patients will be jointly analyzed along with transcriptomic data from public sources and targeted single nucleotide polymorphism (SNP) array data. In addition, a powerful imaging approach called robotic microscopy (RM) will be used to functionally evaluate the predictions of the statistical model thereby providing experimental feedback to the model. Using human neurons derived from PD patient induced pluripotent stem cells (i-neurons) and RM, levels of genes predicted to be beneficial or deleterious will be modulated in the PD i-neurons, and mitigation or exacerbation of disease phenotypes will be quantified to validate or invalidate predictions of the statistical model. The strategy of combining genomic, transcriptomic, phenotypic, and potentially other sources of information using the three-groups framework can be applied to any heritable disease with multiple data types available. The analytical approach in this project will help identify which genes are likely to play a role in pathogenesis, resulting in therapeutic targets and potentially individualized "precision medicine". This could lead directly to treatments for PD, and in addition could provide a useful set of tools for other researchers to pursue therapies for other heritable diseases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

理解原因和寻找人类疾病治疗的最重要方法之一是对人类遗传学的研究。基因组广泛的关联研究（GWAS）已用于确定与常见疾病和疾病相关的基因组区域。 但是，通过这些方法鉴定出的遗传变异通常仅解释其已知遗传力的一小部分，并产生了发现引起疾病变异的差记录。该项目将开发工具将GWA与其他数据源相结合，例如基于家庭的遗传研究，这些研究鉴定了重要的稀有变异和转录组或蛋白质组学研究，这些研究捕获了疾病中的基因表达特征，以发现单独使用GWA的遗传修饰剂，这些遗传修饰剂单独使用GWAS。这将通过在不同的实验类型中结合信息来识别疾病进展。将要使用的统计模型家族的基本组成部分是分布的分层三组混合物。每个基因概率地建模为属于与疾病进展无关的无效基团，这是与疾病疾病结果相关的有害群体，或者是与阳性疾病结果相关的有益群体。这种三组形式主义具有两个关键特征。首先，通过将小组分配的先验概率与差异分布分配，结果后组概率自动考虑了同时分析许多基因固有的多重性。其次，通过在组标签上有条件地构建实验结果的模型，可以将任何数量的数据模式都合并为单个相干概率模型，从而允许跨实验类型的信息共享。这两个功能导致简约的推断，几乎没有假阳性，同时增强了检测信号的功率。 应用合并分析方法的模型疾病将是帕金森氏病（PD）。将共同分析来自PD和对照患者的基因组序列，以及来自公共来源的转录组数据，并有针对性的单核苷酸多态性（SNP）阵列数据。 此外，一种称为机器人显微镜（RM）的强大成像方法将用于在功能上评估统计模型的预测，从而为模型提供实验反馈。使用源自PD患者诱导的多能干细胞（I-神经元）和RM的人神经元，将在PD I-Neurrons中调节预测为有益或有害的基因水平，并将缓解或加剧疾病表型的缓解或加剧，以量化或不适用于验证或不适用于统计模型的预测。 使用三组框架结合基因组，转录组，表型和潜在信息来源的策略可以应用于任何可遗传的疾病，并具有多种数据类型。 该项目中的分析方法将有助于确定哪些基因可能在发病机理中发挥作用，从而导致治疗靶标和潜在的个性化“精度医学”。 这可能会直接导致PD治疗，此外，还可以为其他研究人员提供一组有用的工具，以便为其他可遗传的疾病寻求治疗。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的审查标准通过评估来获得支持。