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: 1761903
• 负责人: Daisy Philtron
• 金额: $ 25万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761903&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.

了解人类疾病的原因和寻找治疗方法的最重要方法之一是研究人类遗传学。全基因组关联研究（GWAS）已被用于识别与常见疾病和病症相关的基因组区域。 然而，通过这些方法鉴定的遗传变异通常只能解释其已知遗传性的一小部分，并且在发现致病变异方面的记录很差。本项目将开发将联合收割机GWAS与其他数据源结合的工具，如识别重要罕见变异的基于家族的遗传学研究和捕获疾病中基因表达特征的转录组学或蛋白质组学研究，以发现单独使用GWAS完全遗漏的遗传修饰因子。本项目将通过结合不同实验类型的信息来识别与疾病进展相关的基因。将采用的统计模型系列的基本构建块是分层的三组混合分布。每个基因被概率性地建模为属于与疾病进展无关的无效组、与阴性疾病结果相关的有害组或与阳性疾病结果相关的有益组。这三组形式主义有两个关键特征。首先，通过用狄利克雷分布分配组分配的先验概率，所得的后验组概率自动考虑同时分析许多基因所固有的多重性。其次，通过有条件地在组标签上构建实验结果的模型，可以将任何数量的数据模态组合在单个相干概率模型中，从而允许跨实验类型共享信息。这两个特征导致具有很少误报的简约推断，同时增强检测信号的能力。 应用联合分析方法的模型疾病将是帕金森？疾病（PD）。来自PD和对照患者的基因组序列将与来自公共来源的转录组数据和靶向单核苷酸多态性（SNP）阵列数据一起进行沿着分析。 此外，一个强大的成像方法称为机器人显微镜（RM）将用于功能评估的统计模型的预测，从而提供实验反馈的模型。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-神经元中调节预测为有益或有害的基因的水平，并且将量化疾病表型的减轻或加重以验证统计模型的预测或使其无效。 使用三组框架组合基因组、转录组、表型和潜在的其他信息来源的策略可以应用于具有多种数据类型的任何遗传性疾病。 该项目的分析方法将有助于确定哪些基因可能在发病机制中发挥作用，从而产生治疗靶点和潜在的个性化“精准医学”。 这可能直接导致帕金森病的治疗，此外还可以为其他研究人员提供一套有用的工具，以寻求其他遗传性疾病的治疗方法。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。