Towards an integrated map of causal connections for common, complex diseases

项目总结/摘要 我实验室的研究旨在了解不同复杂的基因和生物学联系， 性状和疾病。我们计划使用来自几个不同学科的计算方法，包括 人类遗传学，统计遗传学，表观遗传学和群体遗传学，以绘制出遗传，功能 以及数百种性状和疾病之间的进化联系。 遗传学研究已经推断出许多性状之间的因果关系（re。可衡量的指标 疾病状态的严重性或存在）和疾病。然而，几乎所有这些研究都测试了一个特质 一次只治疗一种疾病虽然对于测试关于特定关系的特定假设很重要， 从流行病学研究中获得，这些研究，从本质上讲，往往会错过不可预见和意外的 与其他性状或疾病的因果关系。此外，跨几个特征的复杂模式， 疾病会被遗漏。出于这些原因，有必要考虑采取一种办法， 在一个单一的、统一的框架内（"全表型图谱"），将性状和疾病联系起来。 为此，在接下来的五年里，我们计划开展一系列研究，首先，1）建立一个表型- 这是一张广泛的地图，展示了人类众多共同特征和疾病之间的因果关系。这张地图 需要来自全基因组关联的单个单核苷酸变异（SNV）水平关联结果 数据因此，我已经开始建立一个全面的全基因组关联数据库， 数以百万计的SNV和数百种不同的性状，生物标志物和疾病的几项研究。该数据 包括心血管疾病，心脏代谢疾病， 炎性疾病、精神障碍、肾功能等。我们将推断因果关系 在相关SNV、性状和疾病的所有组合之间使用该数据储存库来生成 全表型图 接下来，我们将通过整合与2）分子功能相关的信息， 基因调控我们将推断与全表型图谱中每个SNV的联系，包括调控元件、细胞类型， 和基因的表达。第三，我们将把每个基因水平上的自然选择指标纳入我们的 全表型图我们将开发一种方法来预测自然灾害的强度和模式， 在每个基因水平上进行选择，然后将其添加到我们的因果关系图中。最后，我们希望使用 全表型图，以探索在不同的联系之间观察到的相似性和差异， 性状和疾病。 我们提出的研究计划可以提供对共享的生物学机制背后的新的见解。 特征和疾病的病因学。重要的是，我们的研究也有方向精准医学应用， 它可以为药物发现工作提供关于新基因靶点的优先级的信息。

PROJECT SUMMARY / ABSTRACT Research in my laboratory aims to understand the genetic and biological links between different complex traits and diseases. We plan to use computational approaches from several different disciplines, including human genetics, statistical genetics, epigenetics and population genetics to map out the genetic, functional and evolutionary links between hundreds of traits and diseases simultaneously. Genetic studies have inferred causal connections among numerous traits (re. measurable indicators of the severity or presence of a disease state) and disease. However, almost all of these studies have tested one trait with one disease at a time. While important for testing specific hypotheses about specific relationships obtained from epidemiological studies, these studies, by nature, tend to miss unforeseen and unexpected causal connections with other traits or disease. Furthermore, complex patterns across several traits and diseases would be missed. For these reasons, there is a need to consider an approach that incorporates all traits and disease links in a single, unified framework (the `phenome-wide map'). To this end, over the next five years, we plan to embark on a series of studies to first, 1) build a phenome- wide map of causal connections between a multitude of common human traits and diseases. This map requires individual single nucleotide variant (SNV) level association results from genome-wide association data. As a result, I have begun to build a comprehensive repository of genome-wide association data for millions of SNVs and hundreds of different traits, biomarkers and diseases from several studies. This data spans a wide spectrum of common diseases, including cardiovascular disease, cardiometabolic conditions, inflammatory diseases, psychiatric disorders, renal function, amongst others. We will infer causal connections using this repository of data between all combinations of associated SNVs, traits and diseases to generate the phenome-wide map. Next, we will add biological links to the map by incorporating information related to 2) molecular function via gene regulation. We will infer links to each SNV in the phenome-wide map with regulatory elements, cell types, and expression of genes. Third, we will incorporate 3) natural selection metrics at the per gene level into our phenome-wide map. We will develop an approach to make predictions on the strength and mode of natural selection at the per gene level, and then add this to our map of causal connections. Finally, we expect to use the phenome-wide map to explore similarities and differences across the different links observed between the traits and diseases. Our proposed research program can provide insights into new biological mechanisms behind the shared etiology of traits and diseases. Importantly, our research also has direction precision medicine applications as it can inform about prioritization of new gene targets for drug discovery efforts.