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

项目摘要/摘要 我实验室的研究旨在了解不同复合体之间的遗传和生物学联系 特征和疾病。我们计划使用几种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，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种特征 一次只有一种疾病。虽然对测试关于特定关系的特定假设很重要 这些研究来自流行病学研究，本质上倾向于遗漏不可预见和意想不到的东西。 与其他特征或疾病的因果关系。此外，跨越几个特征的复杂模式和 疾病将会被遗漏。出于这些原因，有必要考虑一种综合了所有 在一个单一、统一的框架(“表现组全图”)中研究特征和疾病的联系。 为此，在未来五年，我们计划开展一系列研究，首先，1)建立一个现象组- 大量人类共同特征和疾病之间的因果联系的广泛地图。这张地图 需要来自全基因组关联的单个单核苷酸变体(SNV)水平关联结果 数据。因此，我已经开始建立一个全基因组关联数据的全面存储库，用于 来自几项研究的数百万SNV和数百种不同的特征、生物标记物和疾病。此数据 涵盖范围广泛的常见疾病，包括心血管疾病、心脏代谢疾病、 炎症性疾病、精神障碍、肾功能等。我们将推断出因果关系 使用关联SNV、特征和疾病的所有组合之间的这个数据存储库来生成 凤眼座的地图。 接下来，我们将通过加入与分子功能相关的信息，将生物链接添加到地图上 基因调控。我们将利用调节元件、细胞类型、 和基因的表达。第三，我们将把每个基因层面的自然选择指标纳入我们的 凤眼座的地图。我们将开发一种方法来预测自然气候的强度和模式 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 探索观察到的不同联系之间的相似性和差异性的现象范围图 特征和疾病。 我们提出的研究计划可以为研究共享的 特征和疾病的病因学。重要的是，我们的研究还具有精准医学应用的方向 它可以告知药物发现工作中新基因靶点的优先顺序。

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.