Characterizing the evolutionary architecture of complex disease within and across diverse populations

项目总结 在过去的十年中，全基因组关联研究(GWASs)见证了数以千计的复杂性状和 疾病研究并确定了数以千计的可重复相关的遗传变异。GWAs提供了帮助 描述常见基因结构的复杂性，阐明遗传学在疾病中的作用 风险。大量研究表明，复杂性状的风险在功能区高度丰富 这表明风险是通过对相关易感性的扰动调节作用而传递的。 基因。同样，最近的多项研究发现，疾病风险是由自然选择的力量塑造的， 这使有害等位基因在种群中的频率保持在较低水平。总而言之，功能机制和 它们与自然选择的相互作用可以在一种我们称之为进化的一般机制下结合起来 建筑。目前推断常见复杂疾病进化架构的框架仅限于 适用于相对同质的群体，如欧洲血统的个体。最近的几部作品 已经证明，整合多种族的Gwas数据大大提高了识别 由于等位基因频率的异质性增加，导致复杂性状和疾病的原因。 当前的方法进化体系结构不能对跨 关于等位基因频率和连锁不平衡的群体。同样，这些问题的解决方案 方法目前仅限于复杂的疾病和表型，其推断的体系结构，而 信息量大，未能描述调解风险的监管网络机制。能够分析的方法 许多分子表型同时有可能识别共享的结构，并精确定位核心 与疾病风险相关的基因。最后，几项工作表明，将功能信息与 GWAS显著改进了多基因风险预测。总而言之，这些问题和机遇突出了 需要可扩展到多个种群和大规模分子的新计算方法 表型目录，同时考虑了潜在的异质性和共同的信号。在这里，我们提出了小说 整合来自多个不同地理位置的群体和表型的地理信息系统数据的方法 描述特定种群和共享进化架构的特征。重要的是，我们的方法运行 直接对汇总数据进行分析，从而能够立即进行大规模分析。我们打算把我们的小说 获取大规模多民族地理信息系统数据的方法。共同努力，我们的工作将系统地描述 复杂疾病的进化架构以及分子表型和种群在强大、开放、 和可重复使用的方法。

PROJECT SUMMARY The past decade of genome-wide association studies (GWASs) has seen thousands of complex traits and diseases studied and identified thousands of reproducibly associated genetic variants. GWAS has helped characterize the complexity of common genetic architectures and shed light on the role of genetics in disease risk. A large body of works have demonstrated that risks of complex traits are highly enriched in functional regions of the genome, which indicates that risk is mediated through perturbed regulatory action on relevant susceptibility genes. Similarly, multiple recent works have found that disease risks are shaped by forces of natural selection, which kept the frequencies of deleterious alleles low in the population. Together, the functional mechanisms and their interplay with natural selection can be coupled under a general mechanism we refer to as the evolutionary architecture. Current frameworks to infer the evolutionary architecture for common complex diseases are only applicable to relatively homogenous populations, such as individuals of European ancestry. Several recent works have demonstrated that integrating multi-ethnic GWAS data substantially improves statistical power to identify causal factors underlying complex traits and diseases due to the increased heterogeneity in allele frequencies. Current approaches evolutionary architecture are unable to appropriately model the heterogeneity across populations with respect to allele frequencies and linkage disequilibrium. Similarly, the resolution of these methods is currently limited to complex diseases and phenotypes, whose inferred architectures, while informative, fail to describe regulatory network mechanisms that mediate risk. Methods capable of analyzing many molecular phenotypes simultaneously have the potential to identify shared architectures, and pinpoint core genes relevant for disease risk. Lastly, several works have shown that integrating functional information with GWAS substantially improves polygenic risk prediction. Together, these issues and opportunities highlight the need for new computational approaches that can scale to multiple populations and large-scale molecular phenotype catalogues while accounting for underlying heterogeneity and shared signals. Here, we propose novel approaches to integrate GWAS data from multiple, geographically diverse, populations and phenotypes to characterize the population-specific and shared evolutionary architectures. Importantly, our approaches run directly on summary data, which enables immediate large-scale analysis. We propose to apply our novel approaches to large-scale multi-ethnic GWAS data. Together, our work will systematically characterize evolutionary architectures for complex diseases and molecular phenotypes and populations in a robust, open, and reproducible approach.