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

表征不同人群内部和不同人群之间复杂疾病的进化结构

• 批准号: 10302919
• 负责人: Nicholas Mancuso
• 金额: $ 72.55万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10302919
• 关键词: Accounting; Address; Alleles; Architecture; Atlases; Awareness; Biological Assay; Catalogs; Cells; Complex; Coupled; Data; Disease; Etiology; European; Exhibits; Frequencies; Gene Frequency; Genes; Genetic; Genetic Models; Genetic Risk; Genets; Genome; Genomics; Geography; Heritability; Heterogeneity; Individual; Inherited; Light; Linkage Disequilibrium; Mediating; Messenger RNA; Methods; Modeling; Molecular; Natural Selections; Pathway interactions; Performance; Peripheral; Phenotype; Population; Population Heterogeneity; Procedures; Reporter; Reproducibility; Research Personnel; Resolution; Risk; Role; Running; Sample Size; Shapes; Signal Transduction; Susceptibility Gene; Testing; Translating; Weight; Work; base; causal variant; direct application; disease phenotype; disorder risk; experimental study; functional genomics; genetic architecture; genetic variant; genome wide association study; genome-wide; health disparity; improved; large scale data; men; metabolic abnormality assessment; molecular modeling; molecular phenotype; molecular scale; molecular shape; multi-ethnic; novel; novel strategies; open source; polygenic risk score; population stratification; pressure; risk prediction; statistics; trait; transcriptomics

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.

项目概要 过去十年的全基因组关联研究（GWAS）已经发现了数千个复杂的性状和 研究并鉴定了数千种可重复相关的遗传变异。 GWAS 提供了帮助 描述常见遗传结构的复杂性并阐明遗传学在疾病中的作用 风险。大量研究表明，复杂特征的风险在功能区域高度丰富 基因组的变化，这表明风险是通过对相关易感性的扰动监管行动来介导的 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决定的， 这使得人群中有害等位基因的频率保持在较低水平。功能机制和 它们与自然选择的相互作用可以在我们称之为进化的一般机制下耦合起来 建筑学。目前推断常见复杂疾病进化架构的框架仅是 适用于相对同质的人群，例如欧洲血统的个体。近期几部作品 已经证明，整合多种族 GWAS 数据可显着提高统计能力，以识别 由于等位基因频率异质性增加，导致复杂性状和疾病的致病因素。 当前的进化架构方法无法正确建模跨系统的异质性 群体的等位基因频率和连锁不平衡。同样，这些的分辨率 目前的方法仅限于复杂的疾病和表型，其推断的结构，而 信息丰富，未能描述调解风险的监管网络机制。能够分析的方法 许多分子表型同时具有识别共享结构并精确定位核心的潜力 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 显着改善了多基因风险预测。这些问题和机遇共同凸显了 需要能够扩展到多个群体和大规模分子的新计算方法 表型目录，同时考虑潜在的异质性和共享信号。在这里，我们推荐新颖的 整合来自多个、地理上不同的人群和表型的 GWAS 数据的方法 描述特定人群和共享进化架构的特征。重要的是，我们的方法运行 直接基于汇总数据，从而可以立即进行大规模分析。我们建议应用我们的小说 大规模多种族 GWAS 数据的方法。我们的工作将共同系统地描述 复杂疾病、分子表型和种群的进化结构，以稳健、开放、 和可重复的方法。