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.

项目摘要 全基因组关联研究（GWASS）的过去十年已经看到了成千上万的复杂特征和 疾病研究并确定了数千种可重复相关的遗传变异。 GWAS提供了帮助 表征常见遗传结构的复杂性，并阐明了遗传学在疾病中的作用 风险。大量作品表明，复杂性状的风险在功能区域高度丰富 基因组，这表明风险是通过对相关易感性的扰动调节作用介导的 基因。同样，最近的多项作品发现，疾病风险是由自然选择的力量塑造的， 这使人口中有害等位基因的频率保持较低。在一起，功能机制和 它们与自然选择的相互作用可以在我们称为进化的一般机制下耦合 建筑学。当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，例如欧洲血统的个人。最近的一些作品 已经证明，整合多种族GWAS数据可大大提高统计能力以识别 由于等位基因频率的异质性增加而引起的复杂性状和疾病的原因。 当前的进化架构无法适当建模跨越的异质性 人口相对于等位基因频率和连锁不平衡。同样，这些解决方案 方法目前仅限于复杂的疾病和表型，它们的推断结构，而 信息性，无法描述介导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体系结构的潜力，并确定了核心 与疾病风险相关的基因。最后，几项作品表明，将功能信息与 GWAS大大改善了多基因风险预测。这些问题和机遇共同强调了 需要新的计算方法，可以扩展到多个人群和大规模分子 表型目录在考虑基本异质性和共享信号的同时。在这里，我们提出小说 将来自多个，地理上不同，人群和表型的GWA数据整合到的方法 表征人口特定和共享的进化体系结构。重要的是，我们的方法运行 直接基于摘要数据，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。在一起，我们的工作将系统地描述 在强大，开放的情况下，用于复杂疾病和分子表型和种群的进化体系结构 和可再现的方法。