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 过去十年的全基因组关联研究（GWAS）已经发现了数千种复杂的性状， 研究并鉴定了数千种可复制的相关遗传变异。GWAS帮助 描述常见遗传结构的复杂性并揭示遗传学在疾病中的作用 风险大量的研究表明，复杂性状的风险在功能区高度富集 这表明风险是通过对相关易感性的干扰调节作用介导的 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塑造的， 这使得有害等位基因在种群中的频率很低。职能机制和 它们与自然选择的相互作用可以在一个我们称为进化的一般机制下耦合起来。 架构目前推断常见复杂疾病进化结构的框架仅限于 适用于相对同质的人群，如欧洲血统的个体。最近的几个作品 已经证明，整合多种族GWAS数据大大提高了识别的统计能力 由于等位基因频率的异质性增加，导致复杂性状和疾病的因果因素。 当前的方法进化架构无法适当地建模跨 群体中等位基因频率和连锁不平衡。同样，这些决议 方法目前仅限于复杂的疾病和表型，其推断的架构，而 信息，未能描述调节风险的监管网络机制。能够分析 许多分子表型同时具有识别共享结构和精确定位核心的潜力 与疾病风险相关的基因。最后，一些工作表明，将功能信息与 GWAS大大提高了多基因风险预测。这些问题和机遇共同凸显了 需要新的计算方法，可以扩展到多个群体和大规模的分子 表型目录，同时考虑潜在的异质性和共享信号。在这里，我们提出新的 整合来自多个地理上不同的人群和表型的GWAS数据， 描述种群特异性和共享的进化结构。重要的是，我们的方法运行 直接在汇总数据上进行分析，从而实现即时的大规模分析。我们打算把我们的小说 大规模多种族GWAS数据的方法。我们的工作将系统地描述 复杂疾病和分子表型的进化架构和人口在一个强大的，开放的， 可重复的方法。