权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10653221
负责人：
Nicholas Mancuso
金额：
$ 71.92万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
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/10653221
关键词：
Address Alleles Architecture Atlases Awareness Biological Assay Catalogs Cells Complex Coupled Data Disease Ethnic Origin Etiology European ancestry Exhibits Frequencies Gene Frequency Genes Genetic Genetic Models Genetic Risk Genets Genome Genomics Geography Heritability Heterogeneity Individual Inherited Linkage Disequilibrium Maps Mediating Messenger RNA Methods Modeling Molecular Natural Selections Pathway interactions Performance Peripheral Phenotype Population Population Heterogeneity Procedures Reduce health disparities Reporter Reproducibility Research Personnel Resolution Risk Role Running Sample Size Shapes Signal Transduction Susceptibility Gene Testing Translating Work causal variant direct application disease phenotype disorder risk disparity reduction 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

项目摘要

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.

项目总结

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics.

估计由当地血统解释的遗传力，并根据汇总统计评估混合映射中的分层偏差。

DOI：
10.1101/2023.04.10.536252
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Chan,TszFung;Rui,Xinyue;Conti,DavidV;Fornage,Myriam;Graff,Mariaelisa;Haessler,Jeffrey;Haiman,Christopher;Highland,HeatherM;Jung,SuYon;Kenny,Eimear;Kooperberg,Charles;Marchland,LoicLe;North,KariE;Tao,Ran;Wojcik,Genevieve;
通讯作者：