Multi-ethnic risk prediction for complex human diseases integrating multi-source genetic and non-genetic information

整合多源遗传与非遗传信息的人类复杂疾病多民族风险预测

• 批准号: 10754773
• 负责人: Jin Jin
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-02 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10754773
• 关键词: Acceleration; Advisory Committees; Alzheimer' s Disease; Area; Award; Bayesian Method; Bayesian Modeling; Biological; Biological Markers; COVID-19 mortality; Clinical; Collaborations; Communication; Communities; Complement; Complex; Computing Methodologies; Coronary Arteriosclerosis; Data; Data Set; Data Sources; Disease; Disease model; Disparate; Educational process of instructing; Educational workshop; Ensure; Environment; Epidemiology; Ethnic Origin; Ethnic Population; European; Gene Frequency; Genetic; Genetic Markers; Genetic Risk; Genomics; Goals; Grant; Health; Healthcare; Heart Diseases; Heterogeneity; Individual; Interdisciplinary Study; Interview; Knowledge; Linkage Disequilibrium; Literature; Malignant Neoplasms; Malignant neoplasm of prostate; Measurement; Mentors; Methodology; Methods; Minority Groups; Modeling; Modernization; Odds Ratio; Parameter Estimation; Pathway interactions; Phase; Plasma Proteins; Population; Population Heterogeneity; Predictive Factor; Prevention; Prevention Research; Privatization; Proteins; Public Health; Publishing; Research; Research Personnel; Research Support; Risk; Risk Estimate; Risk Factors; Sample Size; Services; Single Nucleotide Polymorphism; Software Tools; Source; Stroke; Structure; Tissues; Training; Underrepresented Minority; Universities; Urine; Woman; Writing; biobank; career; career development; cell type; data fusion; data integration; disorder risk; epidemiology study; ethnic disparity; ethnic diversity; flexibility; functional genomics; genetic variant; genome wide association study; health inequalities; high dimensionality; human disease; improved; interdisciplinary collaboration; lifestyle factors; machine learning framework; malignant breast neoplasm; men; model building; mortality; mortality risk; multi-ethnic; multidisciplinary; non-genetic; novel; polygenic risk score; precision medicine; predictive tools; programs; racial disparity; risk prediction; risk prediction model; skills; sociodemographics; statistics; success; symposium; tool; trait; user friendly software; user-friendly; Acceleration; Advisory Committees; Alzheimer' s Disease; Area; Award; Bayesian Method; Bayesian Modeling; Biological; Biological Markers; COVID-19 mortality; Clinical; Collaborations; Communication; Communities; Complement; Complex; Computing Methodologies; Coronary Arteriosclerosis; Data; Data Set; Data Sources; Disease; Disease model; Disparate; Educational process of instructing; Educational workshop; Ensure; Environment; Epidemiology; Ethnic Origin; Ethnic Population; European; Gene Frequency; Genetic; Genetic Markers; Genetic Risk; Genomics; Goals; Grant; Health; Healthcare; Heart Diseases; Heterogeneity; Individual; Interdisciplinary Study; Interview; Knowledge; Linkage Disequilibrium; Literature; Malignant Neoplasms; Malignant neoplasm of prostate; Measurement; Mentors; Methodology; Methods; Minority Groups; Modeling; Modernization; Odds Ratio; Parameter Estimation; Pathway interactions; Phase; Plasma Proteins; Population; Population Heterogeneity; Predictive Factor; Prevention; Prevention Research; Privatization; Proteins; Public Health; Publishing; Research; Research Personnel; Research Support; Risk; Risk Estimate; Risk Factors; Sample Size; Services; Single Nucleotide Polymorphism; Software Tools; Source; Stroke; Structure; Tissues; Training; Underrepresented Minority; Universities; Urine; Woman; Writing; biobank; career; career development; cell type; data fusion; data integration; disorder risk; epidemiology study; ethnic disparity; ethnic diversity; flexibility; functional genomics; genetic variant; genome wide association study; health inequalities; high dimensionality; human disease; improved; interdisciplinary collaboration; lifestyle factors; machine learning framework; malignant breast neoplasm; men; model building; mortality; mortality risk; multi-ethnic; multidisciplinary; non-genetic; novel; polygenic risk score; precision medicine; predictive tools; programs; racial disparity; risk prediction; risk prediction model; skills; sociodemographics; statistics; success; symposium; tool; trait; user friendly software; user-friendly

Project Summary/Abstract In genome-wide association studies (GWAS), the lack of data sources for non-European populations results in polygenic risk predictions that could exacerbate health inequity. This racial/ethnic disparity problem exists in many epidemiologic studies and impacts public health much more broadly. Furthermore, the rapid identification of novel risk factors for complex diseases brings increasing opportunities to develop comprehensive risk prediction models to combine information on genetic and other types of risk factors. The scientific goal of this proposal is to provide enhanced disease risk prediction tools for ethnically diverse populations integrating genetic and other data sources across disparate studies. The specific aims include: (Aim 1) develop enhanced multi- ethnic genetic risk prediction models combining ancestry-specific GWAS summary statistics with external genomic information, and extend the method to jointly analyze multiple related diseases; (Aim 2) develop a flexible statistical framework that can integrate ancestry-specific, summary-level risk parameter estimates for genetic markers and a variety of other risk factors to further improve multi-ethnic disease risk prediction; and (Aim 3) develop and validate the risk prediction models for leading causes of mortality and other complex traits/diseases, distribute user-friendly software and tools, and investigate their clinical utilization through applications in precision medicine. Dr. Jin’s long-term goal is to establish an interdisciplinary research program that combines statistical genetics, functional genomics and epidemiology, and develop novel statistical and computational methodologies for integrating multi-source health-related data to improve healthcare and reduce health inequities. This award will facilitate the necessary training required for Jin’s successful transition to independence, including support from the mentoring and advisory committee, advanced coursework, and active participation in collaborations, workshops, and scientific conferences. Jin will gain expertise that complements her current skill set through working closely with a highly multidisciplinary mentoring team with a combined expertise in statistical genetics, genomics, epidemiology, and precision medicine. Johns Hopkins University provides young researchers with an active and engaging intellectual environment, with tremendous opportunities for interdisciplinary collaborations and career development services such as teaching institute, grant writing workshops and interview skills practice. The research supported by this grant will generate enhanced, user-friendly disease risk prediction tools for the underrepresented minority populations, as well as general data integration methodologies that can be widely implemented by the community to accelerate future research in disease risk prediction and prevention. Upon completing this award, Jin will gain a critical set of skills in research, mentoring, communication and management that will ensure her success in establishing an independent research program and pursuing broader career goals.

项目摘要/摘要 在全基因组关联研究（GWAS）中，缺乏非欧洲人群的数据源导致 多基因风险预测可能加剧健康不平等。这个种族/种族差异问题存在于 许多流行病学研究和对公共卫生的影响更广泛。此外，快速识别 复杂疾病的新型风险因素带来了越来越多的机会发展全面风险 预测模型结合了有关遗传和其他类型危险因素的信息。这个科学目标 建议是为融合遗传的种族种群提供增强的疾病风险预测工具 以及跨不同研究的其他数据源。具体目的包括：（目标1）开发增强的多型 种族通用风险预测模型将特定于祖先的GWAS摘要统计数据与外部结合 基因组信息，并将方法扩展到共同分析多种相关疾病的方法； （目标2）开发一个 灵活的统计框架可以整合特定于祖先的，摘要级别的风险参数估计值 遗传标记和各种其他风险因素，以进一步改善多种族疾病风险预测；和 （AIM 3）开发和验证导致死亡和其他复合物的主要原因的风险预测模型 特征/疾病，分发用户友好的软件和工具，并通过 精确医学的应用。 Jin博士的长期目标是建立一个结合统计遗传学的跨学科研究计划， 功能性基因组学和流行病学，并为新颖的统计和计算方法开发 整合多源健康相关数据以改善医疗保健并减少健康不平等。这个奖项将 促进金成功过渡到独立所需的必要培训，包括 心理和咨询委员会，高级课程以及积极参与合作， 讲习班和科学会议。金将获得专业知识，使她目前的技能通过 与高度多学科的指导团队紧密合作，具有统计遗传学的综合专业知识， 基因组学，流行病学和精密医学。约翰·霍普金斯大学为年轻研究人员提供 积极且引人入胜的智力环境，并为跨学科合作提供了巨大的机会 以及职业发展服务，例如教学研究所，授予写作研讨会和面试技能实践。 这项赠款支持的研究将为增强的，用户友好的疾病风险预测工具 代表性不足的少数群体以及可以广泛的一般数据集成方法 由社区实施，以加速未来的疾病风险预测和预防研究。之上 完成此奖项，Jin将获得一系列关键的研究，心理，沟通和管理 这将确保她成功建立独立的研究计划并追求更广泛的职业目标。