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: 10349828
• 负责人: Jin Jin
• 金额: $ 9.5万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10349828
• 关键词: 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; Educational process of instructing; Educational workshop; Ensure; Environment; Epidemiology; Ethnic group; European; Gene Frequency; Genetic; Genetic Markers; Genetic Risk; Genomics; Goals; Grant; Health; Healthcare; Heart Diseases; Heterogeneity; Individual; Institutes; Interdisciplinary Study; Interview; Knowledge; Linkage Disequilibrium; Literature; Malignant Neoplasms; Malignant neoplasm of prostate; Measurement; Mentors; Methodology; Methods; Minority Groups; Modeling; Modernization; Odds Ratio; Pathway interactions; Phase; Plasma Proteins; Population; Population Heterogeneity; Predictive Factor; Prevention; Prevention Research; Privatization; 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; base; 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; genomic epidemiology; 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; programs; protein metabolite; racial and ethnic disparities; 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中，缺乏非欧洲人群的数据源导致 可能加剧健康不平等的多基因风险预测。这一种族/民族差距问题存在于 许多流行病学研究和对更广泛的公共卫生的影响。此外，快速识别 复杂疾病的新风险因素带来了更多发展综合风险的机会 预测模型结合了遗传和其他类型的风险因素的信息。它的科学目标是 提案是为整合基因的不同种族人群提供增强的疾病风险预测工具 和其他跨不同研究的数据来源。具体目标包括：(目标1)发展增强型多功能 结合血统统计和外部统计的种族遗传风险预测模型 基因组信息，并将该方法扩展到联合分析多种相关疾病；(目标2)开发 灵活的统计框架，可以集成特定于祖先的、摘要级别的风险参数估计 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；以及 (目标3)开发和验证主要死亡原因和其他复杂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，并通过以下方式调查其临床应用 在精准医学中的应用。 金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项目， 功能基因组学和流行病学，并开发新的统计和计算方法 整合多源健康相关数据，以改善医疗保健，减少健康不平等现象。这一奖项将 为金成功过渡到独立所需的必要培训提供便利，包括从 指导和咨询委员会，高级课程，以及积极参与合作， 研讨会和科学会议。金将获得专业知识，以补充她目前的技能集 与高度多学科的指导团队密切合作，拥有统计遗传学方面的综合专业知识， 基因组学、流行病学和精准医学。约翰霍普金斯大学为年轻的研究人员提供了 积极主动的知识环境，为跨学科合作提供了巨大机会 以及职业发展服务，如教学学院、赠款写作研讨会和面试技能练习。 这项拨款资助的研究将为 未被充分代表的少数群体，以及可广泛推广的一般数据整合方法 由社区实施，以加快未来疾病风险预测和预防的研究。vt.在.的基础上 完成这一奖项后，金将获得一套关键的研究、指导、沟通和管理技能 这将确保她成功地建立一个独立的研究项目，并追求更广泛的职业目标。