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

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.

项目概要/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会加剧健康不平等问题。 许多流行病学研究对公共健康产生了更广泛的影响。此外，快速识别。 复杂疾病的新危险因素带来了越来越多的发展综合风险的机会 结合遗传和其他类型风险因素信息的预测模型。 建议为不同种族人群提供增强的疾病风险预测工具，整合遗传基因 以及不同研究中的其他数据源，具体目标包括：（目标 1）开发增强型多方数据。 种族遗传风险预测模型将特定血统的 GWAS 摘要统计数据与外部数据相结合 基因组信息，并扩展联合分析多种相关疾病的方法（目标2）开发一种 灵活的统计框架，可以整合特定于血统的汇总级风险参数估计 遗传标记和各种其他风险因素，以进一步改进多种族疾病风险预测；以及 （目标 3）开发并验证死亡主要原因和其他复杂因素的风险预测模型 性状/疾病，分发用户友好的软件和工具，并通过以下方式研究其临床应用 精准医疗中的应用。 金博士的长期目标是建立一个结合统计遗传学、 功能基因组学和流行病学，并开发新的统计和计算方法 该奖项将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。 促进金成功过渡到独立所需的必要培训，包括来自 指导和咨询委员会、高级课程以及积极参与合作， Jin 将通过研讨会和科学会议获得补充她当前技能的专业知识。 与具有统计遗传学综合专业知识的高度多学科指导团队密切合作， 约翰霍普金斯大学为年轻研究人员提供了基因组学、流行病学和精准医学的机会。 活跃且引人入胜的智力环境，提供跨学科合作的巨大机会 以及职业发展服务，例如教学机构、资助写作研讨会和面试技巧练习。 这笔赠款支持的研究将为人们提供增强的、用户友好的疾病风险预测工具。 代表性不足的少数群体，以及可以广泛应用的通用数据集成方法 由社区实施，以加速疾病风险预测和预防的未来研究。 完成该奖项后，Jin 将获得研究、指导、沟通和管理方面的一系列关键技能 这将确保她成功建立独立研究计划并追求更广泛的职业目标。