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.

项目总结/文摘