Leveraging functional data to predict disease risk in multi-ethnic populations

利用功能数据预测多种族人群的疾病风险

• 批准号: 10294593
• 负责人: ALKES L PRICE
• 金额: $ 47.68万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10294593
• 关键词: Affect; African American; Asians; Clinical; Collaborations; Communities; Complex; Computer software; Data; Data Set; Disease; Educational Status; Electronic Health Record; Ethnic Origin; Ethnic group; European; Gene Expression; Gene Frequency; Genome; Genotype; Individual; Latino; Letters; Linkage Disequilibrium; Maps; Mediating; Mental disorders; Methodology; Methods; Minor; Molecular; Outcome; Pattern; Phenotype; Population; Population Heterogeneity; Publications; Quantitative Trait Loci; Research; Residual state; Sample Size; Sampling; Sampling Studies; Scanning; South Asian; Statistical Methods; Structure; Underrepresented Populations; Variant; causal variant; data integration; disorder risk; functional genomics; genetic analysis; genetic architecture; genetic variant; genome wide association study; genome-wide; genomic data; health disparity; improved; multi-ethnic; phenotypic data; polygenic risk score; portability; precision medicine; programs; risk prediction; statistics; success; trait

Project Summary Genome-wide association studies (GWAS) have been broadly successful in identifying genetic variants associated to common disease risk, leading to successes in predicting disease risk in European populations using polygenic risk scores. Unfortunately, there is a large gap in the accuracy of polygenic risk scores between European and non-European populations, such that clinical efforts to improve biomedical outcomes via precision medicine may exacerbate health disparities. The increasing availability of multi-ethnic data in larger sample sizes provides opportunities to improve the accuracy of polygenic risk scores, by improving localization of causal variants and aiding identification of variants with population-specific effects. Notably, functional genomics data has great potential to improve all of these efforts, but has yet to be adequately integrated into multi-ethnic approaches. Here, we propose to reap the advantages of integrative analyses of multi-ethnic and functional data, building on the extensive progress of our research program on disease mapping in multi-ethnic populations over the past 8 years; the focus of our current application is on adapting existing statistical methods to a new setting, integrating multi-ethnic and functional data, which currently suffers a large gap in available methods. Our research will be driven by empirical data from >2,500,000 multi-ethnic samples (>1,500,000 with genotype/phenotype data and >1,000,000 with summary association statistics), including African American, Latino, East Asian and South Asian samples spanning a wide range of diseases and quantitative phenotypes. We will analyze both individual-level data and summary-level data and incorporate functional data sets, including genome-wide functional annotations and gene expression data.

项目摘要 全基因组关联研究（GWAS）在鉴定遗传变异方面取得了广泛的成功 与普通疾病风险有关，从而成功预测欧洲人口的疾病风险 使用多基因风险分数。不幸的是，多基因风险得分的准确性存在很大的差距 在欧洲和非欧洲人群之间，以改善生物医学结果的临床努力 通过精确医学可能会加剧健康差异。多种族数据的可用性增加 较大的样本量通过改进来提高多基因风险评分的准确性提供了机会 因果变体的定位和有助于特定效应的变体的鉴定。尤其， 功能基因组学数据具有改善所有这些努力的巨大潜力，但尚未充分 集成到多种族的方法中。在这里，我们建议从 基于我们疾病研究计划的广泛进步的多种族和功能数据 在过去的8年中，多种族人口的映射；我们当前申请的重点是适应 现有的新设置统计方法，集成了多种族和功能数据，目前会受到影响 可用方法中的较大差距。我们的研究将由> 2,500,000多种族的经验数据驱动 样品（> 1,500,000具有基因型/表型数据，> 1,000,000的样品均具有摘要关联统计）， 包括非洲裔美国人，拉丁裔，东亚和南亚样品，这些样本涉及多种疾病 和定量表型。我们将分析个人级别数据和摘要级别数据，以及 结合功能数据集，包括全基因组功能注释和基因表达数据。