Methods for Enhancing Polygenic Risk Prediction Models for Complex Disease

增强复杂疾病多基因风险预测模型的方法

基本信息

  • 批准号:
    10717244
  • 负责人:
  • 金额:
    $ 80.48万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-07-01 至 2027-04-30
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY Early screening and prevention of individuals at risk of complex diseases are important strategies for reducing morbidity and mortality. Polygenic risk scores (PRS) are the cumulative, mathematical aggregation of risk derived from the contributions of many DNA variants across the genome. PRS are an emerging technology in the field of disease risk prediction and have been shown to be correlated with disease incidence. While PRS have shown great promise for complex diseases, current PRS models are overly simplistic and have limited predictive power and clinical utility. PRS do not account for the effects of rare genetic variants or other risk factors (clinical, environmental, social determinants of health) on disease risk. Rare variants generally have greater effects on disease risk due to selective pressure, but only a small number of individuals carry any single rare variant. The sparsity of rare variants makes it difficult to directly incorporate them into PRS. Additionally, while it is known that clinical, environmental, and social risk factors also influence risk, few analyses have successfully integrated PRS with these important non-genetic factors. To address this issue, we will develop novel translational informatics methods that integrate clinical, environmental, and genetic data to improve disease risk prediction. We will assess the clinical utility of these integrated risk prediction models using cardiovascular disease (CVD) to evaluate the potential for translation to clinical use. Based on the complexity of CVD, we hypothesize that a comprehensive range of risk factors along with rare variants need to be incorporated into PRS to improve the risk prediction and maximize the clinical utility of PRS for CVD. To achieve our goal, our specific aims are: 1) To develop novel methods that incorporate rare genetic variants into Polygenic Risk Scores (PRS); 2) To evaluate Integrated Risk Models that combine clinical, environmental, and social risk factors with PRS; 3) To develop and evaluate deep learning models integrating genetic, clinical, environmental, and social risk factors; 4) To translate our integrated models into the electronic health record (EHR). If these specific aims are achieved, we will have a set of integrated models that can be used in downstream clinical implementation programs to ultimately have a translational impact on disease treatment and prevention. Using these novel computational risk prediction models for precision health, along with our EHR integration approaches, will allow for the translation of integrated risk prediction into routine clinical care.
项目总结

项目成果

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Dokyoon Kim其他文献

Dokyoon Kim的其他文献

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{{ truncateString('Dokyoon Kim', 18)}}的其他基金

Translational big data analytic approaches to advance drug repurposing for Alzheimer's disease
转化大数据分析方法促进阿尔茨海默氏病的药物再利用
  • 批准号:
    10175930
  • 财政年份:
    2021
  • 资助金额:
    $ 80.48万
  • 项目类别:
Translational big data analytic approaches to advance drug repurposing for Alzheimer's disease
转化大数据分析方法促进阿尔茨海默氏病的药物再利用
  • 批准号:
    10405522
  • 财政年份:
    2021
  • 资助金额:
    $ 80.48万
  • 项目类别:
Translational big data analytic approaches to advance drug repurposing for Alzheimer's disease
转化大数据分析方法促进阿尔茨海默氏病的药物再利用
  • 批准号:
    10613975
  • 财政年份:
    2021
  • 资助金额:
    $ 80.48万
  • 项目类别:
Unravelling genetic basis of comorbidity using EHR-linked biobank data
使用与 EHR 相关的生物库数据揭示合并症的遗传基础
  • 批准号:
    10034691
  • 财政年份:
    2020
  • 资助金额:
    $ 80.48万
  • 项目类别:
Unravelling genetic basis of comorbidity using EHR-linked biobank data
使用与 EHR 相关的生物库数据揭示合并症的遗传基础
  • 批准号:
    10224747
  • 财政年份:
    2020
  • 资助金额:
    $ 80.48万
  • 项目类别:
Unravelling genetic basis of comorbidity using EHR-linked biobank data
使用与 EHR 相关的生物库数据揭示合并症的遗传基础
  • 批准号:
    10687123
  • 财政年份:
    2020
  • 资助金额:
    $ 80.48万
  • 项目类别:
Unravelling genetic basis of comorbidity using EHR-linked biobank data
使用与 EHR 相关的生物库数据揭示合并症的遗传基础
  • 批准号:
    10460229
  • 财政年份:
    2020
  • 资助金额:
    $ 80.48万
  • 项目类别:
Unravelling genetic basis of comorbidity using EHR-linked biobank data
使用与 EHR 相关的生物库数据揭示合并症的遗传基础
  • 批准号:
    10372247
  • 财政年份:
    2020
  • 资助金额:
    $ 80.48万
  • 项目类别:
Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease
将神经影像、多组学和临床数据整合到复杂疾病中
  • 批准号:
    9916801
  • 财政年份:
    2017
  • 资助金额:
    $ 80.48万
  • 项目类别:
Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease
将神经影像、多组学和临床数据整合到复杂疾病中
  • 批准号:
    9287487
  • 财政年份:
    2017
  • 资助金额:
    $ 80.48万
  • 项目类别:

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