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：
Dokyoon Kim
金额：
$ 80.48万
依托单位：
UNIVERSITY OF PENNSYLVANIA
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

来源：
https://reporter.nih.gov/project-details/10717244
关键词：
Address Adopted African ancestry Area Atrial Fibrillation Cardiomyopathies Cardiovascular Diseases Cardiovascular system Clinical Complex Coronary Arteriosclerosis DNA Data Development Disease Early Diagnosis Early identification Electronic Health Record Electronic Medical Records and Genomics Network Emerging Technologies European ancestry Genetic Genetic Predisposition to Disease Genetic Risk Genome Goals Health Personnel Heart failure Incidence Individual Informatics Investigation Life Machine Learning Mathematics Medicine Methods Modeling Morbidity - disease rate Phenotype Precision Health Prevention Prevention strategy Preventive therapy Public Health Research Risk Risk Factors Scoring Method Surveys Testing Translating Translations Variant Vascular Diseases Veterans biobank cardiac muscle disease cardiovascular disorder risk cardiovascular risk factor clinical care clinical decision support clinical implementation clinical risk cohort data integration deep learning model design disorder prevention disorder risk early screening effective therapy exome genetic information genetic risk factor genetic variant genome-wide heart rhythm high risk population improved innovation machine learning method mortality non-genetic novel polygenic risk score predictive modeling pressure programs prototype rare variant response risk prediction risk prediction model social social health determinants statistics trait translational impact translational potential whole genome

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.

项目摘要早期筛查和预防具有复杂疾病风险的个体是减少的重要策略发病率和死亡率。多基因风险评分（PR）是累积的，数学聚集的风险根据整个基因组的许多DNA变体的贡献。 PR是该领域的新兴技术疾病风险预测，已被证明与疾病发生率相关。虽然PR已显示对于复杂疾病的巨大希望，当前的PRS模型过于简单，预测能力有限和临床实用程序。 PR不考虑罕见遗传变异或其他危险因素的影响（临床，对疾病风险的环境，社会决定因素。稀有变体通常对疾病风险是由于选择性压力而引起的，但只有少数人携带任何稀有变体。这稀有变体的稀疏性使得很难将它们直接纳入PR。另外，虽然知道临床，环境和社会风险因素也会影响风险，很少有分析成功地整合了PR 与这些重要的非遗传因素。为了解决这个问题，我们将开发新颖的翻译信息学方法来整合临床，环境和遗传数据，以改善疾病风险预测。我们将评估这些临床实用性使用心血管疾病（CVD）评估翻译的可能性的综合风险预测模型临床用途。根据CVD的复杂性，我们假设沿途全面的风险因素需要将罕见的变体纳入PR，以改善风险预测并最大化临床实用程序 CVD的PR。为了实现我们的目标，我们的具体目的是：1）开发包含稀有遗传变异的新型方法进入多基因风险评分（PRS）； 2）评估结合临床，环境的综合风险模型和PRS的社会风险因素； 3）开发和评估整合遗传，临床的深度学习模型，环境和社会风险因素； 4）将我们的集成模型转化为电子健康记录（EHR）。如果实现了这些特定目标，我们将拥有一组可以使用的集成模型下游临床实施计划，最终对疾病治疗和预防。使用这些新颖的计算风险预测模型，以进行精确健康以及我们的EHR 整合方法将允许将综合风险预测转化为常规临床护理。