Methods for Enhancing Polygenic Risk Prediction Models for Complex Disease

项目摘要 对有复杂疾病风险的个人进行早期筛查和预防是减少艾滋病毒/艾滋病感染的重要战略。 发病率和死亡率。多基因风险评分（PRS）是累积的，数学聚合的风险， 基因组中许多DNA变异的贡献。PRS是该领域的新兴技术 的疾病风险预测，并已被证明与疾病的发病率。虽然PRS显示 虽然PRS模型对复杂疾病有很大的前景，但目前的PRS模型过于简单，预测能力有限 和临床实用性。PRS不能解释罕见遗传变异或其他风险因素的影响（临床， 健康的环境、社会决定因素）对疾病风险的影响。罕见的变异通常对 由于选择压力，疾病风险很小，但只有少数人携带任何单一的罕见变异。的 罕见变异体的稀疏性使得难以将它们直接并入PRS中。此外，虽然已知 临床、环境和社会风险因素也会影响风险，很少有分析成功整合了PRS 这些重要的非遗传因素。 为了解决这个问题，我们将开发新的翻译信息学方法， 环境和遗传数据，以改善疾病风险预测。我们将评估这些的临床效用 使用心血管疾病（CVD）的综合风险预测模型，以评估转化为 临床应用。基于心血管疾病的复杂性，我们假设一系列全面的危险因素沿着 需要将罕见变异纳入PRS，以改善风险预测并最大限度地提高临床效用 心血管疾病的PRS。 为了实现我们的目标，我们的具体目标是：1）开发新的方法，将罕见的遗传变异 多基因风险评分（PRS）; 2）评价结合联合收割机临床、环境 和社会风险因素; 3）开发和评估深度学习模型，整合遗传，临床， 环境和社会风险因素; 4）将我们的综合模型转化为电子健康记录 （EHR）。如果这些具体目标得以实现，我们将拥有一套可用于以下方面的综合模式： 下游临床实施计划，最终对疾病治疗产生转化影响， 预防使用这些新的计算风险预测模型进行精确健康，沿着我们的EHR 整合方法将允许将整合的风险预测转化为常规的临床护理。

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.