Genotype First: Actionable Genetic Risk through Genotype-to-Phenotype Prediction

项目摘要 疾病的早期预防、发现和干预是促进人类健康的根本目标。与此同时，基因 无论出于何种目的，风险都是常见的、可遗传的疾病风险的最早重要贡献者。因此，从理论上讲， 基因图谱应该是疾病早期预防的理想工具。然而，遗传因素很少被直接用来预测 未来的疾病风险。相反，遗传信息通常归类于表型优先情景：提供或确认 对有明显疾病的个人的诊断或对有强烈家族病史的个人的遗传风险的澄清 疾病。现代基因组学要对疾病预防产生重大影响，基因组信息的使用必须 过渡到基因优先的方法；预测其他健康个体的遗传病风险。一大障碍 这一过渡包括我们有限的预测疾病风险和可能的表型表现的能力 遗传和其他风险因素对个体的影响。疾病风险程度和表型表达传递给任何 遗传因素导致的单个个体是直接和间接遗传效应之间复杂相互作用的结果，其他 不可更改的风险因素(年龄、性别、血统、家族史)和中间可更改的风险因素(环境、 行为、实验室价值、健康状况、治疗状况等)它们中的许多都有自己的直接遗传介体。新的 需要采取方法来剖析这种相互作用，以便个性化和情境化大多数预防性行动 有效降低总体疾病风险。这项提议的总体目标是发展创新型深度学习 以及将基线遗传风险预测与传统风险测量相结合的机器学习方法 因素，以提供更准确和可操作的疾病风险预测。将遗传风险与传统风险捆绑在一起 因素，特别是可修改的风险因素，我们将通过允许确定预防措施来实现可操作性 可能特别有效的行动，因为它们抵消了遗传风险，以及识别可改变的风险 在遗传易感性增加的情况下，应主动监测和控制的因素。为了实现这一目标， 我们建议开发方法来：(1)通过空间分布推断单基因风险变异的可能表型表达 协方差机器学习方法，(2)预测疾病流行病例和期望值的中间值可修改 危险因素来自多基因和其他不可改变的危险因素，最后(3)通过 深部基线遗传期望与观察(测量)的中间可改变风险因素之间的相互作用 学习框架。调整这些训练模型中的年龄和可修改的风险因素，然后将允许交互 预测未来的疾病风险，并确定可修改的风险因素，当这些因素被操纵时，会导致最大的 未来疾病风险的变化。考虑到冠状动脉疾病的公共健康，我们专注于开发治疗方法 重要性，多基因风险评估的已知效用，以及多基因与环境相关的现有证据 互动。此外，我们提出的方法直接与现有的冠心病临床决策支持工具集成在一起 动脉疾病管理。然而，我们将构建一个通用框架，该框架可以扩展到任何常见的可遗传 成人发病的情况，尤其是那些已知可遗传的、传统的危险因素

Project Summary Early disease prevention, detection, and intervention are fundamental goals for advancing human health. Meanwhile, genetic risk is, for all intents and purposes, the earliest significant contributor to common, heritable, disease risk. Thus, in theory, genetic profiling should be the ideal tool for early disease prevention. Yet, genetic factors are rarely used directly to predict future disease risk. Rather, genetic information is typically relegated to phenotype-first scenarios: providing or confirming diagnoses for individuals with overt disease or clarifying the genetic risk for individuals with a strong family history of disease. For modern genomics to make a significant impact on disease prevention the use of genomic information must transition to a genotype-first approach; prediction of genetic disease risk in otherwise healthy individuals. A major barrier to this transition includes our limited ability to predict the precise array of risks and likely phenotypic expression of disease in an individual from genetic and other risk factors. The degree of disease risk and phenotypic expression conveyed to any single individual by genetic factors is a result of a complex interplay between direct and indirect genetic effects, other unmodifiable risk factors (age, gender, ancestry, family history), and intermediate modifiable risk factors (environment, behavior, laboratory values, health status, therapy status, etc.) many of which have their own direct genetic mediators. New approaches are required to dissect this interplay in order to personalize and contextualize preventative actions that most effectively reduce overall disease risk. The overarching goal of this proposal is the development of innovative Deep learning and machine-learning approaches to integrate baseline genetic risk predictions with the measurement of traditional risk factors in order to provide more accurate and actionable predictions of disease risk. By tying genetic risk to traditional risk factors, especially modifiable risk factors, we will enable actionability by allowing both a determination of preventative actions that may be especially effective because they offset genetic risk, as well as the identification of modifiable risk factors that should be monitored and controlled proactively given increased genetic predisposition. To accomplish this goal, we propose to develop methods to: (1) infer the likely phenotypic expressivity of monogenic risk variants via a spatial covariance machine learning approach, (2) predict prevalent disease cases and the expected value of intermediate modifiable risk factors from polygenic and other unmodifiable risk factors, and finally (3) predict prevalent disease cases through interactions between baseline genetic expectations and observed (measured) intermediate modifiable risk factors in a deep learning framework. Adjusting age and modifiable risk factors in these trained models would then allow for the interactive projection of future disease risk and the identification of modifiable risk factors that, when manipulated, lead to the greatest change in future disease risk. We focus on the development of methods for coronary artery disease given its public health importance, the known utility of polygenic risk estimation, and the current evidence for polygene-by-environment interactions. In addition, the approach we propose integrates directly with current clinical decision support tools for coronary artery disease management. However, we will build a general framework that can be extended to any common heritable adult-onset condition, especially those with known heritable, traditional risk factors