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

项目摘要 早期疾病预防、检测和干预是促进人类健康的基本目标。与此同时，基因 风险，对于所有的意图和目的，最早的重要贡献者共同的，遗传的，疾病的风险。因此，理论上， 基因图谱应是早期疾病预防的理想工具。然而，遗传因素很少直接用于预测 未来的疾病风险相反，遗传信息通常被归入表型优先的场景：提供或确认 诊断为个人与明显的疾病或澄清遗传风险的个人与强大的家族史， 疾病为了使现代基因组学对疾病预防产生重大影响， 向基因型优先的方法过渡;预测健康个体的遗传疾病风险。一个主要障碍 这一转变包括我们有限的能力来预测精确的风险阵列和可能的疾病表型表达 遗传和其他风险因素。疾病风险的程度和表型表达传达给任何 遗传因素对单个个体的影响是直接和间接遗传效应之间复杂相互作用的结果， 不可改变的风险因素（年龄、性别、血统、家族史）和中间可改变的风险因素（环境， 行为、实验室值、健康状况、治疗状况等）其中许多都有自己的直接遗传介质。新 需要采取一些方法来剖析这种相互作用，以便个性化和结合大部分 有效降低整体疾病风险。该提案的总体目标是开发创新的深度学习 和机器学习方法，将基线遗传风险预测与传统风险测量相结合 这些因素，以提供更准确和可操作的疾病风险预测。通过将遗传风险与传统风险联系起来 因素，特别是可改变的风险因素，我们将通过允许确定预防性 可能特别有效的行动，因为它们抵消了遗传风险，并确定了可改变的风险 应积极监测和控制的因素，增加遗传易感性。为了实现这一目标， 我们建议开发的方法：（1）推断可能的单基因风险变异的表型表达，通过空间 协方差机器学习方法，（2）预测流行病病例和中间可修改的期望值 从多基因和其他不可改变的危险因素的危险因素，最后（3）预测流行疾病的情况下，通过 基线遗传期望和观察到的（测量的）中间可改变的风险因素之间的相互作用， 学习框架。在这些训练模型中调整年龄和可改变的风险因素， 预测未来的疾病风险，并确定可改变的风险因素，这些因素在被操纵时， 未来疾病风险的变化。我们专注于发展冠状动脉疾病的方法， 重要性，多基因风险估计的已知效用，以及环境多基因的现有证据 交互.此外，我们提出的方法直接与目前的冠状动脉临床决策支持工具相结合， 动脉疾病管理然而，我们将建立一个通用的框架，可以扩展到任何常见的遗传 成人发病的疾病，特别是那些已知的遗传，传统的风险因素

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