III: Medium: Causal inference in biobanks: Leveraging genetics to infer causal relationships using electronic health records

在过去几年中，我们见证了从大型卫生系统中收集患者遗传数据的重大努力，以便能够进行旨在改善患者健康的研究。这些数据可以潜在地识别导致疾病的风险因素并改善治疗。然而，这些数据集的观察性质使得这种推断具有挑战性，部分原因是难以区分相关性和因果关系，这可能会掩盖真实的关系。本项目将利用和扩展最近开发的因果推理技术，以确定医疗数据中的因果关系，并克服这一困难。推进这项研究对于改善患有当今最普遍的常见复杂疾病的个人的前景至关重要，并且还将为观察数据的分析提供一般见解。该项目利用加州大学洛杉矶分校的努力，以扩大参与计算，并将纳入来自不同背景的研究生和本科生。我们建议利用现代技术的因果推理，再加上在生物库中收集的遗传数据的独特特征，以解决生物医学和流行病学的三个关键问题：识别疾病的危险因素，预测对潜在治疗的可能反应，以及识别潜在疾病亚型。与我们的问题直接相关的因果推理的进步是因果图理论的发展，因果图是表示和推理因果效应的统一框架。我们将使用这些图表来测试和估计生物库中测量的相关暴露与疾病之间的因果关系（例如，低密度脂蛋白胆固醇和心脏病发作）。至关重要的是，我们将利用基因数据的可用性作为因果锚点，（或工具变量）这使得即使在存在混杂因素的情况下也能够估计因果效应，从而扩展了广泛用于该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The past several years have witnessed major efforts to collect genetic data from patients in large health systems to enable research aimed at improving patient health. This data can potentially identify risk factors that cause disease and improve treatment. However, the observational nature of these datasets makes such inferences challenging due, in part, to the difficulty of differentiating between correlation and causation which can obscure true relationships. This project will utilize and extend recently developed techniques in causal inference to allow for the identification of causal relationships within the medical data and overcome this difficulty. Advancing this research is critical for improving the outlook for individuals who suffer from today’s most prevalent common, complex disorders and will also provide general insights into the analysis of observational data. The project leverages efforts at UCLA to broaden participation in computing and will incorporate graduate and undergraduate students from diverse backgrounds.We propose to leverage modern techniques for causal inference coupled with the unique characteristics of genetic data collected in Biobanks to solve three key problems in biomedicine and epidemiology: the identification of risk factors for disease, predicting likely responders to a potential treatment, and identifying latent disease subtypes. The advance in causal inference that is directly relevant to our problem is the development in theory on causal graphs as a unifying framework to represent and reason about causal effects. We will use these graphs to test and estimate causal relationships between relevant exposures measured in the biobank and diseases (for example, LDL cholesterol and heart attack).Crucially, we will leverage the availability of genetic data to serve as causal anchors (or instrumental variables) that can enable the estimation of causal effects even in the presence of confounders expanding the technique of mendelian randomization that is widely used in epidemiology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.