Data science tools to identify robust exposure-phenotype associations for precision medicine

项目摘要/摘要 人口统计上不同群体的表型变异是由环境因素驱动的。这个 这项提议的总体目标是部署数据科学方法来推动发现 不同人口统计人群的暴露(E)和表型(P)。我们缺乏数据科学方法来 将暴露组(E)的暴露变量与表型(P)和疾病进行关联、复制和优先排序 发病率(D)，提供精确药物所需。观测研究充满了4个悬而未决的问题 数据科学的挑战。首先，基于电子的研究是：(1)仅限于联系几个假设的暴露- 一次表型对(E-P)，导致环境关联的文献支离破碎。机器 然而，用于特征选择和预测的学习(ML)方法是有希望的，(2)最现存的基于E 队列包含缺失数据，这对使用ML来检测复杂的E-P关联提出了挑战，第三，(3)偏差， 例如混淆和研究设计，影响联想和阻碍翻译。四、(四)数量少 强大的数据资源，系统地记录纵向的E-P和E-D关联 大规模精准医疗。要系统地将多个曝光关联起来是一项挑战 并在队列中复制这些关联。(目标1)。“效果的振动”，或程度 关联性随研究设计(例如，分析方法、样本量)和模型的变化而变化 在观察性研究中，选择是一种隐藏的偏见(目标2)。第三，一个悬而未决的问题是 环境差异导致健康差异。为了应对这些挑战和差距，我们建议 1：开发和测试机器学习方法，将多个环境暴露指标与 多种表型：EP-WAS型。我们假设，暴露于这些环境中将解释 并将把所有数据和模型保存在一个新的EP-WAS目录中。目标2：量化 研究设计如何影响暴露生物标志物和表型之间的关联。我们将扩大规模， 扩展并测试一种称为“效果振动”(VOE)的方法，以衡量研究标准如何影响 关联的稳定性(关联作为分析选择的函数的重现性如何)。目标3.杠杆作用 EP-WASS和VOE解开表型的生物学、人口学和环境影响 高胆固醇血症的差异。我们将在最大的队列中部署EP-WASS和VOE包库 在高胆固醇血症因素中划分不同人口学群体的表型变异的研究。我们会 为生物医学界配备数据科学方法，以实现强大的数据驱动发现和 在观测数据集中对暴露-表型因素的解释，识别 环境健康差距。调查人员将首次确定 在我们所有人的计划中，心脏病环境的规模正好是及时的。

Project Summary/Abstract Phenotypic variability across demographically diverse populations are driven by environmental factors. The overall goal of this proposal is to deploy data science approaches to drive discovery of associations between exposures (E) and phenotypes (P) in demographically diverse populations. We lack data science methods to associate, replicate, and prioritize exposure variables of the exposome (E) in phenotypes (P) and disease incidence (D), required for the delivery of precision medicine. Observational studies are fraught with 4 unsolved data science challenges. First, E-based studies are: (1) limited to associating a few hypothesized exposure- phenotype pairs (E-P) at a time, leading to a fragmented literature of environmental associations. Machine learning (ML) approaches for feature selection and prediction hold promise, however, (2) most extant E-based cohorts contain missing data, challenging the use of ML to detect complex E-P associations, Third, (3) biases, such as confounding and study design influence associations and hinder translation. Fourth, (4) there are few well-powered data resources that systematically document longitudinal E-P and E-D associations across massive precision medicine. It is a challenge to systematically associate a number of exposures in multiple phenotypes and replicate these associations across cohorts. (Aim 1). The “vibration of effects”, or the degree to which associations change as a function of study design (e.g., analytic method, sample size) and model choice is a hidden bias in observational studies (Aim 2). Third, an outstanding question is the degree to which environmental differences lead to health disparities. To address these challenges and gaps, we propose to Aim 1: develop and test machine learning methods to associate multiple environmental exposure indicators with multiple phenotypes: EP-WAS. We hypothesize that exposures will explain a significant amount of variation in phenotype in populations and will deposit all data and models in a novel EP-WAS Catalog. Aim 2: Quantitate how study design influences associations between exposure biomarkers and phenotype. We will scale up, extend, and test a method called “vibration of effects” (VoE) to measure how study criteria influences the stability of associations (how reproducible associations are as a function of analytic choice). Aim 3. Leverage EP-WAS and VoE to disentangle biological, demographic, and environmental influences of phenotypic disparities in hypercholesterolemia. We will deploy EP-WAS and VoE packaged libraries in the largest cohort study to partition phenotypic variation across demographic groups in factors for hypercholesterolemia. We will equip the biomedical community with data science approaches for robust data-driven discovery and interpretation of exposure-phenotype factors in observational datasets, required for the identification of environmental health disparities. For the first time, investigators will ascertain the collective role of the environment in heart disease at scale just in time for the All of Us program.