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

数据科学工具可识别精准医学中强大的暴露-表型关联

• 批准号: 10705899
• 负责人: ARJUN KUMAR MANRAI
• 金额: $ 9.25万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-16 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10705899
• 关键词: Address; All of Us Research Program; Big Data; Biological; Biological Markers; Catalogs; Cohort Studies; Communities; Complex; Country; Data; Data Science; Data Set; Deposition; Disease; Disparity; Environment; Environmental Exposure; Environmental Risk Factor; Goals; Heart Diseases; Incidence; Lead; Libraries; Literature; Machine Learning; Measures; Methods; Modeling; National Institute of Environmental Health Sciences; Observational Study; Phenotype; Population; Population Heterogeneity; Reproducibility; Research Design; Research Personnel; Risk Factors; Role; Sample Size; Testing; Time; Translations; Variant; analytical method; cohort; data resource; disorder risk; environmental health disparity; feature selection; health disparity; hypercholesterolemia; machine learning method; novel; phenome; precision medicine; scale up; tool; vibration

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.

项目概要/摘要 不同人口群体的表型变异是由环境因素驱动的。这 该提案的总体目标是部署数据科学方法来推动发现之间的关联 人口统计不同人群中的暴露（E）和表型（P）。我们缺乏数据科学方法 在表型 (P) 和疾病中关联、复制暴露组 (E) 的暴露变量并确定其优先级 发病率 (D)，是提供精准医疗所需的。观察性研究充满了 4 个未解决的问题 数据科学挑战。首先，基于电子的研究是：（1）仅限于关联一些假设的暴露- 一次显示型对（E-P），导致环境协会的文献支离破碎。机器 然而，用于特征选择和预测的学习（ML）方法大有希望，（2）最现存的基于电子的 队列包含缺失数据，对使用 ML 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会影响联想并阻碍翻译。四、(4) 很少 强大的数据资源，系统地记录纵向 E-P 和 E-D 关联 大规模精准医疗。系统地关联多个风险敞口是一项挑战 表型并在队列中复制这些关联。 （目标 1）。 “效果的振动”，或程度 关联性随着研究设计（例如分析方法、样本量）和模型的变化而变化 选择是观察性研究中隐藏的偏见（目标 2）。第三，一个悬而未决的问题是，在多大程度上 环境差异导致健康差异。为了解决这些挑战和差距，我们建议目标 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。我们假设暴露将解释大量的变化 群体中的表型，并将所有数据和模型存放在一个新颖的 EP-WAS 目录中。目标 2：定量 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）的方法来衡量研究标准如何影响 关联的稳定性（关联的可重复性如何作为分析选择的函数）。目标 3. 杠杆作用 EP-WAS 和 VoE 解开表型的生物、人口和环境影响 高胆固醇血症的差异。我们将在最大的队列中部署 EP-WAS 和 VoE 打包库 研究将不同人口群体的表型变异划分为高胆固醇血症的因素。我们将 为生物医学界提供数据科学方法，以实现强大的数据驱动发现和 解释观察数据集中的暴露表型因素，需要识别 环境健康差异。调查人员将首次确定该组织的集体作用 大规模的心脏病环境正好适合“我们所有人”计划。