Zong Nansu, Sharma Deepak K, Yu Yue, Egan Jan B, Davila Jaime I, Wang Chen, Jiang Guoqian
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Center for Individualized Medicine, Mayo Clinic, Rochester, MN.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:750-759. eCollection 2020.
Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. We developed an FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data that are used in the PheWAS study. As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients with genetic reports from Foundation Medicine as well as the corresponding lab tests and diagnosis from Mayo EHRs. A PheWAS study is conducted and 81 significant genotype-phenotype associations are identified, in which 36 significant associations for cancers are validated based on a literature review.
全表型组关联研究（PheWAS）能够通过将可用的基因组报告与大规模电子健康记录（EHR）相链接，进行全表型组扫描，以发现基因型与临床表型之间的新关联。来自不同EHR系统和基因报告的数据异质性一直是阻碍有意义验证的关键挑战。为解决这一问题，我们提出了一个基于FHIR的框架，以标准方式对PheWAS研究进行建模。我们开发了一个基于FHIR的数据模型概要文件，以实现对PheWAS研究中使用的基因报告和EHR数据中的数据元素进行标准表示。作为概念验证，我们使用了一组1595名泛癌患者实施了所提出的方法，这些患者有来自Foundation Medicine的基因报告以及梅奥EHRs的相应实验室检查和诊断结果。进行了一项PheWAS研究，确定了81个显著的基因型-表型关联，其中基于文献综述验证了36个与癌症相关的显著关联。
