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Description

Programmatic Theme: Translational Bioinformatics

Abstract: 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.

Learning Objective: We developed a FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data for PheWAS study.

As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients and conducted a PheWAS study, which identified 81 significant genotype-phenotype associations.

Authors:

Nansu Zong (Presenter)
Mayo Clinic

Deepak Sharma, Mayo Clinic
Yue Yu, Mayo Clinic
Jan B. Egan, Mayo Clinic
Jaime I. Davila, Mayo Clinic
Chen Wang, Mayo Clinic
Guoqian Jiang, Mayo Clinic

Keywords, Themes & Types