Dataset Information

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.

ABSTRACT:

Motivation

Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL).

Results

We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51?key 'hub' diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes.

Contacts

rong.chen@mssm.edu or joel.dudley@mssm.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Glicksberg BS

PROVIDER: S-EPMC4908366 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.

Glicksberg Benjamin S BS Li Li L Badgeley Marcus A MA Shameer Khader K Kosoy Roman R Beckmann Noam D ND Pho Nam N Hakenberg Jörg J Ma Meng M Ayers Kristin L KL Hoffman Gabriel E GE Dan Li Shuyu S Schadt Eric E EE Patel Chirag J CJ Chen Rong R Dudley Joel T JT

Bioinformatics (Oxford, England) 20160601 12

<h4>Motivation</h4>Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound rega ...[more]

PMID: 27307606

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Dataset Information

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.

Motivation

Results

Contacts

Supplementary information

Publications

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.

Similar Datasets

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