Toward a fine-scale population health monitoring system

CBIPM Genomics Team, Regeneron Genetics Center

Research output: Contribution to journalArticlepeer-review

Abstract

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

Original languageEnglish (US)
Pages (from-to)2068-2083.e11
JournalCell
Volume184
Issue number8
DOIs
StatePublished - Apr 15 2021

Keywords

  • biobanks
  • computational genomics
  • electronic health records
  • genetic ancestry
  • genomic medicine
  • health disparities
  • machine learning
  • population health

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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