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Molecular Medicine

, Volume 23, Issue 1, pp 285–294 | Cite as

Efficient Genome-wide Association in Biobanks Using Topic Modeling Identifies Multiple Novel Disease Loci

  • Thomas H. McCoyJr
  • Victor M. Castro
  • Leslie A. Snapper
  • Kamber L. Hart
  • Roy H. Perlis
Research Article

Abstract

Biobanks and national registries represent a powerful tool for genomic discovery, but rely on diagnostic codes that can be unreliable and fail to capture relationships between related diagnoses. We developed an efficient means of conducting genome-wide association studies using combinations of diagnostic codes from electronic health records for 10,845 participants in a biobanking program at two large academic medical centers. Specifically, we applied latent Dirichilet allocation to fit 50 disease topics based on diagnostic codes, then conducted a genome-wide common-variant association for each topic. In sensitivity analysis, these results were contrasted with those obtained from traditional single-diagnosis phenome-wide association analysis, as well as those in which only a subset of diagnostic codes were included per topic. In meta-analysis across three biobank cohorts, we identified 23 disease-associated loci with p < 1e-15, including previously associated autoimmune disease loci. In all cases, observed significant associations were of greater magnitude than single phenome-wide diagnostic codes, and incorporation of less strongly loading diagnostic codes enhanced association. This strategy provides a more efficient means of identifying phenome-wide associations in biobanks with coded clinical data.

Notes

Acknowledgments

This study was funded by the National Human Genome Research Institute and the National Institute of Mental Health. The authors acknowledge the Partners HealthCare Biobank for providing genomic data and health information data.

Supplementary material

10020_2017_2301285_MOESM1_ESM.pdf (2.9 mb)
Supplementary material, approximately 2.89 MB.

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Authors and Affiliations

  • Thomas H. McCoyJr
    • 1
  • Victor M. Castro
    • 1
    • 2
  • Leslie A. Snapper
    • 1
  • Kamber L. Hart
    • 1
  • Roy H. Perlis
    • 1
  1. 1.Center for Quantitative Health, Division of Clinical Research and Center for Human Genetic ResearchMassachusetts General HospitalBostonUSA
  2. 2.Partners Research Information Systems and ComputingPartners HealthCare SystemBostonUSA

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