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Efficient Replication of over 180 Genetic Associations with Self-Reported Medical Data

Author

Listed:
  • Joyce Y Tung
  • Chuong B Do
  • David A Hinds
  • Amy K Kiefer
  • J Michael Macpherson
  • Arnab B Chowdry
  • Uta Francke
  • Brian T Naughton
  • Joanna L Mountain
  • Anne Wojcicki
  • Nicholas Eriksson

Abstract

While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for obtaining large amounts of medical information from a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web-based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in-depth questions to refine self-reported diagnoses. Our data suggest that online collection of self-reported data from a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations.

Suggested Citation

  • Joyce Y Tung & Chuong B Do & David A Hinds & Amy K Kiefer & J Michael Macpherson & Arnab B Chowdry & Uta Francke & Brian T Naughton & Joanna L Mountain & Anne Wojcicki & Nicholas Eriksson, 2011. "Efficient Replication of over 180 Genetic Associations with Self-Reported Medical Data," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-9, August.
    • Handle: RePEc:plo:pone00:0023473
    DOI: 10.1371/journal.pone.0023473
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    References listed on IDEAS

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    1. John PA Ioannidis & Nikolaos A Patsopoulos & Evangelos Evangelou, 2007. "Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations," PLOS ONE, Public Library of Science, vol. 2(9), pages 1-7, September.
    2. Nicholas Eriksson & J Michael Macpherson & Joyce Y Tung & Lawrence S Hon & Brian Naughton & Serge Saxonov & Linda Avey & Anne Wojcicki & Itsik Pe'er & Joanna Mountain, 2010. "Web-Based, Participant-Driven Studies Yield Novel Genetic Associations for Common Traits," PLOS Genetics, Public Library of Science, vol. 6(6), pages 1-20, June.
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    1. Amy K Kiefer & Joyce Y Tung & Chuong B Do & David A Hinds & Joanna L Mountain & Uta Francke & Nicholas Eriksson, 2013. "Genome-Wide Analysis Points to Roles for Extracellular Matrix Remodeling, the Visual Cycle, and Neuronal Development in Myopia," PLOS Genetics, Public Library of Science, vol. 9(2), pages 1-8, February.

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