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Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

Author

Listed:
  • James Zou

    (Stanford University)

  • Gregory Valiant

    (Stanford University)

  • Paul Valiant

    (Brown University)

  • Konrad Karczewski

    (Analytic and Translational Genetics Unit, Massachusetts General Hospital
    Broad Institute or MIT and Harvard)

  • Siu On Chan

    (Computer Science and Engineering, Chinese University of Hong Kong)

  • Kaitlin Samocha

    (Analytic and Translational Genetics Unit, Massachusetts General Hospital
    Broad Institute or MIT and Harvard)

  • Monkol Lek

    (Analytic and Translational Genetics Unit, Massachusetts General Hospital
    Broad Institute or MIT and Harvard)

  • Shamil Sunyaev

    (Broad Institute or MIT and Harvard
    Brigham and Women’s Hospital, Harvard Medical School)

  • Mark Daly

    (Analytic and Translational Genetics Unit, Massachusetts General Hospital
    Broad Institute or MIT and Harvard
    Harvard Medical School)

  • Daniel G. MacArthur

    (Analytic and Translational Genetics Unit, Massachusetts General Hospital
    Broad Institute or MIT and Harvard
    Harvard Medical School)

Abstract

As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of

Suggested Citation

  • James Zou & Gregory Valiant & Paul Valiant & Konrad Karczewski & Siu On Chan & Kaitlin Samocha & Monkol Lek & Shamil Sunyaev & Mark Daly & Daniel G. MacArthur, 2016. "Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects," Nature Communications, Nature, vol. 7(1), pages 1-5, December.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13293
    DOI: 10.1038/ncomms13293
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