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Nat Commun. 2016 Oct 31;7:13293. doi: 10.1038/ncomms13293.

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

Author information

1
Department of Biomedical Data Science, Stanford University, Palo Alto, California 94305, USA.
2
Computer Science Department, Stanford University, Palo Alto, California 94305, USA.
3
Computer Science Department, Brown University, Providence, Rhode Island 02912, USA.
4
Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
5
Broad Institute or MIT and Harvard, Cambridge, Massachusetts 02142, USA.
6
Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.
7
Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
8
Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA.

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 <0.00001 in healthy humans, consistent with very strong intolerance to gene inactivation.

PMID:
27796292
PMCID:
PMC5095512
DOI:
10.1038/ncomms13293
[Indexed for MEDLINE]
Free PMC Article

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