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dc.contributor.authorZou, James
dc.contributor.authorValiant, Gregory
dc.contributor.authorValiant, Paul
dc.contributor.authorKarczewski, Konrad
dc.contributor.authorChan, Siu On
dc.contributor.authorSamocha, Kaitlin
dc.contributor.authorLek, Monkol
dc.contributor.authorMacArthur, Daniel G.
dc.contributor.authorSunyaev, Shamil R.
dc.contributor.authorDaly, Mark J.
dc.date.accessioned2017-05-17T20:25:02Z
dc.date.available2017-05-17T20:25:02Z
dc.date.issued2016-10
dc.date.submitted2015-12
dc.identifier.issn2041-1723
dc.identifier.urihttp://hdl.handle.net/1721.1/109155
dc.description.abstractAs 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.en_US
dc.description.sponsorshipUnited States. National Institutes of Health (U54DK105566)en_US
dc.description.sponsorshipUnited States. National Institutes of Health (R01GM104371)en_US
dc.language.isoen_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionofhttp://dx.doi.org/10.1038/ncomms13293en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceNatureen_US
dc.titleQuantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projectsen_US
dc.typeArticleen_US
dc.identifier.citationZou, James; Valiant, Gregory; Valiant, Paul; Karczewski, Konrad; Chan, Siu On; Samocha, Kaitlin; Lek, Monkol; Sunyaev, Shamil; Daly, Mark and MacArthur, Daniel G. “Quantifying Unobserved Protein-Coding Variants in Human Populations Provides a Roadmap for Large-Scale Sequencing Projects.” Nature Communications 7 (October 2016): 13293. © 2017 Macmillan Publishers Limited, part of Springer Natureen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentBroad Institute of MIT and Harvarden_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.mitauthorSunyaev, Shamil R
dc.contributor.mitauthorDaly, Mark J
dc.relation.journalNature Communicationsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsZou, James; Valiant, Gregory; Valiant, Paul; Karczewski, Konrad; Chan, Siu On; Samocha, Kaitlin; Lek, Monkol; Sunyaev, Shamil; Daly, Mark; MacArthur, Daniel G.en_US
dspace.embargo.termsNen_US
mit.licensePUBLISHER_CCen_US


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