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

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 o...

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Autores principales: Zou, James, Valiant, Gregory, Valiant, Paul, Karczewski, Konrad, Chan, Siu On, Samocha, Kaitlin, Lek, Monkol, Sunyaev, Shamil, Daly, Mark, MacArthur, Daniel G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095512/
https://www.ncbi.nlm.nih.gov/pubmed/27796292
http://dx.doi.org/10.1038/ncomms13293
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author Zou, James
Valiant, Gregory
Valiant, Paul
Karczewski, Konrad
Chan, Siu On
Samocha, Kaitlin
Lek, Monkol
Sunyaev, Shamil
Daly, Mark
MacArthur, Daniel G.
author_facet Zou, James
Valiant, Gregory
Valiant, Paul
Karczewski, Konrad
Chan, Siu On
Samocha, Kaitlin
Lek, Monkol
Sunyaev, Shamil
Daly, Mark
MacArthur, Daniel G.
author_sort Zou, James
collection PubMed
description 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.
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spelling pubmed-50955122016-11-18 Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects Zou, James Valiant, Gregory Valiant, Paul Karczewski, Konrad Chan, Siu On Samocha, Kaitlin Lek, Monkol Sunyaev, Shamil Daly, Mark MacArthur, Daniel G. Nat Commun Article 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. Nature Publishing Group 2016-10-31 /pmc/articles/PMC5095512/ /pubmed/27796292 http://dx.doi.org/10.1038/ncomms13293 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Zou, James
Valiant, Gregory
Valiant, Paul
Karczewski, Konrad
Chan, Siu On
Samocha, Kaitlin
Lek, Monkol
Sunyaev, Shamil
Daly, Mark
MacArthur, Daniel G.
Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_full Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_fullStr Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_full_unstemmed Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_short Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_sort quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095512/
https://www.ncbi.nlm.nih.gov/pubmed/27796292
http://dx.doi.org/10.1038/ncomms13293
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