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...
| Main Authors: | , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Nature Publishing Group
2017
|
| Online Access: | http://hdl.handle.net/1721.1/109155 |
| _version_ | 1826198045511909376 |
|---|---|
| author | Zou, James Valiant, Gregory Valiant, Paul Karczewski, Konrad Chan, Siu On Samocha, Kaitlin Lek, Monkol MacArthur, Daniel G. Sunyaev, Shamil R. Daly, Mark J. |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Zou, James Valiant, Gregory Valiant, Paul Karczewski, Konrad Chan, Siu On Samocha, Kaitlin Lek, Monkol MacArthur, Daniel G. Sunyaev, Shamil R. Daly, Mark J. |
| author_sort | Zou, James |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T10:58:00Z |
| format | Article |
| id | mit-1721.1/109155 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:58:00Z |
| publishDate | 2017 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1091552022-10-01T00:17:56Z 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 MacArthur, Daniel G. Sunyaev, Shamil R. Daly, Mark J. Massachusetts Institute of Technology. Institute for Medical Engineering & Science Broad Institute of MIT and Harvard Harvard University--MIT Division of Health Sciences and Technology Sunyaev, Shamil R Daly, Mark J 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. United States. National Institutes of Health (U54DK105566) United States. National Institutes of Health (R01GM104371) 2017-05-17T20:25:02Z 2017-05-17T20:25:02Z 2016-10 2015-12 Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/109155 Zou, 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 Nature en_US http://dx.doi.org/10.1038/ncomms13293 Nature Communications Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Nature Publishing Group Nature |
| spellingShingle | Zou, James Valiant, Gregory Valiant, Paul Karczewski, Konrad Chan, Siu On Samocha, Kaitlin Lek, Monkol MacArthur, Daniel G. Sunyaev, Shamil R. Daly, Mark J. 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 |
| url | http://hdl.handle.net/1721.1/109155 |
| work_keys_str_mv | AT zoujames quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT valiantgregory quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT valiantpaul quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT karczewskikonrad quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT chansiuon quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT samochakaitlin quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT lekmonkol quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT macarthurdanielg quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT sunyaevshamilr quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects AT dalymarkj quantifyingunobservedproteincodingvariantsinhumanpopulationsprovidesaroadmapforlargescalesequencingprojects |