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Genetic association analysis of 77,539 genomes reveals rare disease etiologies

The genetic etiologies of more than half of rare diseases remain unknown. Standardized genome sequencing and phenotyping of large patient cohorts provide an opportunity for discovering the unknown etiologies, but this depends on efficient and powerful analytical methods. We built a compact database,...

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Autores principales: Greene, Daniel, Pirri, Daniela, Frudd, Karen, Sackey, Ege, Al-Owain, Mohammed, Giese, Arnaud P. J., Ramzan, Khushnooda, Riaz, Sehar, Yamanaka, Itaru, Boeckx, Nele, Thys, Chantal, Gelb, Bruce D., Brennan, Paul, Hartill, Verity, Harvengt, Julie, Kosho, Tomoki, Mansour, Sahar, Masuno, Mitsuo, Ohata, Takako, Stewart, Helen, Taibah, Khalid, Turner, Claire L. S., Imtiaz, Faiqa, Riazuddin, Saima, Morisaki, Takayuki, Ostergaard, Pia, Loeys, Bart L., Morisaki, Hiroko, Ahmed, Zubair M., Birdsey, Graeme M., Freson, Kathleen, Mumford, Andrew, Turro, Ernest
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033407/
https://www.ncbi.nlm.nih.gov/pubmed/36928819
http://dx.doi.org/10.1038/s41591-023-02211-z
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author Greene, Daniel
Pirri, Daniela
Frudd, Karen
Sackey, Ege
Al-Owain, Mohammed
Giese, Arnaud P. J.
Ramzan, Khushnooda
Riaz, Sehar
Yamanaka, Itaru
Boeckx, Nele
Thys, Chantal
Gelb, Bruce D.
Brennan, Paul
Hartill, Verity
Harvengt, Julie
Kosho, Tomoki
Mansour, Sahar
Masuno, Mitsuo
Ohata, Takako
Stewart, Helen
Taibah, Khalid
Turner, Claire L. S.
Imtiaz, Faiqa
Riazuddin, Saima
Morisaki, Takayuki
Ostergaard, Pia
Loeys, Bart L.
Morisaki, Hiroko
Ahmed, Zubair M.
Birdsey, Graeme M.
Freson, Kathleen
Mumford, Andrew
Turro, Ernest
author_facet Greene, Daniel
Pirri, Daniela
Frudd, Karen
Sackey, Ege
Al-Owain, Mohammed
Giese, Arnaud P. J.
Ramzan, Khushnooda
Riaz, Sehar
Yamanaka, Itaru
Boeckx, Nele
Thys, Chantal
Gelb, Bruce D.
Brennan, Paul
Hartill, Verity
Harvengt, Julie
Kosho, Tomoki
Mansour, Sahar
Masuno, Mitsuo
Ohata, Takako
Stewart, Helen
Taibah, Khalid
Turner, Claire L. S.
Imtiaz, Faiqa
Riazuddin, Saima
Morisaki, Takayuki
Ostergaard, Pia
Loeys, Bart L.
Morisaki, Hiroko
Ahmed, Zubair M.
Birdsey, Graeme M.
Freson, Kathleen
Mumford, Andrew
Turro, Ernest
author_sort Greene, Daniel
collection PubMed
description The genetic etiologies of more than half of rare diseases remain unknown. Standardized genome sequencing and phenotyping of large patient cohorts provide an opportunity for discovering the unknown etiologies, but this depends on efficient and powerful analytical methods. We built a compact database, the ‘Rareservoir’, containing the rare variant genotypes and phenotypes of 77,539 participants sequenced by the 100,000 Genomes Project. We then used the Bayesian genetic association method BeviMed to infer associations between genes and each of 269 rare disease classes assigned by clinicians to the participants. We identified 241 known and 19 previously unidentified associations. We validated associations with ERG, PMEPA1 and GPR156 by searching for pedigrees in other cohorts and using bioinformatic and experimental approaches. We provide evidence that (1) loss-of-function variants in the Erythroblast Transformation Specific (ETS)-family transcription factor encoding gene ERG lead to primary lymphoedema, (2) truncating variants in the last exon of transforming growth factor-β regulator PMEPA1 result in Loeys–Dietz syndrome and (3) loss-of-function variants in GPR156 give rise to recessive congenital hearing impairment. The Rareservoir provides a lightweight, flexible and portable system for synthesizing the genetic and phenotypic data required to study rare disease cohorts with tens of thousands of participants.
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spelling pubmed-100334072023-03-24 Genetic association analysis of 77,539 genomes reveals rare disease etiologies Greene, Daniel Pirri, Daniela Frudd, Karen Sackey, Ege Al-Owain, Mohammed Giese, Arnaud P. J. Ramzan, Khushnooda Riaz, Sehar Yamanaka, Itaru Boeckx, Nele Thys, Chantal Gelb, Bruce D. Brennan, Paul Hartill, Verity Harvengt, Julie Kosho, Tomoki Mansour, Sahar Masuno, Mitsuo Ohata, Takako Stewart, Helen Taibah, Khalid Turner, Claire L. S. Imtiaz, Faiqa Riazuddin, Saima Morisaki, Takayuki Ostergaard, Pia Loeys, Bart L. Morisaki, Hiroko Ahmed, Zubair M. Birdsey, Graeme M. Freson, Kathleen Mumford, Andrew Turro, Ernest Nat Med Article The genetic etiologies of more than half of rare diseases remain unknown. Standardized genome sequencing and phenotyping of large patient cohorts provide an opportunity for discovering the unknown etiologies, but this depends on efficient and powerful analytical methods. We built a compact database, the ‘Rareservoir’, containing the rare variant genotypes and phenotypes of 77,539 participants sequenced by the 100,000 Genomes Project. We then used the Bayesian genetic association method BeviMed to infer associations between genes and each of 269 rare disease classes assigned by clinicians to the participants. We identified 241 known and 19 previously unidentified associations. We validated associations with ERG, PMEPA1 and GPR156 by searching for pedigrees in other cohorts and using bioinformatic and experimental approaches. We provide evidence that (1) loss-of-function variants in the Erythroblast Transformation Specific (ETS)-family transcription factor encoding gene ERG lead to primary lymphoedema, (2) truncating variants in the last exon of transforming growth factor-β regulator PMEPA1 result in Loeys–Dietz syndrome and (3) loss-of-function variants in GPR156 give rise to recessive congenital hearing impairment. The Rareservoir provides a lightweight, flexible and portable system for synthesizing the genetic and phenotypic data required to study rare disease cohorts with tens of thousands of participants. Nature Publishing Group US 2023-03-16 2023 /pmc/articles/PMC10033407/ /pubmed/36928819 http://dx.doi.org/10.1038/s41591-023-02211-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Greene, Daniel
Pirri, Daniela
Frudd, Karen
Sackey, Ege
Al-Owain, Mohammed
Giese, Arnaud P. J.
Ramzan, Khushnooda
Riaz, Sehar
Yamanaka, Itaru
Boeckx, Nele
Thys, Chantal
Gelb, Bruce D.
Brennan, Paul
Hartill, Verity
Harvengt, Julie
Kosho, Tomoki
Mansour, Sahar
Masuno, Mitsuo
Ohata, Takako
Stewart, Helen
Taibah, Khalid
Turner, Claire L. S.
Imtiaz, Faiqa
Riazuddin, Saima
Morisaki, Takayuki
Ostergaard, Pia
Loeys, Bart L.
Morisaki, Hiroko
Ahmed, Zubair M.
Birdsey, Graeme M.
Freson, Kathleen
Mumford, Andrew
Turro, Ernest
Genetic association analysis of 77,539 genomes reveals rare disease etiologies
title Genetic association analysis of 77,539 genomes reveals rare disease etiologies
title_full Genetic association analysis of 77,539 genomes reveals rare disease etiologies
title_fullStr Genetic association analysis of 77,539 genomes reveals rare disease etiologies
title_full_unstemmed Genetic association analysis of 77,539 genomes reveals rare disease etiologies
title_short Genetic association analysis of 77,539 genomes reveals rare disease etiologies
title_sort genetic association analysis of 77,539 genomes reveals rare disease etiologies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033407/
https://www.ncbi.nlm.nih.gov/pubmed/36928819
http://dx.doi.org/10.1038/s41591-023-02211-z
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