Application of full-genome analysis to diagnose rare monogenic disorders

Abstract Current genetic testenhancer and narrows the diagnostic intervals for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with bre...

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Autores principales:	Joseph T. Shieh, Monica Penon-Portmann, Karen H. Y. Wong, Michal Levy-Sakin, Michelle Verghese, Anne Slavotinek, Renata C. Gallagher, Bryce A. Mendelsohn, Jessica Tenney, Daniah Beleford, Hazel Perry, Stephen K. Chow, Andrew G. Sharo, Steven E. Brenner, Zhongxia Qi, Jingwei Yu, Ophir D. Klein, David Martin, Pui-Yan Kwok, Dario Boffelli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Genetics QH426-470
Acceso en línea:	https://doaj.org/article/9a465491e52f48439eba018a2a0c7a7a
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spelling	oai:doaj.org-article:9a465491e52f48439eba018a2a0c7a7a2021-12-02T15:15:24ZApplication of full-genome analysis to diagnose rare monogenic disorders10.1038/s41525-021-00241-52056-7944https://doaj.org/article/9a465491e52f48439eba018a2a0c7a7a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41525-021-00241-5https://doaj.org/toc/2056-7944Abstract Current genetic testenhancer and narrows the diagnostic intervals for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA’s utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.Joseph T. ShiehMonica Penon-PortmannKaren H. Y. WongMichal Levy-SakinMichelle VergheseAnne SlavotinekRenata C. GallagherBryce A. MendelsohnJessica TenneyDaniah BelefordHazel PerryStephen K. ChowAndrew G. SharoSteven E. BrennerZhongxia QiJingwei YuOphir D. KleinDavid MartinPui-Yan KwokDario BoffelliNature PortfolioarticleMedicineRGeneticsQH426-470ENnpj Genomic Medicine, Vol 6, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Genetics QH426-470
spellingShingle	Medicine R Genetics QH426-470 Joseph T. Shieh Monica Penon-Portmann Karen H. Y. Wong Michal Levy-Sakin Michelle Verghese Anne Slavotinek Renata C. Gallagher Bryce A. Mendelsohn Jessica Tenney Daniah Beleford Hazel Perry Stephen K. Chow Andrew G. Sharo Steven E. Brenner Zhongxia Qi Jingwei Yu Ophir D. Klein David Martin Pui-Yan Kwok Dario Boffelli Application of full-genome analysis to diagnose rare monogenic disorders
description	Abstract Current genetic testenhancer and narrows the diagnostic intervals for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA’s utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.
format	article
author	Joseph T. Shieh Monica Penon-Portmann Karen H. Y. Wong Michal Levy-Sakin Michelle Verghese Anne Slavotinek Renata C. Gallagher Bryce A. Mendelsohn Jessica Tenney Daniah Beleford Hazel Perry Stephen K. Chow Andrew G. Sharo Steven E. Brenner Zhongxia Qi Jingwei Yu Ophir D. Klein David Martin Pui-Yan Kwok Dario Boffelli
author_facet	Joseph T. Shieh Monica Penon-Portmann Karen H. Y. Wong Michal Levy-Sakin Michelle Verghese Anne Slavotinek Renata C. Gallagher Bryce A. Mendelsohn Jessica Tenney Daniah Beleford Hazel Perry Stephen K. Chow Andrew G. Sharo Steven E. Brenner Zhongxia Qi Jingwei Yu Ophir D. Klein David Martin Pui-Yan Kwok Dario Boffelli
author_sort	Joseph T. Shieh
title	Application of full-genome analysis to diagnose rare monogenic disorders
title_short	Application of full-genome analysis to diagnose rare monogenic disorders
title_full	Application of full-genome analysis to diagnose rare monogenic disorders
title_fullStr	Application of full-genome analysis to diagnose rare monogenic disorders
title_full_unstemmed	Application of full-genome analysis to diagnose rare monogenic disorders
title_sort	application of full-genome analysis to diagnose rare monogenic disorders
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9a465491e52f48439eba018a2a0c7a7a
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Application of full-genome analysis to diagnose rare monogenic disorders

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