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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Main Authors:	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
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-09-01
Series:	npj Genomic Medicine
Online Access:	https://doi.org/10.1038/s41525-021-00241-5

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spelling	doaj-9a465491e52f48439eba018a2a0c7a7a2021-09-26T11:56:08ZengNature Publishing Groupnpj Genomic Medicine2056-79442021-09-016111010.1038/s41525-021-00241-5Application of full-genome analysis to diagnose rare monogenic disordersJoseph T. Shieh0Monica Penon-Portmann1Karen H. Y. Wong2Michal Levy-Sakin3Michelle Verghese4Anne Slavotinek5Renata C. Gallagher6Bryce A. Mendelsohn7Jessica Tenney8Daniah Beleford9Hazel Perry10Stephen K. Chow11Andrew G. Sharo12Steven E. Brenner13Zhongxia Qi14Jingwei Yu15Ophir D. Klein16David Martin17Pui-Yan Kwok18Dario Boffelli19Institute for Human Genetics, University of California San FranciscoInstitute for Human Genetics, University of California San FranciscoCardiovascular Research Institute, University of California San FranciscoCardiovascular Research Institute, University of California San FranciscoCardiovascular Research Institute, University of California San FranciscoInstitute for Human Genetics, University of California San FranciscoInstitute for Human Genetics, University of California San FranciscoDivision of Medical Genetics, Pediatrics, Benioff Children’s Hospital, University of California San FranciscoDivision of Medical Genetics, Pediatrics, Benioff Children’s Hospital, University of California San FranciscoDivision of Medical Genetics, Pediatrics, Benioff Children’s Hospital, University of California San FranciscoDivision of Medical Genetics, Pediatrics, Benioff Children’s Hospital, University of California San FranciscoCardiovascular Research Institute, University of California San FranciscoBiophysics Graduate Group, University of California BerkeleyDepartment of Plant and Microbial Biology, University of California BerkeleyDepartment of Laboratory Medicine, University of California San FranciscoDepartment of Laboratory Medicine, University of California San FranciscoInstitute for Human Genetics, University of California San FranciscoChildren’s Hospital Oakland Research Institute, Benioff Children’s Hospital Oakland, University of California San FranciscoInstitute for Human Genetics, University of California San FranciscoChildren’s Hospital Oakland Research Institute, Benioff Children’s Hospital Oakland, University of California San FranciscoAbstract 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.https://doi.org/10.1038/s41525-021-00241-5
collection	DOAJ
language	English
format	Article
sources	DOAJ
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
spellingShingle	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 npj Genomic Medicine
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 Publishing Group
series	npj Genomic Medicine
issn	2056-7944
publishDate	2021-09-01
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.
url	https://doi.org/10.1038/s41525-021-00241-5
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Application of full-genome analysis to diagnose rare monogenic disorders

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