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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Autor*in:	Joseph T. Shieh [verfasserIn] Monica Penon-Portmann [verfasserIn] Karen H. Y. Wong [verfasserIn] Michal Levy-Sakin [verfasserIn] Michelle Verghese [verfasserIn] Anne Slavotinek [verfasserIn] Renata C. Gallagher [verfasserIn] Bryce A. Mendelsohn [verfasserIn] Jessica Tenney [verfasserIn] Daniah Beleford [verfasserIn] Hazel Perry [verfasserIn] Stephen K. Chow [verfasserIn] Andrew G. Sharo [verfasserIn] Steven E. Brenner [verfasserIn] Zhongxia Qi [verfasserIn] Jingwei Yu [verfasserIn] Ophir D. Klein [verfasserIn] David Martin [verfasserIn] Pui-Yan Kwok [verfasserIn] Dario Boffelli [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: npj Genomic Medicine - Nature Portfolio, 2016, 6(2021), 1, Seite 10
Übergeordnetes Werk:	volume:6 ; year:2021 ; number:1 ; pages:10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/s41525-021-00241-5

Katalog-ID:	DOAJ068056613

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author	Joseph T. Shieh
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title	Application of full-genome analysis to diagnose rare monogenic disorders
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title_full	Application of full-genome analysis to diagnose rare monogenic disorders
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title_sort	application of full-genome analysis to diagnose rare monogenic disorders
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title_auth	Application of full-genome analysis to diagnose rare monogenic disorders
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Application of full-genome analysis to diagnose rare monogenic disorders
url	https://doi.org/10.1038/s41525-021-00241-5 https://doaj.org/article/9a465491e52f48439eba018a2a0c7a7a https://doaj.org/toc/2056-7944
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author2Str	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
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up_date	2024-07-03T15:37:55.215Z
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Application of full-genome analysis to diagnose rare monogenic disorders

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