Targeted long-read sequencing identifies missing pathogenic variants in unsolved Werner syndrome cases.

genetic variation genomics nanopore sequencing

Journal

Journal of medical genetics
ISSN: 1468-6244
Titre abrégé: J Med Genet
Pays: England
ID NLM: 2985087R

Informations de publication

Date de publication:
09 May 2022
Historique:
received: 05 02 2022
accepted: 14 04 2022
entrez: 9 5 2022
pubmed: 10 5 2022
medline: 10 5 2022
Statut: aheadofprint

Résumé

Werner syndrome (WS) is an autosomal recessive progeroid syndrome caused by variants in Targeted long-read sequencing (T-LRS) on an Oxford Nanopore platform was used to search for a second pathogenic variant in We identified a second pathogenic variant in eight of nine unsolved WS cases. In five cases, T-LRS identified intronic splice variants that were confirmed by either RT-PCR or exon trapping to affect splicing; in one case, T-LRS identified a 339 kbp deletion, and in two cases, pathogenic missense variants. Phasing of long reads predicted all newly identified variants were on a different haplotype than the previously known variant. Finally, in one case, RT-PCR previously identified skipping of exon 20; however, T-LRS did not detect a pathogenic DNA sequence variant. T-LRS is an effective method for identifying missing pathogenic variants. Although limitations with computational prediction algorithms can hinder the interpretation of variants, T-LRS is particularly effective in identifying intronic variants.

Sections du résumé

BACKGROUND BACKGROUND
Werner syndrome (WS) is an autosomal recessive progeroid syndrome caused by variants in
METHODS METHODS
Targeted long-read sequencing (T-LRS) on an Oxford Nanopore platform was used to search for a second pathogenic variant in
RESULTS RESULTS
We identified a second pathogenic variant in eight of nine unsolved WS cases. In five cases, T-LRS identified intronic splice variants that were confirmed by either RT-PCR or exon trapping to affect splicing; in one case, T-LRS identified a 339 kbp deletion, and in two cases, pathogenic missense variants. Phasing of long reads predicted all newly identified variants were on a different haplotype than the previously known variant. Finally, in one case, RT-PCR previously identified skipping of exon 20; however, T-LRS did not detect a pathogenic DNA sequence variant.
CONCLUSION CONCLUSIONS
T-LRS is an effective method for identifying missing pathogenic variants. Although limitations with computational prediction algorithms can hinder the interpretation of variants, T-LRS is particularly effective in identifying intronic variants.

Identifiants

pubmed: 35534204
pii: jmedgenet-2022-108485
doi: 10.1136/jmedgenet-2022-108485
pmc: PMC9613861
mid: NIHMS1802737
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NCI NIH HHS
ID : R01 CA210916
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH101221
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG011744
Pays : United States
Organisme : NHGRI NIH HHS
ID : UM1 HG006493
Pays : United States

Informations de copyright

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: DEM has received travel support from Oxford Nanopore Technologies (ONT) to speak on their behalf. DEM is a paid consultant for and holds stock options in MyOme. DEM and EEE are engaged in a research agreement with ONT. EEE is a scientific advisory board (SAB) member of Variant Bio, Inc.

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Auteurs

Danny E Miller (DE)

Department of Pediatrics, Division of Genetic Medicine, University of Washington, Seattle, Washington, USA dm1@uw.edu picard@uw.edu.
Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA.

Lin Lee (L)

Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Miranda Galey (M)

Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA.

Renuka Kandhaya-Pillai (R)

Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Marc Tischkowitz (M)

Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK.

Deepak Amalnath (D)

Department of Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Avadh Vithlani (A)

Department of Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Koutaro Yokote (K)

Department of Endocrinology, Hematology and Gerontology, Chiba University Graduate School of Medicine, Chiba, Japan.

Hisaya Kato (H)

Department of Endocrinology, Hematology and Gerontology, Chiba University Graduate School of Medicine, Chiba, Japan.

Yoshiro Maezawa (Y)

Department of Endocrinology, Hematology and Gerontology, Chiba University Graduate School of Medicine, Chiba, Japan.

Aki Takada-Watanabe (A)

Department of Endocrinology, Hematology and Gerontology, Chiba University Graduate School of Medicine, Chiba, Japan.

Minoru Takemoto (M)

Department of Diabetes, Metabolism and Endocrinology, International University of Health and Welfare, Otawara, Japan.

George M Martin (GM)

Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Evan E Eichler (EE)

Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA.
Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA.

Fuki M Hisama (FM)

Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, Washington, USA.

Junko Oshima (J)

Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA dm1@uw.edu picard@uw.edu.
Department of Endocrinology, Hematology and Gerontology, Chiba University Graduate School of Medicine, Chiba, Japan.

Classifications MeSH