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Semiautomated approach focused on new genomic information results in time and effort-efficient reannotation of negative exome data.

Authors :
Ferrer A
Duffy P
Olson RJ
Meiners MA
Schultz-Rogers L
Macke EL
Safgren S
Morales-Rosado JA
Cousin MA
Oliver GR
Rider D
Williams M
Pichurin PN
Deyle DR
Morava E
Gavrilova RH
Dhamija R
Wierenga KJ
Lanpher BC
Babovic-Vuksanovic D
Kaiwar C
Vitek CR
McAllister TM
Wick MJ
Schimmenti LA
Lazaridis KN
Vairo FPE
Klee EW
Source :
Human genetics [Hum Genet] 2024 May; Vol. 143 (5), pp. 649-666. Date of Electronic Publication: 2024 Mar 27.
Publication Year :
2024

Abstract

Most rare disease patients (75-50%) undergoing genomic sequencing remain unsolved, often due to lack of information about variants identified. Data review over time can leverage novel information regarding disease-causing variants and genes, increasing this diagnostic yield. However, time and resource constraints have limited reanalysis of genetic data in clinical laboratories setting. We developed RENEW, (REannotation of NEgative WES/WGS) an automated reannotation procedure that uses relevant new information in on-line genomic databases to enable rapid review of genomic findings. We tested RENEW in an unselected cohort of 1066 undiagnosed cases with a broad spectrum of phenotypes from the Mayo Clinic Center for Individualized Medicine using new information in ClinVar, HGMD and OMIM between the date of previous analysis/testing and April of 2022. 5741 variants prioritized by RENEW were rapidly reviewed by variant interpretation specialists. Mean analysis time was approximately 20 s per variant (32 h total time). Reviewed cases were classified as: 879 (93.0%) undiagnosed, 63 (6.6%) putatively diagnosed, and 4 (0.4%) definitively diagnosed. New strategies are needed to enable efficient review of genomic findings in unsolved cases. We report on a fast and practical approach to address this need and improve overall diagnostic success in patient testing through a recurrent reannotation process.<br /> (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Details

Language :
English
ISSN :
1432-1203
Volume :
143
Issue :
5
Database :
MEDLINE
Journal :
Human genetics
Publication Type :
Academic Journal
Accession number :
38538918
Full Text :
https://doi.org/10.1007/s00439-024-02664-3