Evidence map›Paper›PMID 38538918›Full record

ArticleHuman genetics2024

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

Alejandro Ferrer, Patrick Duffy, Rory J Olson, Michael A Meiners, Laura Schultz-Rogers, Erica L Macke, Stephanie Safgren, Joel A Morales-Rosado, Margot A Cousin, Gavin R Oliver and 18 more

Abstract read
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In one paragraph

Article in Human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

28 authors.

Alejandro FerrerDivision of Hematology, Mayo Clinic, Rochester, MN, USA.
Patrick DuffyBioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA.
Rory J OlsonCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Michael A MeinersBioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA.
Laura Schultz-RogersDepartment of Pathology and Lab Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Erica L MackeThe Institute of Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA.
Stephanie SafgrenCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Joel A Morales-RosadoDepartment of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
Margot A CousinCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Gavin R OliverCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
David RiderBioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA.
Megan WilliamsBioinformatics Systems, Information Technology, Mayo Clinic, Rochester, MN, USA.
Pavel N PichurinDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
David R DeyleDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Eva MoravaDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Ralitza H GavrilovaDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Radhika DhamijaDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Klass J WierengaDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Brendan C LanpherDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Dusica Babovic-VuksanovicDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Charu KaiwarChildren's Hospital Colorado, Aurora, CO, USA.
Carolyn R VitekCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Tammy M McAllisterCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Myra J WickDepartment of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.
Lisa A SchimmentiCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Konstantinos N LazaridisCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Filippo Pinto E VairoCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Eric W KleeCenter for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. Klee.Eric@mayo.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

GenomicsDatabases, GeneticExomeExome SequencingGenetic TestingGenome, HumanHumansPhenotypeWhole Genome Sequencing

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.