Evidence map›Paper›PMID 24033266›Full record

ArticleClinical genetics2013

A systematic approach to assessing the clinical significance of genetic variants.

H Duzkale, J Shen, H McLaughlin, A Alfares, M A Kelly, T J Pugh, B H Funke, H L Rehm, M S Lebo

Open access · greenAbstract read
In one paragraph

Article in Clinical genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed, 2 pooled it
13.9field-weighted citation impact, top 1% of its field
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

105 citing papers in PubMed, 2 syntheses or guidelines pooled it, 189 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Structured Application of Genetic Testing in a Pediatric Kidney Clinic.Journal of the American Society of Nephrology : JASN · 2026
    Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Biomedicines · 2024
    Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Clinical and functional analysis of the germlineCold Spring Harbor molecular case studies · 2023
    Article
  18. Article
  19. [Genomic newborn screening-research approaches, challenges, and opportunities].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2023
    Review
  20. AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine.Database : the journal of biological databases and curation · 2023
    Article

45 more citing papers are in PubMed but not listed here.

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

9 authors at 4 institutions in 2 countries.

H DuzkaleHarvard Medical School Genetics Training Program, Boston, MA, USA; Laboratory for Molecular Medicine, Partners HealthCare Center for Personalized Genetic Medicine, Cambridge, MA, USA.
J Shen
H McLaughlin
A Alfares
M A Kelly
T J Pugh
B H Funke
H L Rehm
M S Lebo
Harvard University · USMass General Brigham · USMassachusetts General Hospital · USWoman's Hospital · US

Funding

Resource ProjectU41HG006834 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI REHM, HEIDI L · 2013 to 2020
$26.9M
ClinGen AI Data Delivery SupplementU24HG006834 · NHGRI · BROAD INSTITUTE, INC. · PI Marina DiStefano, CHRISTA LESE MARTIN · 2021 to 2026
$26.7M
Integration of Whole Genome Sequencing into Clinical MedicineU01HG006500 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI GREEN, ROBERT C. · 2012 to 2016
$11.5M
NHGRI NIH HHS HG006500NHGRI NIH HHS HG006834NHGRI NIH HHS U01 HG006500NHGRI NIH HHS U24 HG006834NHGRI NIH HHS U41 HG006834
6 · The paper itself

Abstract

Molecular genetic testing informs diagnosis, prognosis, and risk assessment for patients and their family members. Recent advances in low-cost, high-throughput DNA sequencing and computing technologies have enabled the rapid expansion of genetic test content, resulting in dramatically increased numbers of DNA variants identified per test. To address this challenge, our laboratory has developed a systematic approach to thorough and efficient assessments of variants for pathogenicity determination. We first search for existing data in publications and databases including internal, collaborative and public resources. We then perform full evidence-based assessments through statistical analyses of observations in the general population and disease cohorts, evaluation of experimental data from in vivo or in vitro studies, and computational predictions of potential impacts of each variant. Finally, we weigh all evidence to reach an overall conclusion on the potential for each variant to be disease causing. In this report, we highlight the principles of variant assessment, address the caveats and pitfalls, and provide examples to illustrate the process. By sharing our experience and providing a framework for variant assessment, including access to a freely available customizable tool, we hope to help move towards standardized and consistent approaches to variant assessment.

Indexed as

AlgorithmsGenetic TestingGenetic VariationSoftwareBase SequenceDatabases, GeneticDecision TreesFemaleHigh-Throughput Nucleotide SequencingHumansMaleMolecular Sequence DataPrognosisRisk AssessmentRNA, MessengerRNA, Messenger(4−9) clinical interpretationgain-of-function (GOF)genetic variantloss of function (LOF)next-generation sequencing (NGS)sequence analysisvariant assessmentvariant of uncertain significance (VUS)

Identifiers

PMID24033266
PMCPMC3995020
OpenAlexW1564417183

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.