Evidence map›Paper›PMID 28132688›Full record

ArticleAmerican journal of human genetics2017

InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines.

Quan Li, Kai Wang

Abstract read
In one paragraph

Article in American journal of human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 668 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
668citing papers in PubMed, 1 pooled it
–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

668 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. AAVC: An automated framework for high-accuracy ACMG-based variant classification.Genetics in medicine : official journal of the American College of Medical Genetics · 2026
    Article
  20. Article

608 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

2 authors.

Quan LiZilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA.
Kai WangZilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA; Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA; Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Electronic address: kw2701@cumc.columbia.edu.

Funding

Integrated variation detection annotation and analysis for high-throughout sequeR01HG006465 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, KAI · 2012 to 2016
$1.8M
UNDERSTANDING THE FUNCTIONAL IMPACTS OF GENETIC VARIANTS IN MENTAL DISORDERSR01MH108728 · NIMH · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, KAI · 2016 to 2018
$1.2M
NHGRI NIH HHS R01 HG006465NIMH NIH HHS R01 MH108728
6 · The paper itself

Abstract

In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published updated standards and guidelines for the clinical interpretation of sequence variants with respect to human diseases on the basis of 28 criteria. However, variability between individual interpreters can be extensive because of reasons such as the different understandings of these guidelines and the lack of standard algorithms for implementing them, yet computational tools for semi-automated variant interpretation are not available. To address these problems, we propose a suite of methods for implementing these criteria and have developed a tool called InterVar to help human reviewers interpret the clinical significance of variants. InterVar can take a pre-annotated or VCF file as input and generate automated interpretation on 18 criteria. Furthermore, we have developed a companion web server, wInterVar, to enable user-friendly variant interpretation with an automated interpretation step and a manual adjustment step. These tools are especially useful for addressing severe congenital or very early-onset developmental disorders with high penetrance. Using results from a few published sequencing studies, we demonstrate the utility of InterVar in significantly reducing the time to interpret the clinical significance of sequence variants.

Indexed as

Genetic VariationAlgorithmsComputational BiologyDatabases, GeneticEvidence-Based MedicineGenetic TestingGenome, HumanGenomicsHigh-Throughput Nucleotide SequencingHumansMolecular Sequence AnnotationMutation, MissenseNeurodevelopmental DisordersSoftwareACMGANNOVARclinical interpretationClinVargenetic diagnosisInterVarvariant annotationvariant interpretation

Identifiers

PMID28132688
PMCPMC5294755

What OpenQuestion holds

Textmetadata
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.