Evidence map›Paper›PMID 18951437›Full record

ArticleHuman mutation2008

Genetic evidence and integration of various data sources for classifying uncertain variants into a single model.

David E Goldgar, Douglas F Easton, Graham B Byrnes, Amanda B Spurdle, Edwin S Iversen, Marc S Greenblatt, IARC Unclassified Genetic Variants Working Group

Abstract read
In one paragraph

Article in Human mutation, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.

0numbers the graph read from it
0cells of the map it votes in
103citing 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

103 citing papers in PubMed.

  1. Article
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  6. Review
  7. Article
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  13. Article
  14. Assessing the pathogenicity ofFrontiers in oncology · 2022
    Article
  15. Accurate assignment of disease liability to genetic variants using only population data.Genetics in medicine : official journal of the American College of Medical Genetics · 2022
    Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Review

43 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

7 authors.

David E GoldgarDepartment of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah 84132-2409, USA. david.goldgar@hsc.utah.edu
Douglas F Easton
Graham B Byrnes
Amanda B Spurdle
Edwin S Iversen
Marc S Greenblatt
IARC Unclassified Genetic Variants Working Group

Funding

The Role of CHFR in Tumorigenesis and Paclitaxel-Sensitivity in Breast CancerP50CA116201 · NCI · MAYO CLINIC ROCHESTER · PI PETER C LUCAS · 2005 to 2026
$49.9M
BRCA2 missense mutations and breast cancerR01CA116167 · NCI · MAYO CLINIC ROCHESTER · PI COUCH, FERGUS JOSEPH · 2007 to 2011
$3.0M
BRCA1 and BRCA2 missense mutations and breast cancer riskU01CA116167 · NCI · MAYO CLINIC ROCHESTER · PI COUCH, FERGUS JOSEPH · 2013 to 2017
$2.7M
COMPUTATIONAL &LABORATORY STUDY OF P16/INK4 MUTATIONSR01CA096536 · NCI · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI GREENBLATT, MARC S · 2002 to 2004
$739k
COMPUTATIONAL &LABORATORY STUDY OF P16/INK4 MUTATIONSR55CA096536 · NCI · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI GREENBLATT, MARC S · 2002 to 2002
$50k
NCI NIH HHS CA116167NCI NIH HHS CA96536NCI NIH HHS P50 CA116201NCI NIH HHS R01 CA096536NCI NIH HHS R01 CA116167NCI NIH HHS R55 CA096536NCI NIH HHS U01 CA116167
6 · The paper itself

Abstract

Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case-control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two-component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process.

Indexed as

Genetic Predisposition to DiseaseModels, BiologicalBayes TheoremCase-Control StudiesData CollectionGenetic TestingGenetic VariationGenotypeHumansLikelihood FunctionsNeoplastic Syndromes, HereditaryPhenotypeRisk FactorsSensitivity and SpecificityUncertainty

Identifiers

PMID18951437
PMCPMC2936773

What OpenQuestion holds

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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.