Evidence map›Paper›PMID 23196672›Full record

ReviewGenetics in medicine : official journal of the American College of Medical Genetics2013

Mapping the incidentalome: estimating incidental findings generated through clinical pharmacogenomics testing.

Matthew J Westbrook, M Frances Wright, Sara L Van Driest, Tracy L McGregor, Joshua C Denny, Rebecca L Zuvich, Ellen Wright Clayton, Kyle B Brothers

Abstract readReview
In one paragraph

Review in Genetics in medicine : official journal of the American College of Medical Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Observational
  6. Article
  7. Article
  8. Public preferences for the return of research results in genetic research: a conjoint analysis.Genetics in medicine : official journal of the American College of Medical Genetics · 2014
    Article
  9. Secondary use of clinical data: the Vanderbilt approach.Journal of biomedical informatics · 2014
    Article
  10. Article
  11. Article
  12. Review
  13. A framework for analyzing the ethics of disclosing genetic research findings.The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics · 2014
    Article
  14. Article
  15. Ethical, legal, and social implications of incorporating genomic information into electronic health records.Genetics in medicine : official journal of the American College of Medical Genetics · 2013
    Review
  16. Article
  17. Ethical issues in pediatric pharmacogenomics.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2013
    Article
  18. Article
  19. Review
  20. 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

8 authors.

Matthew J WestbrookCenter for Biomedical Ethics and Society, Vanderbilt University, Nashville, Tennessee, USA.
M Frances Wright
Sara L Van Driest
Tracy L McGregor
Joshua C Denny
Rebecca L Zuvich
Ellen Wright Clayton
Kyle B Brothers

Funding

VANDERBILT UNIVERSITY CTSA FOR PEDIATRIC RESEARCHUL1RR024975 · NCRR · VANDERBILT UNIVERSITY · PI BERNARD, GORDON RAPHAEL · 2007 to 2011
$45.7M
The Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR000445 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$41.4M
Repurposing of a Long-Acting Parathyroid Hormone Analog for the Treatment of HypoparathyroidismZIATR000011 · NCATS · NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES · PI YANG, NA · 2015 to 2019
$8.2M
Vanderbilt Genome Electronic Records ProjectU01HG006378 · NHGRI · VANDERBILT UNIVERSITY · PI RODEN, DAN M · 2011 to 2014
$4.0M
NCATS NIH HHS UL1TR000011NCATS NIH HHS UL1 TR000445NCRR NIH HHS UL1 RR024975NHGRI NIH HHS 1U01HG006378-01NHGRI NIH HHS U01 HG006378
6 · The paper itself

Abstract

purposeGreater clinical validity and economic feasibility are driving the more widespread use of multiplex genetic technologies in routine clinical care, especially for applications in pharmacogenomics. Empirical data on the numbers and types of incidental findings generated through such testing are needed to develop policies and practices related to their clinical use. Of particular importance are disparities in findings relevant to different ancestry groups.

methodsThe Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment Resource, or PREDICT, is an institutional program to implement prospective clinical genotyping of 34 pharmacogenomic-related genes to guide drug selection and dosing. We curated 5,566 journal articles to quantify and characterize the incidental, non-pharmacogenomic genotype-phenotype associations that could be generated through this clinical genotyping project.

resultsWe identified 372 putative incidental genotype-phenotype associations that might be revealed in patients undergoing clinical genotyping for pharmacogenomic purposes. Of these, 287 associations were supported by at least one study demonstrating an odds ratio ≥2.0 or ≤0.5. Numbers of potentially relevant findings varied widely by ancestry group.

conclusionRigorous clinical policies for the clinical management of incidental findings are needed because the sheer number of significant findings could prove overwhelming to health-care institutions, providers, and patients.

Indexed as

Genetic Association StudiesIncidental FindingsPharmacogeneticsFemaleGenetic Predisposition to DiseaseGenetic TestingGenomicsHumansOdds RatioPopulation Groups

Identifiers

PMID23196672
PMCPMC3648626

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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.