Evidence map›Paper›PMID 26736087›Full record

ArticlePharmacogenetics and genomics2016

PGRNseq: a targeted capture sequencing panel for pharmacogenetic research and implementation.

Adam S Gordon, Robert S Fulton, Xiang Qin, Elaine R Mardis, Deborah A Nickerson, Steve Scherer

Abstract read
In one paragraph

Article in Pharmacogenetics and genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing 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

65 citing papers in PubMed.

  1. Article
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  7. Pharmacogenomic and Statistical Analysis.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  8. The Implementation of Pharmacogenetics in the United Kingdom.Handbook of experimental pharmacology · 2023
    Article
  9. Article
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  11. Review
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  14. Review
  15. Article
  16. Article
  17. Targeted Genotyping in Clinical Pharmacogenomics: What Is Missing?The Journal of molecular diagnostics : JMD · 2022
    Article
  18. Article
  19. Article
  20. Article

5 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

6 authors.

Adam S GordonaDepartment of Genome Sciences, University of Washington, Seattle, Washington bThe Baylor College of Medicine Human Genome Sequencing Center, Houston, Texas cThe Genome Institute at Washington University, St Louis, Missouri, USA.
Robert S Fulton
Xiang Qin
Elaine R Mardis
Deborah A Nickerson
Steve Scherer

Funding

Pharmacogenomics and Risk of Cardiovascular DiseaseU01HL069757 · NHLBI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI KRAUSS, RONALD M · 2001 to 2009
$24.7M
Pharmacogenetics of Phase II Drug Metabolizing EnzymesU19GM061388 · NIGMS · MAYO CLINIC ROCHESTER · PI WEINSHILBOUM, RICHARD M. · 2010 to 2014
$15.8M
PHARMACOGENETICS OF PHASE II DRUG METABOLIZING ENZYMESU01GM061388 · NIGMS · MAYO CLINIC ROCHESTER · PI WEINSHILBOUM, RICHARD M. · 2000 to 2009
$15.7M
Pharmacogenomics and Risk of Cardiovascular DiseaseU19HL069757 · NHLBI · CHILDREN'S HOSPITAL & RES CTR AT OAKLAND · PI KRAUSS, RONALD M · 2010 to 2014
$12.8M
Medical Genetics Training GrantT32GM007454 · NIGMS · UNIVERSITY OF WASHINGTON · PI Gail Pairitz Jarvik, Andrew Ben Stergachis · 1985 to 2026
$6.9M
NEXT GENERATION SEQUENCING ADD-ON-PHARMACOGENOMICS RESEARCH NETWROK (PGRN)U01GM097119 · NIGMS · WASHINGTON UNIVERSITY · PI MARDIS, ELAINE R · 2010 to 2014
$2.6M
Warfarin Therapy Using Computer Assisted GuidanceR43HL069579 · NHLBI · STANDING STONE, INC. · PI WURSTER, MARK W · 2002 to 2004
$107k
NHLBI NIH HHS R43 HL069579NHLBI NIH HHS U01 HL069757NHLBI NIH HHS U19 HL069757NIGMS NIH HHS T32 GM007454NIGMS NIH HHS U01 GM061388NIGMS NIH HHS U01 GM097119NIGMS NIH HHS U19 GM061388
6 · The paper itself

Abstract

objectivesAlthough the costs associated with whole-genome and whole-exome next-generation sequencing continue to decline, they remain prohibitively expensive for large-scale studies of genetic variation. As an alternative, custom-target sequencing has become a common methodology on the basis of its favorable balance between cost, throughput, and deep coverage.

methodsWe have developed PGRNseq, a custom-capture panel of 84 genes with associations to pharmacogenetic phenotypes, as a tool to explore the relationship between drug response and genetic variation, both common and rare. We utilized a set of 32 diverse HapMap trios and two clinical cohorts to assess platform performance, accuracy, and ability to discover novel variation.

resultsWe found that PGRNseq generates ultra-deep coverage data (mean=496x) that are over 99.8% concordant with orthogonal datasets. In addition, in our testing sets, PGRNseq identified many novel, rare variants of interest, underscoring its value in both research and clinical settings.

conclusionPGRNseq is an ideal platform for carrying out sequencing-based analyses of pharmacogenetic variation in large cohorts. In addition, the high accuracy associated with genotypes from PGRNseq highlight its utility as a clinical test.

Identifiers

PMID26736087
PMCPMC4935646

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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.