Evidence map›Paper›PMID 24896101›Full record

ArticlePloS one2014

Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank.

Jacob B Hall, Logan Dumitrescu, Holli H Dilks, Dana C Crawford, William S Bush

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 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

31 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
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  8. Article
  9. Pharmacogenomic and Statistical Analysis.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
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  12. Observational
  13. Observational
  14. A Global Comparison of Anatomic Risk Factors and Their Relationship to Obstructive Sleep Apnea Severity in Clinical Samples.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2019
    Article
  15. Observational
  16. Local ancestry transitions modify snp-trait associations.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018
    Article
  17. Article
  18. Article
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  20. Reducing Clinical Noise for Body Mass Index Measures Due to Unit and Transcription Errors in the Electronic Health Record.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017
    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

5 authors.

Jacob B HallCenter for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America.
Logan DumitrescuCenter for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America.
Holli H DilksVanderbilt Technologies for Advanced Genomics (VANTAGE), Vanderbilt University, Nashville, Tennessee, United States of America.
Dana C CrawfordCenter for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America.
William S BushCenter for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America.

Funding

The Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR000445 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$41.4M
Training Program on Genetic Variation and Human PhenotypesT32GM080178 · NIGMS · VANDERBILT UNIVERSITY · PI COX, NANCY J, SAMUELS, DAVID C · 2007 to 2021
$3.1M
Training Program in Quantitative Ocular GenomicsT32EY021453 · NEI · VANDERBILT UNIVERSITY · PI BRANTLEY, MILAM A, COX, NANCY J · 2011 to 2020
$1.4M
NCATS NIH HHS UL1 TR000445NEI NIH HHS T32 EY021453NIGMS NIH HHS T32 GM080178
6 · The paper itself

Abstract

Recently, the development of biobanks linked to electronic medical records has presented new opportunities for genetic and epidemiological research. Studies based on these resources, however, present unique challenges, including the accurate assignment of individual-level population ancestry. In this work we examine the accuracy of administratively-assigned race in diverse populations by comparing assigned races to genetically-defined ancestry estimates. Using 220 ancestry informative markers, we generated principal components for patients in our dataset, which were used to cluster patients into groups based on genetic ancestry. Consistent with other studies, we find a strong overall agreement (Kappa  = 0.872) between genetic ancestry and assigned race, with higher rates of agreement for African-descent and European-descent assignments, and reduced agreement for Hispanic, East Asian-descent, and South Asian-descent assignments. These results suggest caution when selecting study samples of non-African and non-European backgrounds when administratively-assigned race from biobanks is used.

Indexed as

Biological Specimen BanksDatabases, GeneticElectronic Health RecordsEthnicityGenetic MarkersHumansRacial GroupsGenetic Markers

Identifiers

PMID24896101
PMCPMC4045967

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.