Evidence map›Paper›PMID 23300413›Full record

ReviewPLoS computational biology2012

Chapter 11: Genome-wide association studies.

William S Bush, Jason H Moore

Abstract readReview
In one paragraph

Review in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 488 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
488citing papers in PubMed, 5 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

488 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Trial
  8. Article
  9. Testing for Genetic Interactions in Complex Disease With Distance Correlation.Biometrical journal. Biometrische Zeitschrift · 2026
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Estimating recombination fraction via Pearson correlation.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  17. Article
  18. Article
  19. Genome-wide association study identifies significant SNPs for milk production and stature traits in Karan Fries cattle.Mammalian genome : official journal of the International Mammalian Genome Society · 2026
    Article
  20. Article

428 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.

William S BushDepartment of Biomedical Informatics, Center for Human Genetics Research, Vanderbilt University Medical School, Nashville, Tennessee, United States of America. william.s.bush@vanderbilt.edu
Jason H Moore

Funding

Quantitative Biology Research InstituteP20GM103534 · NIGMS · DARTMOUTH COLLEGE · PI AMOS, CHRISTOPHER I. · 2012 to 2016
$8.6M
Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
Bioinformatics Strategies: Biodefense Vaccine ResearchR01AI059694 · NIAID · DARTMOUTH COLLEGE · PI MOORE, JASON H. · 2004 to 2012
$4.5M
Machine Learning Prediction of Cancer SusceptibilityR01LM009012 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2006 to 2014
$3.3M
Bioinformatics Strategies for Multidimensional Brain Imaging GeneticsR01LM011360 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI MOORE, JASON H., SAYKIN, ANDREW J · 2012 to 2015
$1.4M
Bioinformatics Approaches to Visual Disease GeneticsR01EY022300 · NEI · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2012 to 2014
$947k
NEI NIH HHS R01 EY022300NEI NIH HHS R01-EY022300NIAID NIH HHS R01 AI059694NIAID NIH HHS R0I-AI59694NIGMS NIH HHS P20 GM103534NLM NIH HHS R01 LM009012NLM NIH HHS R01 LM010098NLM NIH HHS R01 LM011360NLM NIH HHS R01-LM011360NLM NIH HHS R0I-LM009012NLM NIH HHS R0I-LM010098
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.

Indexed as

Genome-Wide Association StudyElectronic Health RecordsGenetic LinkageGenetic VariationHaplotypesHumansPhenotype

Identifiers

PMID23300413
PMCPMC3531285

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.