Evidence map›Paper›PMID 19763151›Full record

ReviewNature reviews. Genetics2009

Bayesian statistical methods for genetic association studies.

Matthew Stephens, David J Balding

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 228 papers, 18 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
228citing papers in PubMed, 18 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

228 citing papers in PubMed, 18 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Pooled it
  11. Pooled it
  12. Pooled it
  13. Pooled it
  14. Pooled it
  15. Pooled it
  16. Pooled it
  17. Pooled it
  18. Pooled it
  19. Trial
  20. Review

168 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.

Matthew StephensDepartments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637, USA. mstephens@uchicago.edu
David J Balding

Funding

Multipoint and significance methods for genome-wide association studiesU01HL084689 · NHLBI · UNIVERSITY OF WASHINGTON · PI STEPHENS, MATTHEW · 2006 to 2008
$880k
EAPO CDC HHS EP/C533542NHLBI NIH HHS HL084689
6 · The paper itself

Abstract

Bayesian statistical methods have recently made great inroads into many areas of science, and this advance is now extending to the assessment of association between genetic variants and disease or other phenotypes. We review these methods, focusing on single-SNP tests in genome-wide association studies. We discuss the advantages of the Bayesian approach over classical (frequentist) approaches in this setting and provide a tutorial on basic analysis steps, including practical guidelines for appropriate prior specification. We demonstrate the use of Bayesian methods for fine mapping in candidate regions, discuss meta-analyses and provide guidance for refereeing manuscripts that contain Bayesian analyses.

Indexed as

Bayes TheoremGenome-Wide Association StudyHumansPolymorphism, Single Nucleotide

Identifiers

PMID19763151

What OpenQuestion holds

Textmetadata
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.