Evidence map›Paper›PMID 22293689›Full record

ArticleEuropean journal of human genetics : EJHG2012

An evaluation of different meta-analysis approaches in the presence of allelic heterogeneity.

Jennifer Asimit, Aaron Day-Williams, Lina Zgaga, Igor Rudan, Vesna Boraska, Eleftheria Zeggini

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of human genetics : EJHG, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

  1. Pooled it
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  6. Article
  7. Review
  8. Review
  9. Association between CCR6 and rheumatoid arthritis: a meta-analysis.International journal of clinical and experimental medicine · 2015
    Article
  10. Using population isolates in genetic association studies.Briefings in functional genomics · 2014
    Review
  11. Article
  12. Review
  13. 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

6 authors.

Jennifer AsimitDepartment of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.
Aaron Day-Williams
Lina Zgaga
Igor Rudan
Vesna Boraska
Eleftheria Zeggini

Funding

Wellcome Trust 098051
6 · The paper itself

Abstract

Meta-analysis has proven a useful tool in genetic association studies. Allelic heterogeneity can arise from ethnic background differences across populations being meta-analyzed (for example, in search of common frequency variants through genome-wide association studies), and through the presence of multiple low frequency and rare associated variants in the same functional unit of interest (for example, within a gene or a regulatory region). The latter challenge will be increasingly relevant in whole-genome and whole-exome sequencing studies investigating association with complex traits. Here, we evaluate the performance of different approaches to meta-analysis in the presence of allelic heterogeneity. We simulate allelic heterogeneity scenarios in three populations and examine the performance of current approaches to the analysis of these data. We show that current approaches can detect only a small fraction of common frequency causal variants. We also find that for low-frequency variants with large effects (odds ratios 2-3), single-point tests have high power, but also high false-positive rates. P-value based meta-analysis of summary results from allele-matching locus-wide tests outperforms collapsing approaches. We conclude that current strategies for the combination of genetic association data in the presence of allelic heterogeneity are insufficiently powered.

Indexed as

AllelesGenetic HeterogeneityMeta-Analysis as TopicGenetic VariationGenome-Wide Association StudyHumans

Identifiers

PMID22293689
PMCPMC3355266

What OpenQuestion holds

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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.