Evidence map›Paper›PMID 21397060›Full record

ArticleAmerican journal of human genetics2011

GATES: a rapid and powerful gene-based association test using extended Simes procedure.

Miao-Xin Li, Hong-Sheng Gui, Johnny S H Kwan, Pak C Sham

Abstract read
In one paragraph

Article in American journal of human genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 264 papers, 22 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
264citing papers in PubMed, 22 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

264 citing papers in PubMed, 22 syntheses or guidelines pooled it.

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204 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

4 authors.

Miao-Xin LiDepartment of Psychiatry and State Key Laboratory for Cognitive and Brain Sciences, the University of Hong Kong, Pokfulam, Hong Kong.
Hong-Sheng Gui
Johnny S H Kwan
Pak C Sham

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The gene has been proposed as an attractive unit of analysis for association studies, but a simple yet valid, powerful, and sufficiently fast method of evaluating the statistical significance of all genes in large, genome-wide datasets has been lacking. Here we propose the use of an extended Simes test that integrates functional information and association evidence to combine the p values of the single nucleotide polymorphisms within a gene to obtain an overall p value for the association of the entire gene. Our computer simulations demonstrate that this test is more powerful than the SNP-based test, offers effective control of the type 1 error rate regardless of gene size and linkage-disequilibrium pattern among markers, and does not need permutation or simulation to evaluate empirical significance. Its statistical power in simulated data is at least comparable, and often superior, to that of several alternative gene-based tests. When applied to real genome-wide association study (GWAS) datasets on Crohn disease, the test detected more significant genes than SNP-based tests and alternative gene-based tests. The proposed test, implemented in an open-source package, has the potential to identify additional novel disease-susceptibility genes for complex diseases from large GWAS datasets.

Indexed as

Computer SimulationCrohn DiseaseDatabases, GeneticGenetic Association StudiesGenome-Wide Association StudyHumansLinkage DisequilibriumPolymorphism, Single NucleotideReproducibility of Results

Identifiers

PMID21397060
PMCPMC3059433

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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.