Evidence map›Paper›PMID 19065143›Full record

ArticleMolecular psychiatry2009

Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk.

V Moskvina, N Craddock, P Holmans, I Nikolov, J S Pahwa, E Green, Wellcome Trust Case Control Consortium, M J Owen, M C O'Donovan

Abstract read
In one paragraph

Article in Molecular psychiatry, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
165citing papers in PubMed, 7 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

165 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  6. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder.Journal of the American Academy of Child and Adolescent Psychiatry · 2010
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  13. L-methionine and the L-type CaFrontiers in neural circuits · 2024
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105 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

9 authors.

V MoskvinaDepartment of Psychological Medicine, School of Medicine, Cardiff University, Cardiff, UK.
N Craddock
P Holmans
I Nikolov
J S Pahwa
E Green
Wellcome Trust Case Control Consortium
M J Owen
M C O'Donovan

Funding

Medical Research Council G0000934Medical Research Council G0800509Medical Research Council G0801418Wellcome Trust 076113Wellcome Trust 079643
6 · The paper itself

Abstract

Genome-wide association (GWAS) analyses have identified susceptibility loci for many diseases, but most risk for any complex disorder remains unattributed. There is therefore scope for complementary approaches to these data sets. Gene-wide approaches potentially offer additional insights. They might identify association to genes through multiple signals. Also, by providing support for genes rather than single nucleotide polymorphisms (SNPs), they offer an additional opportunity to compare the results across data sets. We have undertaken gene-wide analysis of two GWAS data sets: schizophrenia and bipolar disorder. We performed two forms of analysis, one based on the smallest P-value per gene, the other on a truncated product of P method. For each data set and at a range of statistical thresholds, we observed significantly more SNPs within genes (P(min) for excess<0.001) showing evidence for association than expected whereas this was not true for extragenic SNPs (P(min) for excess>0.1). At a range of thresholds of significance, we also observed substantially more associated genes than expected (P(min) for excess in schizophrenia=1.8 x 10(-8), in bipolar=2.4 x 10(-6)). Moreover, an excess of genes showed evidence for association across disorders. Among those genes surpassing thresholds highly enriched for true association, we observed evidence for association to genes reported in other GWAS data sets (CACNA1C) or to closely related family members of those genes including CSF2RB, CACNA1B and DGKI. Our analyses show that association signals are enriched in and around genes, large numbers of genes contribute to both disorders and gene-wide analyses offer useful complementary approaches to more standard methods.

Indexed as

Genetic Predisposition to DiseaseGenome, HumanGenome-Wide Association StudyBipolar DisorderDatabases, GeneticGenetic LinkageGenomic LibraryHumansModels, GeneticPolymorphism, Single NucleotideSchizophrenia

Identifiers

PMID19065143
PMCPMC3970088

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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.