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.

Moskvina, V., Craddock, N., Holmans, P., Nikolov, I., Pahwa, J. S., Green, E., Wellcome Trust Case Control Consortium, Brown, Matthew A., Bradbury, Linda, Owen, M. J. and O'Donovan, M. C. (2009) 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.. Molecular Psychiatry, 14 3: 252-260. doi:10.1038/mp.2008.133


Author Moskvina, V.
Craddock, N.
Holmans, P.
Nikolov, I.
Pahwa, J. S.
Green, E.
Wellcome Trust Case Control Consortium
Brown, Matthew A.
Bradbury, Linda
Owen, M. J.
O'Donovan, M. C.
Title 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.
Journal name Molecular Psychiatry   Check publisher's open access policy
ISSN 1359-4184
Publication date 2009-03
Year available 2008
Sub-type Article (original research)
DOI 10.1038/mp.2008.133
Volume 14
Issue 3
Start page 252
End page 260
Total pages 9
Editor Licinio, J.
Place of publication United Kingdom
Publisher Nature Publishing Group
Language eng
Subject C1
970111 Expanding Knowledge in the Medical and Health Sciences
060405 Gene Expression (incl. Microarray and other genome-wide approaches)
110319 Psychiatry (incl. Psychotherapy)
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.
Keyword SCAN METAANALYSIS
MULTILOCUS MODELS
REPLICATION
REFUTATION
ETIOLOGY
HAPMAP
LOCUS
Q-Index Code C1
Q-Index Status Provisional Code
Additional Notes Please note that Matthew Brown and Linda Bradbury are members of the Wellcome Trust Case Control Consortium

Document type: Journal Article
Sub-type: Article (original research)
Collection: UQ Diamantina Institute Publications
 
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Created: Wed, 14 Apr 2010, 13:15:50 EST by Kylie Hengst on behalf of UQ Diamantina Institute