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 (Pmin for excess<0.001) showing evidence for association than expected whereas this was not true for extragenic SNPs (Pmin for excess>0.1). At a range of thresholds of significance, we also observed substantially more associated genes than expected (Pmin for excess in schizophrenia=1.8 × 10−8, in bipolar=2.4 × 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.
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This study was supported by the Medical Research Council (UK) and by the Wellcome Trust. This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for that project was provided by the Wellcome Trust under award 076113.
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Moskvina, V., Craddock, N., Holmans, P. et al. 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. Mol Psychiatry 14, 252–260 (2009). https://doi.org/10.1038/mp.2008.133
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