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

被引:281
作者
Moskvina, V. [1 ,2 ]
Craddock, N. [1 ]
Holmans, P. [1 ]
Nikolov, I. [1 ,2 ]
Pahwa, J. S. [1 ,2 ]
Green, E. [1 ]
Owen, M. J. [1 ]
O'Donovan, M. C. [1 ]
机构
[1] Cardiff Univ, Sch Med, Dept Psychol Med, Cardiff CF23 6BQ, S Glam, Wales
[2] Cardiff Univ, Sch Med, Bioinformat & Stat Unit, Cardiff CF23 6BQ, S Glam, Wales
基金
英国惠康基金; 英国医学研究理事会;
关键词
genetics; association; bipolar; schizophrenia; psychosis; SCAN METAANALYSIS; REPLICATION; REFUTATION; ETIOLOGY; LOCUS;
D O I
10.1038/mp.2008.133
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:252 / 260
页数:9
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