A genome-wide association analysis of a broad psychosis phenotype identifies three loci for further investigation
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(2014). A genome-wide association analysis of a broad psychosis phenotype identifies three loci for further investigation. Biological Psychiatry 75 (5), 386-397
Background: Genome-wide association studies (GWAS) have identified several loci associated with schizophrenia and/or bipolar disorder. We performed a GWAS of psychosis as a broad syndrome rather than within specific diagnostic categories. Methods: 1239 cases with schizophrenia, schizoaffective disorder, or psychotic bipolar disorder; 857 of their unaffected relatives, and 2739 healthy controls were genotyped with the Affymetrix 6.0 single nucleotide polymorphism (SNP) array. Analyses of 695,193 SNPs were conducted using UNPHASED, which combines information across families and unrelated individuals. We attempted to replicate signals found in 23 genomic regions using existing data on nonoverlapping samples from the Psychiatric GWAS Consortium and Schizophrenia-GENE-plus cohorts (10,352 schizophrenia patients and 24,474 controls). Results: No individual SNP showed compelling evidence for association with psychosis in our data. However, we observed a trend for association with same risk alleles at loci previously associated with schizophrenia (one-sided p.003). A polygenic score analysis found that the Psychiatric GWAS Consortium's panel of SNPs associated with schizophrenia significantly predicted disease status in our sample (p = 5 x 10(-14)) and explained approximately 2% of the phenotypic variance. Conclusions: Although narrowly defined phenotypes have their advantages, we believe new loci may also be discovered through metaanalysis across broad phenotypes. The novel statistical methodology we introduced to model effect size heterogeneity between studies should help future GWAS that combine association evidence from related phenotypes. Applying these approaches, we highlight three loci that warrant further investigation. We found that SNPs conveying risk for schizophrenia are also predictive of disease status in our data.