A genome-wide association analysis of a broad psychosis phenotype identifies three loci for further investigation

Elvira Bramon, Matti Pirinen, Amy Strange, Kuang Lin, Colin Freeman, Céline Bellenguez, Zhan Su, Gavin Band, Richard Pearson, Damjan Vukcevic, Cordelia Langford, Panos Deloukas, Sarah Hunt, Emma Gray, Serge Dronov, Simon C Potter, Avazeh Tashakkori-Ghanbaria, Sarah Edkins, Suzannah J Bumpstead, Maria J ArranzSteven Bakker, Stephan Bender, Richard Bruggeman, Wiepke Cahn, David Chandler, David A Collier, Benedicto Crespo-Facorro, Paola Dazzan, Lieuwe de Haan, Marta Di Forti, Milan Dragović, Ina Giegling, Jeremy Hall, Conrad Iyegbe, Assen Jablensky, Eugenia Kravariti, Ignacio Mata, Colm McDonald, Carmine M Pariante, Marco Picchioni, Madiha Shaikh, Timothea Toulopoulou, Jim Van Os, Muriel Walshe, Christopher G Mathew, Robert Plomin, Richard C Trembath, Cathryn M Lewis, Robin M Murray, John Powell, Psychosis Endophenotypes International Consortium

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


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:1,239 cases with schizophrenia, schizoaffective or psychotic bipolar disorder, 857 of their unaffected relatives and 2,739 healthy controls were genotyped with the Affymetrix 6.0 SNP array. Analyses of 695,193 SNPs were conducted using UNPHASED, which combines information across families and unrelated individuals. We attempted to replicate signals we found in 23 genomic regions using existing data on non-overlapping samples from the Psychiatric GWAS Consortium (PGC) and SGENE-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 forassociation with same risk alleles at loci previously associated with schizophrenia (one-sided P=0.003). A polygenic scoreanalysisfound that the PGC’s panel of SNPs associated with schizophrenia significantly predicted disease status in our sample (P=5x10-14) and explained approximately 2% of the phenotypic variance.

Conclusion: Although narrowly-defined phenotypes have their advantages,we believe new loci may also be discovered through meta-analysis across broad phenotypes. The novel statistical methodologywe introduced to model effect size heterogeneity between studiesshould help future GWAS that combine association evidence from related phenotypes.By applying these approaches wehighlightthree loci that warrant further investigation. We found that SNPs conveying risk for schizophrenia are also predictive of disease status in our data.
Original languageEnglish
Pages (from-to)386-397
Number of pages12
JournalBiological psychiatry
Issue number5
Publication statusPublished - 1 Mar 2014


  • Acknowledged-BRC
  • Acknowledged-BRC-13/14


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