LAUSR

Background The aetiology of Bipolar Disorder (BD) and Schizophrenia (SZ) involves a complex gene-environment interplay and this involves common risk alleles that are shared across the disorders. Bipolar disorder is heterogeneous in presentation and we and others have postulated that the particular clinical presentation is in part determined by the particular risk alleles carried. To investigate this, we have employed sparse Canonical Correlation Analysis (sCCA), to seek patterns of correlation between psychotic symptoms and schizophrenia risk alleles in a large (N=3903) deeply phenotyped sample of subjects with BD. Methods Canonical Correlation Analysis (CCA) seeks linear relationships between two sets of multi-dimensional variables. In the psychiatric genetics field, this approach can be adapted to enable the analysis of both phenotype and genetic variables in one framework. This has the advantage of minimizing the number of statistical tests required. A recent extension of CCA to sparse canonical correlation analysis (sCCA) allows application of the general approach to high dimensional genomic data (Witten et al., 2009). sCCA has advantages compared to CCA in terms of precision of parameter estimates and statistical power. We employed sCCA to analyse 30 psychotic symptoms assessed with the OPerational CRITeria checklist (OPCRIT) and 82 independent genome-wide significant SNPs that were identified by the SZ PGC2 study (The Psychiatric Genomics Consortium, 2014) for which we had data in our BD sample. We applied the sCCA as a purely data driven approach to individual OPCRIT psychotic symptoms and also to groups of OPCRIT psychotic symptoms (“positive”, “negative”, “disorganised”) defined by a previously published factor analysis study of schizophrenia. Results sCCA analysis of the individual OPCRIT items with the set of significantly associated with SZ 82 sCCA analysis based on individual psychotic symptoms revealed a significant association (p=0.045) with the largest weights attributed to delusions of influence, bizarre behaviour and grandiose delusions and an indel SNP on chromosome 3 (3:180594593, build 37). sCCA analysis on the same set of SNPs and OPCRIT symptom groups confirmed association with the same SNP and the “disorganised” group of OPCRIT symptoms (p=0.01). The results also show significant association when we use less stringent p-value thresholds in SZ. In this case the most heavily weighted SNP remains 3:180594593, but other SNPs also contribute to the analysis. Discussion Our results suggest the CCA canonical correlation approach might be a useful tool to explore phenotype-genotype relationships. It can be applied to generate data-driven hypotheses with the potential to provide further insights into the complex architecture of psychiatric disorders. To the best of our knowledge, this is the first study to apply this approach in analysing phenotypes and genotypes together. sCCA offers the potential to be extended to include a larger number of fine grained systematic descriptors of BD, and for testing for correlation with genetic profiles from other co-morbid disorders.