IDENTIF YING UNANTICIPATED GENES AND MECHANISMS IN SERIOUS MENTAL ILLNESS

Genome-wide association studies (GWAS) investigate the relationship or association between genetic markers, usually single-nucleotide polymorphisms (SNPs), along the entire genome with categorical (e.g., diagnoses) or quantitative traits (e.g., imaging phenotypes). In contrast, candidate gene association studies investigate the relationship between genetic markers at a particular locus (e.g., a gene hypothesized to have biological relevance for a particular disorder) and either categorical or quantitative traits. The GWAS approach has an advantage compared to the candidate gene approach because it does not require a priori knowledge of a trait’s etiological connection, or its function singly or as a part of a network (Potkin et al. 2009a; Stranger, Stahl, and Raj 2011); thus this approach can lead to unanticipated discoveries of genes associated with mental illness. While the advantages of this approach apply to mental illness in general, we will confine our examples to schizophrenia and bipolar subjects, as several SNPs are risk factors for the development of both disorders and for some of their shared clinical characteristics. We would prefer to include only relevant studies with both bipolar and schizophrenia subjects; however, given that there are very few such studies, we will include studies with either disorder. Why combine genetic and imaging data? Mental disorders are brain disorders that can be studied with functional brain imaging measures, which are known to be highly heritable. Studying brain imaging measures in neuropsychiatry without considering genetics neglects part of the risk for developing mental disorders such as schizophrenia and bipolar disorder. Similarly, studying genetics in psychiatric disorders without determining their brain effects fails to understand (at least part of) the consequences of these genetic influences. The rationale for considering both schizophrenia and bipolar disorder is due to their overlap in clinical symptoms, medication response, and risk genes (Tesli et al. 2014), although we are aware that the two disorders also have significant differences in these domains. The approach to jointly study multiple psychiatric syndromes is in accord with the National Institute of Mental Health’s Research Domain Criteria (RDoC) approach of examining possible common mechanisms across disorders. The Psychiatric Genomics Consortium (PGC) cross-diagnosis working group has identified six candidate regions that are shared risk factors for both schizophrenia and bipolar disorders (Ruderfer et al. 2014). However, these data to date lack a functional phenotype that is likely to be critical in understanding the candidate regions’ role in these disorders. In this chapter we selectively review studies that combine both genome-wide genetic and functional imaging data in schizophrenia and/or bipolar disorder. An advantage of the GWAS approach is that it surveys the entire genome for associations between genetic markers and phenotypes (e.g., diagnosis, imaging, and/or clinical characteristics). Adequate SNP coverage is important, and in current studies at least 750,000 SNPs proportionately distributed across the genome are needed for detailed association studies. With imputation of SNPs absent in early SNP chips used for genotyping, this has become a realistic goal. Rich multimodal quantitative imaging phenotype data offer the potential of linking genetic risk with functional brain consequences and have several additional advantages over case-control studies that will be discussed OUP UNCORRECTED PROOF – FIRSTPROOFS, Mon Aug 1