The second line of my research focuses on clarifying the nosologic boundaries and etiologic overlap among mental disorders. Psychiatry is the only medical field that defines disorders based on descriptive symptom profiles instead of pathologic evidence. Thus, to make categorical distinctions between ‘normal’ and ‘disordered’ with respect to mental functioning, arbitrary decisions are inevitable. Furthermore, there is obvious overlap of clinical symptoms across various psychiatric and neurodevelopmental disorders, making it challenging to draw diagnostic boundaries. There is a need to re-conceptualize the nosology of mental disorders that reflects underlying neurobiologic bases.
Working towards this goal, I developed a log-linear modeling-based testing strategy specifically designed for evaluating shared genetic risk effects in cross disorder GWAS (Lee et al., 2011). Using this method, I performed cross disorder modeling analyses reported in the PGC Cross Disorder Group (CDG) study (Lancet 2013). Using the genome-wide SNP data from 33,332 cases and 27,888 controls, primary meta-analysis identified four genomic loci supported by significant risk effects to a range of psychiatric disorders, particularly autism, ADHD, bipolar disorder, major depressive disorder, and schizophrenia. I applied my modeling method to predict specific pleiotropic disorder models for top GWAS loci underlying core psychopathology. In this work, I also demonstrated: (1) a shared etiologic role of calcium signaling pathway genes (as noted above) and (2) broad implication of altered brain gene expression in mental disorders using post-mortem brain eQTL (expression Quantitative Trait Loci) data.
As a lead analytic member of the PGC-CDG, my research in this area will focus on cross disorder analyses of nine psychiatric disorders currently studied at the PGC. This PGC2 cross-disorder study expands the prior PGC1-CDG work in a major way. It allows us to study a much broader spectrum of psychopathology with the statistical power of genetic data that is unparalleled in psychiatric genomics research. As of now, I have collected genome-wide association data from 239,551 individuals of 151 PGC study cohorts for this analysis (total of 91,234 cases and 148,317 controls). The final data-freeze of this multi-year international effort is planned in early 2016.