Progress in understanding the genetic and neurobiological basis of neuropsychiatric disorders has come from both human studies and animal model studies. Until recently, the lack of concerted integration between the two approaches had been hindering the pace of discovery, or more exactly constituted a missed opportunity to accelerate our understanding of this complex and heterogeneous group of disorders. Our group has helped overcome this “lost in translation” barrier by developing an approach called Convergent Functional Genomics (CFG). The approach integrates animal model gene expression data with human genetic linkage/association data, as well as human tissue (postmortem brain, blood) data. This Bayesian strategy for cross-validating findings extracts meaning from large datasets, and prioritizes candidate genes, pathways and mechanisms for subsequent targeted, hypothesis-driven research. The CFG approach may also be particularly useful for identification of blood biomarkers of the illness. We have applied this approach to bipolar and related disorders, and more recently in collaborative studies of alcoholism, schizophrenia, and anxiety disorders. The candidate genes identified by CFG are pursued at two levels: 1) genetic association studies of functional polymorphisms in families and patients, and 2) studies in transgenic mice. Their work so far has pointed to, among other things, an overlap between mood and pain, mood and appetite regulating mechanisms, and between mood and circadian clock regulating genes. This work has resulted in 3 papers over the last 2 years, and one in preparation.
