Convergent Functional Genomics
Using animal-model pharmacogenomics as a "Rosetta Stone" for breaking the genetic code of major neuropsychiatric disorders: An Introduction
Progress in understanding the genetic and neurobiological basis of neuropsychiatric disorders is derived from both human studies and animal-model studies. Until recently, the lack of concerted integration between the two approaches was hindering the pace of research. This same lack of integration hindered opportunities to accelerate our understanding of this complex and heterogeneous group of disorders.
Our research group has helped to overcome this "lost in translation" barrier by developing an approach called Convergent Functional Genomics (CFG). The approach integrates data from animal-model gene expression studies with data from human genetic linkage/association studies, as well as data obtained from human tissue (postmortem brain, blood).
This Bayesian strategy for cross-validating findings extracts meaning from large datasets by prioritizing candidate genes, pathways, and mechanisms; which are then targeted by subsequent hypothesis-driven research. The CFG approach may be particularly useful for identification of blood biomarkers of mental disorders, such as bipolar and related disorders. More recently, this CFG has been utilized in collaborative studies of alcoholism, schizophrenia, and anxiety disorders.
Candidate genes identified by CFG are investigated at two levels: 1) genetic association studies of functional polymorphisms in families and patients, and; 2) studies in transgenic mice.
Research thus far has pointed to - amongst other things - an overlap between: mood and pain; mood and appetite regulating mechanisms; mood and circadian-clock-regulating genes.