Convergent Functional Genomics
“The increasing volume of information is just more opportunity to build better answers to questions. The more information you have, the better”- Larry Page, Google
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 and/or was a missed opportunity 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. Top candidate genes, for which there are multiple independent lines of evidence, are less likely to be false positives. The network of lines of evidence for each gene is resilient, even if one or another of the nodes (lines of evidence) is less than optimal. In the end, the results speak for themselves in terms of the ability of our Convergent Functional Genomics approach to extract signal and prioritize findings, similar to a Google PageRank algorithm , from large and potentially noisy datasets. The CFG approach has been successfully applied to bipolar disorder, schizophrenia, alcoholism, and blood biomarker discovery.
Convergent Function Genomics in Action
Recently, the CFG approach was used to integrate in a comprehensive fashion genetic and gene expression evidence for bipolar disorder, resulting in the most complete model of bipolar disorder pathophysiology to date (see below).
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H. Le-Niculescu, S.D. Patel, M. Bhat, R. Kuczenski, S.V. Faraone, M.T. Tsuang, F. McMahon, N. J. Schork , J.I. Nurnberger Jr., A. B. Niculescu. Convergent Functional Genomics of Genome-Wide Association Data for Bipolar Disorder: Comprehensive Identification of Candidate Genes, Pathways and Mechanisms. American Journal of Medical Genetics Part B (Neuropsychiatric Genetics). 2009 Mar 5 150B(2):155-181. Link to article PDF file (842 KB) |
Convergent Functional Genomics Reveals the Architecture of Psychiatric Disorders
Cumulative Combinatorics of Common Gene Variants and Environment (CC x CG x E) Model for Bipolar and other Complex Disorders(see below).
