Convergent functional genomics: a Bayesian candidate gene identification approach for complex disorders

Abstract

Identifying genes involved in complex neuropsychiatric disorders through classic human genetic approaches has proven difficult. To overcome that barrier, we have developed a translational approach called Convergent Functional Genomics (CFG), which cross-matches animal model microarray gene expression data with human genetic linkage data as well as human postmortem brain data and biological role data, as a Bayesian way of cross-validating findings and reducing uncertainty. Our approach produces a short list of high probability candidate genes out of the hundreds of genes changed in microarray datasets and the hundreds of genes present in a linkage peak chromosomal area. These genes can then be prioritized, pursued, and validated in an individual fashion using: (1) human candidate gene association studies and (2) cell culture and mouse transgenic models. Further bioinformatics analysis of groups of genes identified through CFG leads to insights into pathways and mechanisms that may be involved in the pathophysiology of the illness studied. This simple but powerful approach is likely generalizable to other complex, non-neuropsychiatric disorders, for which good animal models, as well as good human genetic linkage datasets and human target tissue gene expression datasets exist.