Convergent Functional Genomics
Convergent Functional Genomics (CFG)
Developed by Alexander Niculescu, MD, PhD, and collaborators starting in 1999, it is an approach for identifying and prioritizing candidate genes  and biomarkers  for complex psychiatric and medical disorders by integrating and tabulating multiple lines of evidence- gene expression and genetic data, from human studies and animal model work. Developed independently but conceptually analogous to Google PageRank. The more lines of evidence for a gene (links), the higher it comes up on the CFG prioritization list. CFG represents a fit-to-disease approach, that extracts and prioritizes in a Bayesian fashion biologically-relevant signal even from limited size studies. That signal is predictive and is reproducible in independent studies, as opposed to the fit-to-cohort aspect of classic human genetic studies like Genome-wide association study (GWAS), where the issue of genetic heterogeneity makes the top statistically significant findings from even large size studies less reproducible in independent studies.
- Niculescu, A.B., 3rd et al. Identifying a series of candidate genes for mania and psychosis: a convergent functional genomics approach. Physiol Genomics 4, 83-91 (2000).
- Ogden, C.A. et al. Candidate genes, pathways and mechanisms for bipolar (manic-depressive) and related disorders: an expanded convergent functional genomics approach. Mol Psychiatry 9, 1007-29 (2004).
- Le-Niculescu, H. et al. Towards understanding the schizophrenia code: An expanded convergent functional genomics approach. Am J Med Genet B Neuropsychiatr Genet 144, 129-58 (2007).
- Rodd, Z.A. et al. Candidate genes, pathways and mechanisms for alcoholism: an expanded convergent functional genomics approach. Pharmacogenomics J 7, 222-56 (2007).
- Le-Niculescu, H. et al. Convergent functional genomics of genome-wide association data for bipolar disorder: comprehensive identification of candidate genes, pathways and mechanisms. Am J Med Genet B Neuropsychiatr Genet 150B, 155-81 (2009).
- Patel, S.D. et al. Coming to grips with complex disorders: genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics. Am J Med Genet B Neuropsychiatr Genet 153B, 850-77.
- Le-Niculescu, H. et al. Identifying blood biomarkers for mood disorders using convergent functional genomics. Mol Psychiatry 14, 156-74 (2009).
- Kurian, S.M. et al. Identification of blood biomarkers for psychosis using convergent functional genomics. Mol Psychiatry (2009).
- Bertsch, B. et al. Convergent functional genomics: a Bayesian candidate gene identification approach for complex disorders. Methods 37, 274-9 (2005).
- Niculescu, A.B. & Le-Niculescu, H. Convergent Functional Genomics: what we have learned and can learn about genes, pathways, and mechanisms. Neuropsychopharmacology 35, 355-6.
- Niculescu, A.B. & Le-Niculescu, H. The P-value illusion: how to improve (psychiatric) genetic studies. Am J Med Genet B Neuropsychiatr Genet 153B, 847-9.