Convergent Functional Genomics (CFG)

Convergent Functional Genomics (CFG)

Developed by Alexander Niculescu, MD, PhD, and collaborators starting in 1999 ^[1], it is an approach for identifying and prioritizing candidate genes ^{[2][3][4][5][6]} and biomarkers ^[7][8] 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 ^[9][10]. 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 ^[5][6][7][8], 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 ^[11].

References

1. ^ 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).
2. ^ 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).
3. ^ 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).
4. ^ Rodd, Z.A. et al. Candidate genes, pathways and mechanisms for alcoholism: an expanded convergent functional genomics approach. Pharmacogenomics J 7, 222-56 (2007).
5. ^ ^{a b} 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).
6. ^ ^{a b} 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.
7. ^ ^{a b} Le-Niculescu, H. et al. Identifying blood biomarkers for mood disorders using convergent functional genomics. Mol Psychiatry 14, 156-74 (2009).
8. ^ ^{a b} Kurian, S.M. et al. Identification of blood biomarkers for psychosis using convergent functional genomics. Mol Psychiatry (2009).
9. ^ Bertsch, B. et al. Convergent functional genomics: a Bayesian candidate gene identification approach for complex disorders. Methods 37, 274-9 (2005).
10. ^ 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.
11. ^ 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.

External links

• Laboratory of Neurophenomics