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]
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]