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CFG provides a fit\u2010to\u2010disease prioritization of genes that leads to generalizability in independent cohorts, and counterbalances the fit\u2010to\u2010cohort prioritization inherent in classic genetic\u2010only approaches, which have been plagued by poor reproducibility across cohorts. We have now extended our previous work to include more datasets of GWAS, and more recent evidence from other lines of work. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder. Biological pathway analyses identified top canonical pathways, and epistatic interaction testing inside these pathways has identified genes that merit future follow\u2010up as direct interactors (intra\u2010pathway epistasis, INPEP). Moreover, we have put together a panel of best <jats:italic>P<\/jats:italic>\u2010value single nucleotide polymorphisms (SNPs), based on the top candidate genes we identified. We have developed a genetic risk prediction score (GRPS) based on our panel, and demonstrate how in two independent test cohorts the GRPS differentiates between subjects with bipolar disorder and normal controls, in both European\u2010American and African\u2010American populations. Lastly, we describe a prototype of how such testing could be used to categorize disease risk in individuals and aid personalized medicine approaches, in psychiatry and beyond. \u00a9 2010 Wiley\u2010Liss, Inc.<\/jats:p>","DOI":"10.1002\/ajmg.b.31087","type":"journal-article","created":{"date-parts":[[2010,4,12]],"date-time":"2010-04-12T17:49:39Z","timestamp":1271094579000},"page":"850-877","source":"Crossref","is-referenced-by-count":56,"title":["Coming to grips with complex disorders: Genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional 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