ABN is a scientific co-founder of Mindscape Diagnostics.
Article first published online: 9 APR 2010
Copyright © 2010 Wiley-Liss, Inc.
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics
Volume 153B, Issue 4, pages 850–877, June 2010
How to Cite
Patel, S.D., Le-Niculescu, H., Koller, D.L., Green, S.D., Lahiri, D.K., McMahon, F.J., Nurnberger, J.I. and Niculescu, A.B. (2010), 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., 153B: 850–877. doi: 10.1002/ajmg.b.31087
How to Cite this Article: Patel SD, Le-Niculescu H, Koller DL, Green SD, Lahiri DK, McMahon F, Nurnberger JI, Niculescu AB. 2010. 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 Part B 153B: 850–877.
- Issue published online: 17 MAY 2010
- Article first published online: 9 APR 2010
- Manuscript Accepted: 19 FEB 2010
- Manuscript Received: 26 JAN 2010
- convergent functional genomics;
- genetic risk;
- bipolar disorder
We previously proposed and provided proof of principle for the use of a complementary approach, convergent functional genomics (CFG), combining gene expression and genetic data, from human and animal model studies, as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach [Le-Niculescu et al., 2009b]. CFG provides a fit-to-disease prioritization of genes that leads to generalizability in independent cohorts, and counterbalances the fit-to-cohort prioritization inherent in classic genetic-only 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-up as direct interactors (intra-pathway epistasis, INPEP). Moreover, we have put together a panel of best P-value 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-American and African-American 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. © 2010 Wiley-Liss, Inc.