Human Genetics

, 125:63 | Cite as

Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission

  • Kathleen Askland
  • Cynthia Read
  • Jason Moore
Original Investigation


Despite known heritability, the complex genetic architecture of bipolar disorder (likely including trait, locus and allelic heterogeneity, as well as genetic interactions) has confounded genetic discovery for many years. Even modern day whole genome association studies (WGAS) using over half a million common SNPs have implicated only a handful of genes at the genomewide level. Temporally coincident with this series of WGAS, a host of pathways-based analyses (PBAs) have emerged as novel computational approaches in the examination of large-scale datasets, but thus far rarely have been applied to WGAS data in psychiatric disorders. Here, we report a series of PBAs conducted using exploratory visual analysis, an analytic and visualization software tool for examining genomic data, to examine results from the National Institutes of Mental Health and Wellcome-Trust Case Control Consortium WGAS in bipolar disorder. Consistent with a host of prior linkage findings, some candidate gene association studies, and recent WGAS, our strongest findings suggest involvement of ion channel structural and regulatory genes, including voltage-gated ion channels and the broader ion channel group that comprises both voltage- and ligand-gated channels. Moreover, we found only modest overlap in the particular genes driving the significance of these gene sets across the analyses. This observation strongly suggests that variation in ion channel genes, as a class of genes, may contribute to the susceptibility of bipolar disorder and that heterogeneity may figure prominently in the genetic architecture of this susceptibility.


Bipolar Disorder Wellcome Trust Case Control Consortium Candidate Gene Association Study Whole Genome Association Study GenMAPP Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This manuscript is, in part, the product of preliminary analyses for an NIMH K08 application (K08 MH085810-01), currently under review. Additionally, the analyses in this paper would not have been possible without the primary WGAS data collection work of The Wellcome Trust Case Control Consortium and the Sklar et al. research groups. The authors would like to acknowledge Peter Andrews, B.S., EVA software engineer in the Computational Genetics Lab Dartmouth Medical School for his invaluable assistance in our analysis. We also acknowledge Drs. Benjamin Greenberg and Steven Rasmussen from The Warren Alpert School of Medicine at Brown University for their editorial reviews and suggestions. Finally, we thank the anonymous reviewers for their incisive and valuable reviews of our manuscript.

Supplementary material

439_2008_600_MOESM1_ESM.doc (274 kb)
Supplementary Material (DOC 274 kb)


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© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Psychiatry and Human BehaviorWarren Alpert Medical School of Brown University, Butler HospitalProvidenceUSA
  2. 2.OCD Research GroupButler HospitalProvidenceUSA
  3. 3.Department of GeneticsDartmouth Medical SchoolHanoverUSA

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