Home

Selected Publications

Firsts by our lab

Media

Recent Presentations

Convergent Functional Genomics

Animal Models

PhenoChipping

3D Mindscape

Current Research

Pyramids

Pathways and Mechanisms

FAQ

Convergent studies by other groups

Lab Members

Participate in Our Studies
(PDF file)

SASS: Simplified Affective State Scale
(PDF file)

CFI-S: Convergent Functional Information for Suicide Scale
(PDF file)
(Instructions)

Semantic Web Primer (PDF file)

INBRAIN

On Our Minds
(Blog)

Citations and reproducibility

"Life and Mind"
Book

Interesting Links

Photo Album

Contact Us

Gifts and Giving

You are visitor number 619633.

Candidate Genes, Pathways, and Mechanisms for Bipolar (Manic-Depressive) and Related Disorders Identified Through a Comprehensive Convergent Functional Genomics Approach

Cory Ogden 1,2,3, Michael Rich 1,2,3, Nicholas Schork 2, Martin Paulus 2,3, Mark Geyer 2,3, James B. Lohr 2,3, Ronald Kuczenski 2,3, and Alexander B. Niculescu 1,2,3*.

1Laboratory of Neurophenomics; 2Department of Psychiatry, UC San Diego; 3VA San Diego Healthcare System VISN-22 MIRECC


Abstract

Identifying genes for bipolar mood disorders through classic genetics has proven difficult. Here we present a comprehensive convergent approach that translationally integrates brain gene expression data from a relevant pharmacological mouse model with human data (linkage loci from human genetic studies, changes in post-mortem brains from patients), as a Bayesian strategy of cross-validating findings.

Topping the list of candidate genes we have DARPP-32 (dopamine- and cAMP- regulated phosphoprotein of 32 kilodaltons, PENK (preproenkephalin), and TAC1 (tachykinin 1, substance P). These data suggest that more primitive molecular mechanisms involved in pleasure and pain may have been recruited by evolution to play a role in higher mental functions such as mood. The analysis also revealed other high probability candidates genes (neurogenesis, neurotrophic, neurotransmitter, signal transduction, circadian, synaptic, and myelin related), pathways and mechanisms of likely importance in pathophysiology.


Tables


Figures


Genechip Microarray Data