Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder.
BACKGROUND
Comorbidity of psychiatric and substance use disorders represents a significant complication in the clinical course of both disorders. Bipolar Disorder (BD) is a psychiatric disorder characterized by severe mood swings, ranging from mania to depression, and up to a 70% rate of comorbid Tobacco Use Disorder (TUD). We found epidemiological evidence consistent with a common underlying etiology for BD and TUD, as well as evidence of both genetic and environmental influences on BD and TUD. Therefore, we hypothesized a common underlying genetic etiology, interacting with nicotine exposure, influencing susceptibility to both BD and TUD.


METHODS
Using meta-analysis, we compared TUD rates for BD patients and the general population. We identified candidate genes showing statistically significant, replicated, evidence of association with both BD and TUD. We assessed commonality among these candidate genes and hypothesized broader, multi-gene network influences on the comorbidity. Using Fisher Exact tests we tested our hypothesized genetic networks for association with the comorbidity, then compared the inferences drawn with those derived from the commonality assessment. Finally, we prioritized candidate SNPs for validation.


RESULTS
We estimate risk for TUD among BD patients at 2.4 times that of the general population. We found three candidate genes associated with both BD and TUD (COMT, SLC6A3, and SLC6A4) and commonality analysis suggests that these genes interact in predisposing psychiatric and substance use disorders. We identified a 69 gene network that influences neurotransmitter signaling and shows significant over-representation of genes associated with BD and TUD, as well as genes differentially expressed with exposure to tobacco smoke. Twenty four of these genes are known drug targets.


CONCLUSIONS
This work highlights novel bioinformatics resources and demonstrates the effectiveness of using an integrated bioinformatics approach to improve our understanding of complex disease etiology. We illustrate the development and testing of hypotheses for a comorbidity predisposed by both genetic and environmental influences. Consistent with our hypothesis, the selected network models multiple interacting genetic influences on comorbid BD with TUD, as well as the environmental influence of nicotine. This network nominates candidate genes for validation and drug testing, and we offer a panel of SNPs prioritized for follow-up.
McEachin
Richard C
RC
Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA. mceachin@umich.edu
Saccone
Nancy L
NL
Saccone
Scott F
SF
Kleyman-Smith
Yelena D
YD
Kar
Tiara
T
Kare
Rajesh K
RK
Ade
Alex S
AS
Sartor
Maureen A
MA
Cavalcoli
James D
JD
McInnis
Melvin G
MG
D001714:Bipolar Disorder / Q000453:epidemiology* / Q000235:genetics*; D002394:Catechol O-Methyltransferase / Q000235:genetics; D015897:Comorbidity; D019295:Computational Biology; D050483:Dopamine Plasma Membrane Transport Proteins / Q000235:genetics; D004777:Environment; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D018377:Neurotransmitter Agents / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors; D050486:Serotonin Plasma Membrane Transport Proteins / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics; D014029:Tobacco Use Disorder / Q000453:epidemiology* / Q000235:genetics*
D050483:Dopamine Plasma Membrane Transport Proteins; D018377:Neurotransmitter Agents; C493214:SLC6A3 protein, human; C493230:SLC6A4 protein, human; D050486:Serotonin Plasma Membrane Transport Proteins; D002394:Catechol O-Methyltransferase