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Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach.

ABSTRACT: Comorbidity of Major Depressive Disorder (depression) and Alcohol Use Disorders (AUD) is well documented. Depression, AUD, and the comorbidity of depression with AUD show evidence of genetic and environmental influences on susceptibility. We used an integrated bioinformatics approach, mining available data in multiple databases, to develop and refine a model of gene-by-environment interaction consistent with this comorbidity.We established the validity of a genetic model via queries against NCBI databases, identifying and validating TNF (Tumor Necrosis Factor) and MTHFR (Methylenetetrahydrofolate Reductase) as candidate genes. We used the PDG-ACE algorithm (Prioritizing Disease Genes by Analysis of Common Elements) to show that TNF and MTHFR share significant commonality and that this commonality is consistent with a response to environmental exposure to ethanol. Finally, we used MetaCore from GeneGo, Inc. to model a gene-by-environment interaction consistent with the data.TNF Alpha Converting Enzyme (TACE) activity is suppressed by ethanol exposure, resulting in reduced TNF signaling. TNF binds to TNF receptors, initiating signal transduction pathways that activate MTHFR expression. MTHFR is an essential enzyme in folate metabolism and reduced folate levels are associated with both AUD and depression. Integrating these pieces of information our model shows how excessive alcohol use would be expected to lead to reduced TNF signaling, reduced MTHFR expression, and increased susceptibility to depression.The proposed model provides a novel hypothesis on the genetic etiology of comorbid depression with AUD, consistent with established clinical and biochemical data. This analysis also provides an example of how an integrated bioinformatics approach can maximize the use of available biomedical data to improve our understanding of complex disease.

SUBMITTER: McEachin RC

PROVIDER: S-EPMC2547861 | biostudies-other | 2008 Jul

REPOSITORIES: biostudies-other

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Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach.

McEachin Richard C RC Keller Benjamin J BJ Saunders Erika F H EF McInnis Melvin G MG

BioData mining 20080717 1

<h4>Background</h4>Comorbidity of Major Depressive Disorder (depression) and Alcohol Use Disorders (AUD) is well documented. Depression, AUD, and the comorbidity of depression with AUD show evidence of genetic and environmental influences on susceptibility. We used an integrated bioinformatics approach, mining available data in multiple databases, to develop and refine a model of gene-by-environment interaction consistent with this comorbidity.<h4>Methods</h4>We established the validity of a gen ...[more]

PMID: 18822146

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Project description:ImportanceAlcohol dependence (AD) and major depression (MD) are leading causes of disability that often co-occur. Genetic epidemiologic data have shown that AD and MD share a common possible genetic cause. The molecular nature of this shared genetic basis is poorly understood.ObjectivesTo detect genetic risk variants for comorbid AD and MD and to determine whether polygenic risk alleles are shared with neuropsychiatric traits or subcortical brain volumes.Design, setting, and participantsThis genome-wide association study analyzed criterion counts of comorbid AD and MD in African American and European American data sets collected as part of the Yale-Penn study of the genetics of drug and alcohol dependence from February 14, 1999, to January 13, 2015. After excluding participants never exposed to alcohol or with missing information for any diagnostic criterion, genome-wide association studies were performed on 2 samples (the Yale-Penn 1 and Yale-Penn 2 samples) totaling 4653 African American participants and 3169 European American participants (analyzed separately). Tests were performed to determine whether polygenic risk scores derived from potentially related traits in European American participants could be used to estimate comorbid AD and MD.Main outcomes and measuresComorbid criterion counts (ranging from 0 to 14) for AD (7 criteria) and MD (9 criteria, scaled to 7) as defined by the DSM-IV.ResultsOf the 7822 participants (3342 women and 4480 men; mean [SD] age, 40.1 [10.7] years), the median comorbid criterion count was 6.2 (interquartile range, 2.3-10.9). Under the linear regression model, rs139438618 at the semaphorin 3A (SEMA3A [OMIM 603961]) locus was significantly associated with AD and MD comorbidity in African American participants in the Yale-Penn 1 sample (β = 0.89; 95% CI, 0.57-1.20; P = 2.76 × 10-8). In the independent Yale-Penn 2 sample, the association was also significant (β = 0.83; 95% CI, 0.39-1.28; P = 2.06 × 10-4). Meta-analysis of the 2 samples yielded a more robust association (β = 0.87; 95% CI, 0.61-1.12; P = 2.41 × 10-11). There was no significant association identified in European American participants. Analyses of polygenic risk scores showed that individuals with a higher risk of neuroticism (β = 1.01; 95% CI, 0.50-1.52) or depressive symptoms (β = 0.87; 95% CI, 0.32-1.42) and a lower level of subjective well-being (β = -0.94; 95% CI, -1.46 to -0.42) and educational attainment (β = -1.00, 95% CI, -1.57 to -0.44) had a higher level of AD and MD comorbidity, while larger intracranial (β = 1.07; 95% CI, 0.50 to 1.64) and smaller putamen volumes (β = -1.16; 95% CI, -1.86 to -0.46) were associated with higher risks of AD and MD comorbidity.Conclusions and relevanceSEMA3A variation is significantly and replicably associated with comorbid AD and MD in African American participants. Analyses of polygenic risk scores identified pleiotropy with neuropsychiatric traits and brain volumes. Further studies are warranted to understand the biological and genetic mechanisms of this comorbidity, which could facilitate development of medications and other treatments for comorbid AD and MD.

Project description:BackgroundPsychiatric comorbidities may complicate depression treatment by being associated with increased role impairments. However, depression symptom severity might account for these associations. Understanding the independent associations of depression severity and comorbidity with impairments could help in treatment planning. This is especially true for depressed Veterans, who have high psychiatric comorbidity rates.Methods2,610 Veterans beginning major depression treatment at the Veterans Health Administration (VHA) were administered a baseline self-report survey that screened for diverse psychiatric comorbidities and assessed depression severity and role impairments. Logistic and generalized linear regression models estimated univariable and multivariable associations of depression severity and comorbidities with impairments. Population attributable risk proportions (PARPs) estimated the relative importance of depression severity and comorbidities in accounting for role impairments.ResultsNearly all patients (97.8%) screened positive for at least one comorbidity and half (49.8%) for 4+ comorbidities. The most common positive screens were for generalized anxiety disorder (80.2%), posttraumatic stress disorder (77.9%), and panic/phobia (77.4%). Depression severity and comorbidities were significantly and additively associated with impairments in multivariable models. Associations were attenuated much less for depression severity than for comorbidities in multivariable versus univariable models. PARPs indicated that 15-60% of role impairments were attributable to depression severity and 5-32% to comorbidities.LimitationsThe screening scales could have over-estimated comorbidity prevalence. The cross-sectional observational design cannot determine either temporal or causal priorities.ConclusionsAlthough positive screens for psychiatric comorbidity are pervasive among depressed VHA patients, depression severity accounts for most of the associations of these comorbidities with role impairments.

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Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach.

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Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach.

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