Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach
© McEachin et al; licensee BioMed Central Ltd. 2008
Received: 22 January 2008
Accepted: 17 July 2008
Published: 17 July 2008
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.
Comorbidity of Major Depressive Disorder (depression) with Alcohol Use Disorders (AUD) is well documented . Further, significant statistical association between depression and alcohol dependence has long been established . For depressed individuals, the odds ratio for alcohol dependence may be as high 4 to 7, relative to controls, with a similar increase in prevalence of depression among alcohol dependent individuals [3, 4]. This association between depression and AUD suggests that there may be one or more common underlying elements influencing susceptibility to both disorders.
Depression, AUD, and the comorbidity of depression with AUD are complex traits, demonstrating both genetic and environmental influences on susceptibility. Depression is familial, with estimates of heritability as high as 54% . AUD is also familial, with heritability estimates in the range of 50 to 60% . Comorbidity of depression with AUD also shows evidence of both genetic and shared environmental influences [7–10]. Given evidence of association between depression and AUD, as well as evidence that both the individual traits and the comorbidity are influenced by both genetic and environmental factors, we used an integrated bioinformatics approach, maximizing the use of available data in multiple databases, to investigate the hypothesis that interacting genetic and environmental influences are common underlying elements of susceptibility to comorbid depression with AUD. We report the development and testing of a model of this comorbidity, as well as implications for future research into comorbid depression with AUD.
Queries against NCBI Databases
Comparison of Whole Genome Association (WGA) to text based, Entrez Gene, search for candidate genes.
Identifying Candidate Genes
Text Based Search
Genotyping and Phenotyping
Current Availability of Data
Free (Taxpayer Subsidized)
Low; limited number of markers tested
Low; much remains unknown about most genes
Positive Predictive Value
Low; many false positives
High; curated information is backed by experimental data and peer review
Generally no, although population specific information may be included in some records
Specific Variants Identified
Yes, although SNPs that tag haplotypes may not be disease causing variants
Generally no, although specific variants may be annotated. Separate genotyping is usually required for validated candidates
Multi-Gene Effects on Phenotype
PubMed queries and citation counts
Short form, PubMed Queries
PubMed Citation Count
depression AND ethanol
depression AND MTHFR
ethanol AND MTHFR
depression AND ethanol AND MTHFR
depression AND TNF
ethanol AND TNF
depression AND ethanol AND TNF
depression AND NFkB
ethanol AND NFkB
depression AND ethanol AND NFkB
depression AND TNFR
ethanol AND TNFR
depression AND ethanol AND TNFR
depression and APOE
ethanol and APOE
depression AND ethanol AND APOE
There are, however, limitations to the use of MeSH annotation in querying publications. First, while MeSH contains thousands of Medical Subject Headings, it is not an exhaustive annotation of all potential biomedical contexts. Second, there may be missing annotation or ambiguity in the annotation for some publications. For example, as mentioned in the PPV analysis, not all of the 171 papers that we counted as True Positives were specifically about comorbid AUD with depression. The earlier papers tended to refer to AUD and depression as co-occurring phenotypes, while the later papers focused on evaluating the comorbidity. There also may be ambiguity in the user's choice of MeSH terms queried, and some publications may report negative associations, though the bias against publication of negative results minimizes the number of these papers. And finally, a lack of citations is not evidence of a lack of association. While co-occurrence of queried terms is not proof of association, co-occurrence does provide a quantitative measure of peer reviewed research on the association of these specific terms and complements other forms of data that can be mined. In future analyses, Natural Language Processing will help to improve these queries by mining the full text of publications for a wide variety of concepts, and provide additional evidence of association among terms. Given believable candidate genes, derived from queries of available data at Entrez Gene and PubMed, we continued the analysis using DAVID, PDG-ACE, and MetaCore (GeneGo, Inc.) to understand how these candidate genes may be related to the etiology of AUD, depression, and comorbid AUD with depression.
DAVID, PDG-ACE, and GeneGo
DAVID software, the PDG-ACE algorithm, and the GeneGo suite perform related tasks. The essential assumption in the use of all three tools is that, in complex diseases, multiple genetic influences converge on a single phenotype. The three tools use different approaches to identify how multiple genes may influence the phenotype of interest and assess the statistical significance of the results seen.
DAVID  uses a database of available gene annotation to overlay candidate genes onto predefined gene sets. For example, genes may be organized into predefined sets based on annotation for their participation in a Gene Ontology function, process, or component . A set of candidate genes is then derived from a genotype/phenotype analysis (e.g. WGA, microarray expression, or text-based database query) and the candidate genes are overlaid on the predefined gene sets. If a large proportion of the candidate genes are found to be annotated for a given gene set (e.g. an over-represented Gene Ontology process), the candidate genes may have an important impact on the process represented by that gene set. Since the candidate genes were initially derived from a genotype/phenotype relationship, the over-represented process may be important in the phenotype .
However, since much gene annotation remains incomplete, PDG-ACE  [see Additional file 2] serves as an adjunct to software that depends on gene annotation. As noted, the Entrez Gene database offers a reliable summary of information on a wide range of genetic and environmental influences for each gene (e.g. gene function, disease influences, localization, environmental effects). However, much of the information available in Entrez Gene is free text, rather than formal annotation, limiting the use of tools like DAVID. We created the PDG-ACE algorithm to overcome this limitation by mining Entrez Gene text to identify biomedical keywords that are common and significantly over-represented in the descriptions of genes at a selected locus pair, relative to all locus pairs in the genome.
The PDG-ACE algorithm uses a controlled vocabulary of biomedical keywords to limit the search toconcepts likely to be useful in understanding disease etiology and show statistically significant over-representation. For a given locus pair, PDG-ACE mines the Entrez Gene text to find keywords from the controlled vocabulary that are common across the locus pair. For keywords that are common across the locus pair, significance of over-representation is established by permutation testing. Keywords that are common and significantly over-represented across the locus pair highlight rare combinations of genes that share the biomedical concepts associated with the keywords. Testing of the PDG-ACE algorithm, using both positive controls (documented gene-gene interactions in complex disease) and negative controls (randomly selected gene pairs), demonstrates that PDG-ACE provides insight into the etiology of true genetic interactions, while excluding loci where the data is insufficient to identify interactions . As with DAVID, the loci are initially defined by the disease phenotype, so the over-represented keywords may offer insight into the nature of a genetic interaction in disease etiology. In applying the PDG-ACE algorithm, we define genetic interaction to mean a "statistically significant multi-gene or multi-gene-by-environment influence on the phenotype", consistent with Hartman et al. . This definition is deliberately broad, allowing PDG-ACE to identify unannotated influences on the phenotype, ranging from well-defined influences (e.g. epistasis, protein-protein binding, canalization, genetic robustness, buffering) to completely novel influences, as long as the influence is multi-gene (or multi-gene-by-environment) and statistically significant. The complete list of keywords used in this analysis is included in Additional file 3. Software implementing the PDG-ACE algorithm is available on request from Dr. Ben Keller, firstname.lastname@example.org.
Parameter settings used in GeneGo analysis
Use canonical pathways
Analyze network (transcription factors)
Discard objects user list
Use indirect interactions
Use interactions weights
Add complementary objects
Filter interactions by confidence level
Use binding interactions (special cases)
Discard objects experiments
Use unspecified reactions
Genetic Influences on Comorbidity
We first searched for evidence of genetic influences on depression and AUD by querying NCBI's Entrez Gene database for "depression AND ethanol AND human [orgn]". This query returned exactly three genes: Tumor Necrosis Factor (TNF superfamily, member 2, GeneID 7124), Methylenetetrahydrofolate Reductase (MTHFR, 5,10-methylenetetrahydrofolate reductase (NADPH), GeneID 4524) [24–29], and APOE (apolipoprotein E, GeneID 348). However, the Gene Ontology annotation for APOE, "response to ethanol", is inferred from electronic annotation, rather than manual curation. In an effort to use only the most reliable information to establish our initial candidate genes, we excluded APOE from the formal analysis. However, we have included a brief analysis of APOE in the results.
We searched PubMed for supporting information on potential roles of the proteins coded by TNF and MTHFR in depression, AUD, and comorbid depression with AUD. In searching PubMed, we found evidence that both TNF and MTHFR have been associated with depression and with ethanol in the literature, but not with the comorbidity of depression with AUD (Table 2). Notably, the result in Table 2 showing that TNF is associated with both depression and ethanol in a single publication is one of the false positive results identified in the PPV analysis. We didn't find evidence of MTHFR being associated with both depression and AUD in any single paper.
In total, the published literature is consistent with the hypothesis that TNF and MTHFR may both influence, or be influenced by, depression and AUD. We consider TNF and MTHFR to be valid candidate genes for both depression and AUD. Given these candidate genes, we next sought evidence that they participate in the comorbidity via genetic interaction.
Interaction between TNF and MTHFR via DAVID and PDG-ACE
Given evidence of genetic influences on depression and AUD, we refined the hypothesis to include interaction between TNF and MTHFR. We first used DAVID software to explore functional annotation consistent with interaction between TNF and MTHFR. DAVID looks for over-representation of candidate genes in signaling or metabolic pathways, Gene Ontology processes, etc., based on available gene annotation. TNF and MTHFR were not found to be significantly over-represented in any gene set tested by DAVID.
Significant biomedical keywords identified by PDG-ACE across the TNF/MTHFR gene pair, and relevance to depression with AUD
# PubMed citations for query:
hormone replacement therapy
Environmental Influence of Ethanol Exposure
Each significantly over-represented keyword identified by PDG-ACE represents one hypothesis on the etiology of a genetic interaction between TNF and MTHFR in depression and AUD. We tested each of these hypotheses by querying PubMed for "depression AND ethanol AND keyword" (Table 4). As in the previous round of testing, we used MeSH annotation to limit the queries and we used citation counts to provide indications of relevant association in the literature. Notably, since ethanol is a keyword in the PDG-ACE controlled vocabulary that we used, it acts as a positive control in this analysis and is the top keyword when ranked by relevance. In addition to ethanol, the top keywords ranked by relevance to depression and AUD are "consumption", "background", and "intake". In the Entrez Gene records for TNF and MTHFR, intake and consumption both refer to intake of alcohol, while background refers specifically to genetic background. Results of the PDG-ACE analysis are consistent with the hypothesis that genetic background, via TNF and MTHFR, as well as environmental influences, via alcohol intake or consumption, are interacting elements of susceptibility in comorbid depression with AUD. Given evidence of genetic interaction between TNF and MTHFR, as well as gene-by-environment interaction in comorbid depression with AUD, we next sought to understand TNF and MTHFR in a cellular context.
Model Building via GeneGo
We used the GeneGo suite [22, 23] to place TNF and MTHFR in a cellular context. Results of the PDG-ACE analysis suggested that ethanol consumption exerts an environmental influence on this genetic system. Also, TNF is a secreted protein that responds to the environment by binding TNF receptors to influence intracellular signal transduction pathways that regulate gene expression. Given these inputs, we focused our GeneGo analysis on cell signaling via signal transduction and the regulation of gene expression within the cell (input parameters in Table 3).
Genetic variation or extracellular signals that affect any part of the feedback cycle (Figure 2) would tend to be amplified over a number of cycles, potentially leading to a growing imbalance in the system over time. In this network, NF-kB activates the expression of MTHFR, which metabolizes folate, so imbalances in the system would be expected to lead to imbalances in folate metabolism. This result is consistent with evidence of decreased folate levels in alcoholism . In addition, reduced folate levels are associated with depression , where research on etiology suggests that reduced folate levels may alter neurotransmitter metabolism [39–41].
Ranking of Gene Ontology processes for GeneGo network in Figure 2
Gene Ontology Process
positive regulation of transcription from RNA polymerase II promoter
negative regulation of interleukin-12 biosynthetic process
positive regulation of I-kappaB kinase/NF-kappaB cascade
positive regulation of transcription, DNA-dependent
regulation of I-kappaB kinase/NF-kappaB cascade
positive regulation of transcription
positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process
regulation of interleukin-12 biosynthetic process
interleukin-12 biosynthetic process
response to wounding
Apolipoprotein E (APOE) was excluded from the formal analysis because the annotation in Entrez Gene was inferred, rather than curated. In pursuing the hypothesis that APOE is involved in comorbid depression with AUD, we re-started the analysis including APOE. We queried PubMed for citations consistent with this hypothesis (Table 2) and found evidence of APOE's role in both depression and AUD, but no evidence for a role in the comorbidity. PDG-ACE analysis did not produce any significant results after correction for multiple hypothesis tests. We attempted to insert APOE into the GeneGo network (Figure 2) and found no annotated interactions between APOE and the other genes in the network. Apolipoprotein E may well have an influence on comorbid depression with AUD but, if so, the mechanism is either independent of the network modeled or, more likely, the data is not yet available to reliably connect APOE to this network.
Comorbid depression with AUD shows evidence of both genetic and environmental influences on susceptibility. In three phases of hypothesis generation and testing (NCBI database queries, PDG-ACE and GeneGo analyses) we established and tested a model of gene-by-environment interaction that shows evidence of influencing the comorbidity and is consistent with established knowledge about AUD and depression . We first hypothesized that common genetic influences affect AUD and depression. We tested this hypothesis by searching for candidate genes and found published evidence, via Entrez Gene and PubMed, supporting the roles of TNF and MTHFR in depression and AUD. Given evidence of a multi-gene influence on the comorbidity, we hypothesized that TNF and MTHR participate in a genetic interaction influencing the comorbidity. Mining the Entrez Gene records for TNF and MTHFR via the PDG-ACE algorithm, we found twenty one keywords that are common and significantly over-represented across the gene pair, consistent with interaction between these genes in comorbid AUD with depression. In addition, among the significant keywords, those that are most often associated with depression and AUD in the literature are suggestive of an environmental effect on this genetic interaction via ethanol intake or consumption. Given evidence that TNF and MTHFR participate in a genetic interaction that may be influenced by environmental exposure to ethanol, as well as evidence that TNF influences signal transduction pathways in response to the environment, we hypothesized signal transduction as the most appropriate model of the gene-by-environment interaction. Modeling this hypothesis via GeneGo, we found that both TNF and MTHFR are influenced by a genetic feedback cycle that incorporates environmental ethanol exposure into folate metabolism. Altered folate levels, as well as AUD, are consistently linked to depression [42–44].
Additional hypotheses tested based on GeneGo modelling
Short form, PubMed Queries
# PubMed Citations
depression AND CBS
ethanol AND CBS
depression AND ethanol AND CBS
depression AND BHMT
ethanol AND BHMT
depression AND ethanol AND BHMT
depression AND SHMT
ethanol AND SHMT
depression AND ethanol AND SHMT
depression AND MTR
ethanol AND MTR
depression AND ethanol AND MTR
depression AND folate
ethanol AND folate
depression AND ethanol AND folate
depression AND p53
ethanol AND p53
depression AND ethanol AND p53
depression AND HNF4a
ethanol AND HNF4a
depression AND ethanol AND HNF4a
Notably, while the evidence assembled in this analysis is consistent with our hypothesized model of gene-by-environment interaction of comorbid depression with AUD, much data remains missing. For example, we emphasize the environmental influence of alcohol consumption, though we have little information on how alcohol dosage or the duration of alcohol exposure might affect genetic influences in our model. Equally, while we have identified a network of genes that may influence the comorbidity, we have limited information on heritable variation that would be expected to increase or decrease susceptibility. To date, the C677T variant of MTHFR is associated with both susceptibility to depression [27, 45, 46] and ethanol response , and levels of TNF mRNA have been associated with depression . Cytogenetic band 6p21, the chromosomal location of TNF, has recently been associated with chromosomal aberrations in alcoholism . However, the genetic influences of this network are likely to be much more complex than what we know so far. Missing data on these influences await follow-on analyses (e.g., targeted genotyping in an affected population versus controls, animal modelling) that can be informed by the model developed here. Also, given the model proposed, future data from WGA or microarray studies can be tested on a reduced number of hypotheses, thus increasing the power of these tools.
Implications of this model are consistent with dietary guidance recommending monitoring and appropriate supplementation of folate for patients in treatment for depression [48–50], AUD, or comorbid depression with AUD [37, 38, 42–44]. In addition, identification of the variants associated with risk could be useful in prognosis and treatment of either or both conditions . Pharmacogenomic approaches are being successfully implemented in psychiatry, to the great benefit of patients with specific genetic variants [52–54], and the identification of disease predisposing variants will likely improve the prognosis for depression with AUD.
The model developed in this analysis represents one mechanism whereby, for genetically susceptible individuals, alcohol intake could lead to altered folate metabolism and increased susceptibility to depression. The effect of excessive alcohol consumption on folate levels is not new  but this analysis puts the environmental effect of alcohol consumption into a genetic context and provides a model for further hypothesis testing.
Candidate gene approaches offer a necessarily limited view of gene-disease association because so much data is missing on virtually all genes. In addition, data mining approaches, if not carefully controlled, can lead to false positive associations. However, by starting with the most reliable data available to select candidate genes, using multiple lines of evidence to test the validity of candidates, then putting the candidates into context, we believe we have developed one valid model of gene-by-environment interaction influencing comorbid depression with AUD. The proposed gene-by-environment interaction model provides a biologically plausible and testable hypothesis on the genetic etiology of comorbid depression with AUD.
With appropriate caveats, this approach to complex disease analysis could be applied to understanding many diseases. Both publication bias towards positive results and bias towards genes that are commonly studied will tend to steer text based evidence in the direction of a relatively limited set of genes. This bias will be countered by the large volumes of data currently being generated without prior hypotheses. In particular, WGA and microarray data are being deposited in publicly available datasets. These data will complement text based approaches by allowing for unbiased tests of the reduced number of hypotheses posed. Indeed, as the volume of data available in databases increases, this type of analysis will become more valuable.
RCM, BJK, and MGM were supported in part by the National Center for Integrative Biomedical Informatics, U54-DA-021519. RCM, EFHS, and MGM were supported in part by the Prechter Bipolar Genetics Fund.
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