SNP by SNP by environment interaction network of alcoholism

The SNP×SNP×E Network of Alcoholism

The Manhattan plot in Fig. 1 shows the significance of association of each SNP from genome-wide association analysis conducted in the available cohort of alcoholism. In this plot, each marker is represented by a dot and the –log₁₀ (p-value) is displayed on the y-axis. Markers above the horizontal black line (p<0.0005) have been used in subsequent analysis for construction of the SNP×SNP×E network of alcoholism (see Methods Section for more details). Figures 2 and 3 provide the full picture of the SNP×SNP×E network and a sub-graph of this network, respectively. Data collection, preprocessing, and the working principle of the model are described in Methods Section. The network has 413 nodes (397 SNPs, 15 environmental factors (Table 1), and one phenotypic variable). An edge from a node (parent node) to another node (child node) indicates the conditional probability of the child node being in a state (homozygous wild-type or BB, heterozygotes or Bb, and homozygous mutant or bb) given the state of the parent node. Note that each node can have either a single parent or two parents, one of which is constantly the phenotypic node with two states (case and control). The 397 SNPs in the network are found in the 21 chromosomal regions that have been linked to alcoholism in previous association or linkage studies (all of which employed datasets and/or statistical methods different from ours).

Variable	Level/Range
Income/yr $	0–10 K 10–20 K 20–30 K 30–40 K 40–50 K 50–75 K 75–100 K 100–150 K >150 K
Location of childhood home	large city/suburbs/small city/village/rural area
Level of education	less than H.S. grad/H.S. grad/some college/college grad or more
Gender	M/F
Age at interview	18–77
Race	AA/EA
Sexually abused as a child	yes/no
Otherwise physically abused as a child	yes/no
Neglected as a child	yes/no
Experienced sexual trauma	yes/no
Otherwise experienced physical trauma	yes/no
Experienced non-physical trauma	yes/no
Weight	85–435 lb
Frequency with which attends religious services	0–500
Height	49–80 in

Figures 2 and 3 confirm the frequent assertion that alcoholism is a byproduct of genetic and demographic factors. Based on Fig. 3, there seem to be a few likely reasons why such a synergy exists between demographic and genetic variables. First, the inclusion of race allowed the network to distinguish between SNPs that increase the risk of alcoholism only in African Americans (AAs) and those that do so only in European Americans (EAs). It is clear from Fig. 3 that there are a large number of SNPs that fit that description. As further evidence of race’s role, removal of race from the demographic-genetic classifier results in a decline in area under receiver operating characteristic curve (AUC) of 8.7%, the largest decline occurred by removing any feature from the network. Note that throughout this work, the AUC metric is merely used for ranking purposes (see Methods Section for details).

Results of network composition analysis

Genes

Alcohol has a variety of effects on the body; many of these arise from alcohol’s activation of receptors in the brain [20]. A number of the genes identified in our analysis have important functions in the brain. In total, 9 of the 13 genes listed in Table 2a (excluding the intergenic set) either have been explicitly associated with alcoholism in the literature or have functional ties to the disease (e.g. are involved in brain activity). Three genes have been explicitly associated with the development of alcoholism. CPE has been identified in prior GWA studies on alcoholism [7], and it encodes the enzyme carboxypeptidase E, which activates neuropeptides [21], proteins crucial to communication among neurons. PKNOX2, which regulates the transcription of other genes and affects anatomical development [22], has been linked to various types of substance abuse in European women [23]. GLT25D2 was identified as related to alcoholism in a GWA study on a dataset that had no samples in common with ours [24]. Five other genes have functional ties to alcoholism and the development of the behavior (Additional file 1, Supplementary Notes, Section 1). While many identified genes were generally in alignment with prior knowledge, further work should be done to understand the associations between alcoholism and the five genes that went uncorroborated in the literature (BLNK, BMPER, PDLIM5, VEPH1, AMPD3). Finally, the high importance of intergenic SNPs in Table 2a is surprising, but similar SNPs have been tied in prior GWA studies to alcoholism [25], and the noncoding RNA that is transcribed from intergenic regions affects gene expression levels in some cases [26].

G×G and G×E Interactions

Table 2b and c show demographic variables and interactions with a significant p-value (see Methods Section for details). Some of these factors and interactions are explicitly stated in prior studies. For example, a prior study [27] has demonstrated that alcohol consumption is negatively correlated with both income and educational status, both of which were deemed important demographic factors in Table 2b. The significance of the edge between income and education is sensible as well, as the conditional probability tables of the network indicate that a high level of education may be able to counteract a low level of income with respect to the development of alcoholism, and vice versa. Another prior study [28] provides the reason for the significance of the edge between race and income: there is a much stronger association between income and alcoholism in African Americans than in European Americans. Although no SNP-SNP interaction were deemed significant, the numerous SNP-SNP interactions that connect SNPs on the same gene (see Fig. 3) are reasonable, as SNPs that are closer together are more likely to interact and/or affect the same function [29].

Biological Pathways

Data Collection and Preprocessing

We utilized SAGE data [2], which featured 3,829 subjects and considered 948,658 SNPs from across the human genome, as well as several demographic variables. The data included human samples from three prior studies [2]; 30% of the individuals were African Americans and 70% were European Americans. The SAGE dataset includes 1,897 Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) cases and 1,932 alcohol-exposed non-dependents. We used 15 environmental variables (demographic factors) that are listed in Table 1. Several demographic factors were left out, especially ones relating to comorbidities, because their distributions across the cases and controls were heavily imbalanced. All continuous demographic variables in the data (e.g. income) were discretized. We first removed any SNPs out of Hardy-Weinberg equilibrium (P < 0.0001). Hardy-Weinberg equilibrium tests were run separately on the African Americans and the European Americans in order to ensure identification of any SNPs common only in one race out of equilibrium. SNPs with minor allele frequency (MAF) below 0.01 or call rate below 98% were also removed from consideration, leaving a total of 934,128 SNPs. Finally, the 3,776 samples (1909 cases and 1867 controls) with a genotyping rate above 98% were maintained. A Cochran-Mantel-Haenszel (CMH) association test was used to rank the 934,128 SNPs [30]. The association analysis was performed with the software PLINK [37]. The top 652 SNPs (p < 0.0005) were maintained for network construction as detailed in the next few subsections.

Maximum-weight Dependence Tree (MWDT)

Intuitively, I(SNP₁, SNP₂) measures the amount of information that SNP₁ carries about SNP₂ and vice versa. In a graphical representation of dependency among SNPs, we assume the dependencies have a tree structure (meaning each node has a single parent and one node (the root) has no parent), and assign to every edge of the tree an \( I\left({\mathrm{SNP}}_i,{\mathrm{SNP}}_{m_i}\right) \). Then the tree with the maximum weight is the one that maximizes \( {\displaystyle {\sum}_{i=1}^n I\left({\mathrm{SNP}}_i,{\mathrm{SNP}}_{m_i}\right)} \) where m _i denotes the parent node of node i and n is the number of SNPs under study. Note that there is no difficulty to maximize \( {\displaystyle {\sum}_{i=1}^n I\left({\mathrm{SNP}}_i,{\mathrm{SNP}}_{m_i}\right)} \) without considering the class labels; however, doing so leads to a static network that may not differentiate one class from another. In other words, it is not possible to use the network as an inferential tool. The technique originally proposed in [16] resolves this problem by stratifying the samples at the outset and constructing one network for each class. Nevertheless, having a different network of interactions for each class will not only make the inference a more difficult and elusive task, but may not have a biological ground either.

AUC in Ranking Networks of Interactions

From the previous section, we have to note that the MWDT guarantees the maximum likelihood estimate of the joint distribution given the true tree dependency among a set of given SNPs. However, for a set of SNPs of size n (here 652 SNPs selected as described before), there will be 2 ⁿ -1 potential maximum weight networks that can be constructed on any subset of n variables. Of course one may choose to grow the tree on all n SNPs but here we propose a complementary step to further narrow down the list of potential genetic factors used in the proposed network of alcoholism. To do so, we use the network as a classifier and use the AUC to rank a set of potential networks (see next subsection) and choose the one with the highest AUC. Unless otherwise stated, we employ 3-fold cross-validation procedure to compute AUCs. Nevertheless, since for the initial dimensionality reduction step, we use the CMH test on the full training data, we shall not interpret AUC as the predictive ability of our constructed network on a subset of SNPs and/or other factor. In other words, the use of AUC here is merely a measure to rank constructed sub-networks of interactions.

Ranking mechanism

To construct the optimal network of interactions, two approaches were employed: one is a backward sequential iterative approach described below, and the other is an approach based on a combination of linkage disequilibrium (LD) analysis [39] and the backward iterative approach. In the (backward) iterative approach, the MWDT was first trained with the remaining SNPs and the 15 demographic variables as part of the network. In each subsequent iteration, the 50 SNPs with the largest CMH p-values were removed and a new network was constructed using the reduced list of SNPs. The best network was the one with the highest AUC in differentiating cases from controls. The LD analysis-based approach sought to eliminate redundant SNPs. LD analysis was performed and SNPs that were strongly linked (i.e. frequently co-occurred in both the cases and the controls) were grouped into bins. The approach outlined by Carlson, et al. [40], with the r² threshold lowered from 0.8 to 0.4, was used to produce a single tag SNP for each LD bin. Only the tag SNPs were maintained, and the iterative approach was applied to them. This approach ensures that multiple SNPs that are proxies due to low LD distance are not selected. The tag SNP acts as a proxy for all SNPs in that region. The best networks from the two approaches were compared, and the one with the highest AUC was selected as the SNP×SNP×E network.

Analysis of network composition

To study the gene level interactions with the phenotype based on SNP level variations, we enumerate all genes with at least one SNP in the network. For each gene, we construct a sub-network of SNPs involved in the full SNP×SNP×E network located on that gene and record the AUC of a newly constructed sub-network. We consider race and sex as part of each sub-network. This would unlock the full potential of race- or sex-specific SNPs. In a sense, this analysis is similar to the adjustment for sex and age in the classical regression analysis.

We next considered important demographic features. To evaluate the importance of each demographic factor, we calculated the decline in resubstitution AUC (AUC on the training set) upon removal of that factor and all edges connected to it from the full SNP×SNP×E network. Resubstitution was used because the response of cross-validation AUC to minor changes is relatively imprecise due to larger variance of cross-validation estimators [41]. We used the Molecular Signatures Database [42] to determine the lists of genes related to 186 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) [43]. For each KEGG pathway, we recorded the AUC of the corresponding sub-network constructed using SNPs in the full network that are within the pathways’s genes, as well as race and sex. Finally, to detect most important interactions, we successively removed each edge in the full SNP×SNP×E network and recorded the decline in AUC. The analysis left us with an AUC for each gene, pathway, and a decline in AUC for each demographic feature and interaction. Rather than reporting the actual AUCs, which here is merely used for ranking purposes, we calculated a p-value associated with each AUC. Although here ranking based on AUC or p-value leads to the same result, we use the p-value threshold of 0.05 (non-adjusted) to narrow down the list.

To determine a p-value for each gene- or pathway-specific network, we constructed 1,000 networks, each with the same number of nodes for which the AUC in question was calculated, and determined their AUCs. The set of genetic features for each of the 1,000 networks was drawn randomly from the background set of SNPs. Race and sex were included as features in all 1,000 networks in order to ensure parity with the procedure used to generate the gene- or pathway-specific network. The list of 1,000 random AUCs enabled the calculation of a p-value for the AUC in question.

To determine the statistical significance of each decline in AUC (used for quantifying the importance of each demographic variable and the interactions in the SNP×SNP×E network), we used the same background set to construct 1,000 random networks with the same set of demographic factors and the same number of SNPs as in the SNP×SNP×E network. For each randomly generated model, we recorded the decline in AUC upon removal of a random SNP (in the case of the declines in AUC for demographic factors) or a random edge (in the case of the declines in AUC for interaction). The 1,000 random declines in AUC enabled the calculation of a p-value for the decline in AUC of interest. Each gene, demographic factor, pathway, and interaction relationship was now associated with a p-value.