Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance

Some characteristics of the study subjects belonging to the CLI dataset.

<p>Some characteristics of the study subjects belonging to the CLI dataset.</p

The posterior probability of strong relevance of predictors for each target and for a multi-target case based on the CLI data set.

<p>Target variables: IgE level - <i>IgE</i>, Eosinophil level – <i>Eos</i>, Rhinitis – <i>Rhi</i>, Asthma – <i>Ast</i>.</p><p>“<i>Exist</i>” denotes the probability of strong relevance with respect to a given target.</p><p>“<i>Only</i>” denotes posteriors for strong relevance to exactly one of the targets.</p><p>“<i>OtherThan</i>”denotes posteriors for strong relevance to any other target than the one specified by the subcolumn.</p><p>“<i>AP</i>” column contains an approximation of multi-target strong relevance based on the individual strong relevance posteriors of the targets.</p><p>“<i>MT</i>” denotes the posterior of multi-target strong relevance.</p

The a posteriori probability that a SNP is directly relevant (D-Relevant), associated, strongly relevant (S-Relevant), transitive or in pure interaction with asthma using the RA data set.

<p>The a posteriori probability that a SNP is directly relevant (D-Relevant), associated, strongly relevant (S-Relevant), transitive or in pure interaction with asthma using the RA data set.</p

A possible dependency model in asthma.

<p>An example for a possible transitive association between IgE and Asthma. Although there is a possible direct relationship, IgE level may relate to asthma indirectly via eosinophil or a presence of allergy.</p

The most probable univariate (MBM), bivariate (2-MBS), trivariate (3-MBS) subsets of variables (Asthma dataset).

<p>Relevant SNPs having high or moderately high posteriors, i.e. high probability of being a member of the Markov blanket (MBM) of the target variable <i>Asthma</i> (<b>A</b>). Relevant SNP sets of size 2 (<b>B</b>); and of size 3 (<b>C</b>) indicating partial strong relevance. 2-MBS and 3-MBS denote the k = 2 and k = 3 sized subsets of Markov blanket sets. A high <i>k</i>-MBS posterior of a set of SNPs indicates their joint relevance and possible interactions between the SNPs.</p

The posterior probability of strong relevance for the most relevant SNPs in case of RA and CLI data.

*<p><i>RA – Asthma</i>: RA dataset, Asthma as target.</p><p><i>RA – Multitarget</i>: RA dataset, Asthma and Rhinitis as targets.</p>**<p><i>CLI – Asthma</i>: CLI dataset, Asthma as target.</p><p><i>CLI – Multitarget</i>: CLI dataset, IgE level-Eosinophil level-Rhinitis-Asthma as targets.</p

Gene-gene (SNP-SNP) joint relevance and interactions for asthma susceptibility.

*<p>Interaction terms were forced into logistic regression using the enter method. The main effects entered are indicated with underscore.</p>**<p>Posterior probability of joint relevance.</p>***<p>P-value and exp(B) values corresponding to the interaction terms in logistic regression using continuous variables.</p

Illustration of different dependency types between variables in a Bayesian Network structure.

<p><i>Pairwise relevance relations</i>: Direct causal relevance (e.g., Y1 and SNP1 have common edge), Transitive causal relevance (e.g., there are two directed paths between Y3 and SNP5), Confounded relevance (e.g., Y2 and SNP3 have a common ancestor SNP1), Association (e.g., Y1 and SNP1, because SNP1 is directly related to Y1; Y3 and SNP5, because SNP5 is transitively related to Y3; Y2 and SNP3, because they are in a confounded relation), Pure interactionist relevance (e.g., Y1 and SNP7 have common child), Strong relevance (e.g., Y1 and SNP1, because SNP1 is directly related to Y1; Y1 and SNP7, because they are in pure interaction). <i>Relevance of variable sets</i>: Strong relevance (e.g., the variable set consisting of Y2's parents, its children, and the other parents of its children is {Y1, SNP9, Y3, SNP7}). <i>Relevance for multiple target variables</i>: Strong relevance to one or more targets (e.g., the variable set consisting of {Y1,Y2,Y3}'s parents, its children, and the other parents of its children is {SNP1, SNP4, SNP7, SNP9}). <i>Red nodes</i>: potential target variables, <i>Green nodes</i>: SNP variables.</p

Hypothesized connection between FRMD6 and Birc5 in the conserved Hippo pathway.

<p>Hypothetic hippo pathway components in mammals are shown in various colors, with pointed and blunt arrowheads indicating activating and inhibitory interactions, respectively. The pathway regulates transcriptions of several genes, among others that of <i>Birc5</i>. Based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033573#pone.0033573-Zhao1" target="_blank">[36]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033573#pone.0033573-Heallen1" target="_blank">[37]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033573#pone.0033573-Dong1" target="_blank">[38]</a>. According to this pathway lower level of <i>FRMD6</i> might be associated with higher level of <i>Birc5</i>, as was found in the lung of the animal model of asthma.</p