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

Learning Causal Bayesian Networks from Literature Data

In biomedical domains free text electronic literature is an important resource for knowledge discovery and acquisition. It is particularly true in the context of data analysis, where it provides a priori components to enhance learning, or references for evaluation. The biomedical literature contains the rapidly accumulating, voluminous collection of scientific observations boosted by the new high-throughput measurement technologies. The broader context of our work is to support statistical inference about the structural properties of the domain model. This is a two-step process, which consists of (1) the reconstruction of the beliefs over mechanisms from the literature by learning generative models and (2) their usage in a subsequent learning phase. To automate the extraction of this prior knowledge we discuss the types of uncertainties in a domain with respect to causal mechanisms and introduce a hypothesis about certain structural faithfulness between the causal Bayesian network model of the domain and a binary Bayesian network representing occurrences (i.e. causal relevance) of domain entities in publications describing causal relations. Based on this hypothesis, we propose various generative probabilistic models for the occurrences of biomedical concepts in scientific papers. Finally, we investigate how Bayesian network learning with minimal linguistic analysis support can be applied to discover and extract causal dependency domain models from the domain literature