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.

The treatment of acute lymphoblastic leukemia (ALL) and osteosarcoma (OSC) is very effective: the vast majority of patients recover and survive for decades. However, they still need to face serious adverse effects of chemotherapy. One of these is cardiotoxicity which may lead to progressive heart failure in the long term. Cardiotoxicity is contributed mainly to the use of anthracyclines and might have genetic risk factors. Our goal was to test the association between left ventricular function and genetic variations of candidate genes. Echocardiography data from medical records of 622 pediatric ALL and 39 OSC patients were collected from the period 1989–2015. Fractional shortening (FS) and ejection fraction (EF) were determined, 70 single nucleotide polymorphisms (SNPs) in 26 genes were genotyped. Multivariate logistic regression and multi-adjusted general linear model were performed to investigate the influence of genetic polymorphisms on the left ventricular parameters. Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) method was applied to test for the potential interaction of the studied cofactors and SNPs. Our results indicate that variations in ABCC2, CYP3A5, NQO1, SLC22A6 and SLC28A3 genes might influence the left ventricular parameters. CYP3A5 rs4646450 TT was 17% among ALL cases with FS lower than 28, and 3% in ALL patients without pathological FS (p = 5.60E-03; OR = 6.94 (1.76–27.39)). SLC28A3 rs7853758 AA was 12% in ALL cases population, while only 1% among controls (p = 6.50E-03; OR = 11.56 (1.98–67.45)). Patients with ABCC2 rs3740066 GG genotype had lower FS during the acute phase of therapy and 5–10 years after treatment (p = 7.38E-03, p = 7.11E-04, respectively). NQO1 rs1043470 rare T allele was associated with lower left ventricular function in the acute phase and 5–10 years after the diagnosis (p = 4.28E-03 and 5.82E-03, respectively), and SLC22A6 gene rs6591722 AA genotype was associated with lower mean FS (p = 1.71E-03), 5–10 years after the diagnosis. Genetic variants in transporters and metabolic enzymes might modulate the individual risk to cardiac toxicity after chemotherapy.

In this study, we aimed to identify novel genes involved in experimental and human asthma, importance of which has not yet been recognized. In an ovalbumin-induced murine model of asthma, we applied microarray gene expression analysis at different time points after allergen challenges. Advanced statistical methods were used to relate gene expression changes to cellular processes and to integrate our results into multiple levels of information available in public databases. At 4 h after the first allergen challenge, gene expression pattern reflected mainly an acute, but non-atopic, inflammatory response and strong chemotactic activity. At 24 h after the third allergen challenge, gene set enrichment analysis revealed significant over-representation of gene sets corresponding to T_h2-type inflammation models. Among the top down-regulated transcripts, an anti-oxidant enzyme, paraoxonase-1 (PON1), was identified. In human asthmatic patients, we found that serum PON1 activity was reduced at exacerbation, but increased parallel with improving asthma symptoms. PON1 gene polymorphisms did not influence the susceptibility to the disease. Our observations suggest that an altered PON1 activity might be involved in the pathogenesis of asthma, and serum PON1 level might be used for following up the effect of therapy.