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ISCB 2014 Vienna, Austria • Abstracts - Poster Presentations 137Wednesday, 27th August 2014 • 15:30-16:00 Monday25thAugustTuesday26thAugustThursday28thAugustAuthorIndexPostersWednesday27thAugustSunday24thAugust P4.5.76 IL-1A gene promoter region polymorphism and the risk of familial CAD in a Pakistani population S Hussain1 1 COMSATS Institute of Information Technology, Islamabad, Pakistan   Coronary artery disease has complex etiology with acquired and inherited risk factors. Several factors including family history have been reported in the pathogenesis of CAD. The potential role of IL-1A gene variations in pathogenesis of CAD with familial history has not been investigated so far. Serum IL-1A levels were determined from 670 sporadic samples includ- ing 335 CAD patients and 335 healthy controls by using enzyme-linked immunosorbent assay. C-reactive protein levels were measured by Tina- quant C-reactive protein (latex) high sensitive assay. Genotyping for IL- 1A-889C>T polymorphism was investigated by using PCR-RFLP method. Heritability of susceptible allele was investigated from 130 trio-families with CAD affected offspring in this study. We observed a significant increase in IL-1A and hs-CRP levels in patients than in controls (P <0.0001, respectively). The IL-1A-889C>T polymor- phism was significantly associated with CAD in patients compared with healthy controls (P <0.0001).The minor alleleT at -889 was more prevalent in cases vs. controls (P <0.0001). The mutant allele T is more transmitted to affected offspring from heterozygous parents (χ2 TDT = 17.88 with 1 df, OR = 2.6, 95%CI = 19.04-48.68, P <0.0001). Further, we observed a significant increased in hs-CRP concentration in sporadic CAD patients after the comparison with healthy controls (P <0.0001). The mutant genotype CT+TT was significantly associated with high levels of hs-CRP from CAD patients (P <0.0001). For the first time we demonstrate a significant association of IL-1A-889 functional polymorphism with CAD.   P4.5.81 Selecting a classification function for class prediction in gene expression data VL Jong1,2 , PW Novianti1 , KCB Roes1,3 , MJC Eijkemans1 1 Julius Center, Department of Biostatistics, UMC, Utrecht, The Netherlands, 2 Viroscience Lab, Erasmus Medical Center, Rotterdam, The Netherlands, 3 Directorate of Quality Control and Patient Safety, UMC, Utrecht, The Netherlands   Classifiers have been shown to perform differently across datasets. Thus, a key question is how to select a classifier for a particular data. In this study, we devised a guideline based on data characteristics in choosing an opti- mal classifier for such data. We simulated data using correlation structures observed in a variety of real-life datasets. We varied sample size, number of genes, proportion of informative genes, absolute log2 fold changes and pair-wise correlations. For each simulated scenario, nine classifiers chosen from discriminant analyses, tree based, regularization/shrinkage, nearest neighbors, neural networks and partial least squares methods were constructed. To arrive at an optimal choice, the resulting error rates were clustered by hierarchical clustering. Well performing classifiers were clustered together and were summarized in a table specifying recommended classifiers for each sce- nario. These results were applied to eight real-life datasets. We hypothesized group 0 (optimal) and group 1 (non-optimal) classifier(s) for each data and constructed all classifiers. The median error of each clas- sifier was compared to those in the opposite group to determine its ob- served value. Using predicted and observed values, sensitivity and false positive rate (FPR) of each data were computed and a bivariate random effects model was fitted to this pair. As expected, the optimality of classi- fiers depended on specific scenarios. On the datasets evaluated this pre- dicted an optimal classifier with aggregated sensitivity of 80.8% and FPR of 13.7%. Hence, our proposed guidance allows selection of an optimal classifica- tion function based on data characteristics with a sensitivity of 80.8%. P4.5.84 Risk of pneumonia in patients with respiratory disease according to type of inhaler devices Y Kim1 , EJ Jang1,2 , S Choi1 , J Kim1 , CH Lee1,3 , JJ Yim3 , DK Kim4 , HI Yoon5 1 National Evidence-based Healthcare Collaborating Agency, Seoul, Republic of Korea, 2 Department of Information Statistics, Andong National, Andong, Republic of Korea, 3 Seoul National University Hospital, Seoul, Republic of Korea, 4 SMG-Seoul National University Boramae Medical Center, Seoul, Republic of Korea, 5 Seoul National University Bundang Hospital, Seongnam, Republic of Korea   Objective: The objective of this was to investigate the risk of a hospital admission or an emergency room visit for pneumonia in patients with re- spiratory disease between MDI and DPI of ICS with/without LABA. Methods: A retrospective Cohort study was conducted using the Korean national claims database between January 1, 2009 and December 31, 2011. We performed the individual matching to minimize the selection bias. Cox proportional hazard regression model was applied to compare the pneumonia risk of MDI vs. DPI and all data manipulation. Results: For ICS users, eligible cohort were 63,635 patients (18,780 in the ICS DPI group; 44,855 in the ICS MDI group), and 18,759 DPI users were one-to-one matched with 18,759 MDI users. The risk of pneumonia was higher in MDI users compared with DPI users after adjustment in to- tal cohort(HR 1.6; 95% confidence interval (CI) 1.2 to 2) and in matched cohort(HR 1.7; 95% CI 1.3 to 2.2). For ICS/LABA users, eligible cohort were 244,699 patients (236,724 in the ICS/LABA DPI group; 7,945 in the ICS/LABA MDI group) and 7,942 MDI users were one-to-five matched with 36,690 DPI users. The risk of pneu- monia was higher in MDI users compared with DPI users after adjustment both total cohort and matched cohort(HR 1.6; 95% CI 1.3 to 1.9 in the eli- gible cohort; HR 1.6; 95% CI 1.3 to 2.0 in the matched cohort). Conclusions: Use of MDI seems to increase risk of pneumonia compared to use of DPI in ICS users and ICS/LABA users.   P4.5.90 Finding variant X - a framework for identification of causative variants in WGS-data R Kreuzhuber1 , RV Pandey1 , A Weinhäusel1 , R Kallmeyer1 , I Visne1 , E Dilaveroglu1 , A Yildiz1 , A Kriegner1 1 AIT - Austrian Institute of Technology, Vienna, Austria   The number of rare monogenic diseases is estimated to be >5000. For half of these the underlying genes are unknown (McKusik V.A., 2011). An in- creasing proportion of common diseases, such as schizophrenia or autism, previously thought to be due to complex multifactorial inheritance, are now thought to represent a heterogeneous collection of rare monogenic disorders (Mitchell KJ, Porteous DJ, 2011), the large majority of which is still unknown. For the efficient investigation of genetic mutations next generation se- quencing (NGS) technology has revolutionized molecular diagnostics. Its big advantage is fast and cost-effective sequencing of enormous amounts of nucleic acids. Whole exome and whole genome NGS reveals a never before seen amount of variants for which filtering strategies a field of in- tensive research. MutAid, which is implemented on the XworX-platform, is a comprehen- sive variant calling pipeline for Sanger and most NGS platforms using state of the art open source mappers and variant callers. MutAid provides solid data management and quality control combined with the various filtering approaches: Integrative use of existing data bases and information con- tained within phenotypic ontologies. Co-analysis of results produced by those sequencing platforms is one of its key-features. This combination should enable the genetic researcher to identify caus- ative mutations more easily, especially in the context of rare diseases.

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