Eighty-eight variants highlight the role of T cell regulation and airway remodeling in asthma pathogenesis

Publisher's version (útgefin grein)Asthma is one of the most common chronic diseases affecting both children and adults. We report a genome-wide association meta-analysis of 69,189 cases and 702,199 controls from Iceland and UK biobank. We find 88 asthma risk variants at 56 loci, 19 previously unreported, and evaluate their effect on other asthma and allergic phenotypes. Of special interest are two low frequency variants associated with protection against asthma; a missense variant in TNFRSF8 and 3‘ UTR variant in TGFBR1. Functional studies show that the TNFRSF8 variant reduces TNFRSF8 expression both on cell surface and in soluble form, acting as loss of function. eQTL analysis suggests that the TGFBR1 variant acts through gain of function and together with an intronic variant in a downstream gene, SMAD3, points to defective TGFβR1 signaling as one of the biological perturbations increasing asthma risk. Our results increase the number of asthma variants and implicate genes with known role in T cell regulation, inflammation and airway remodeling in asthma pathogenesis.We thank the individuals who participated in this study and the staff at the Icelandic Patient Recruitment Center and the deCODE genetics core facilities. Further to all our colleagues who contributed to the data collection and phenotypic characterization of clinical samples as well as to the genotyping and analysis of the whole-genome association data. This research has been conducted using the UK biobank Resource under Application Number ‘24711’.Peer Reviewe

A self-organizing genetic algorithm for protein structure prediction

In the Genetic Algorithm (GA) with the standard random immigrants approach, a fixed number of individuals of the current population are replaced by random individuals in every generation. The random immigrants inserted in every generation maintain, or increase, the diversity of the population, what is advantageous to GAs applied to complex problems like the protein structure prediction problem. The rate of replaced individuals in the standard random immigrants approach is defined a priori, and has a great influence on the performance of the algorithm. In this paper, we propose a new strategy to control the number of random immigrants in GAs, applied to the protein structure prediction problem. Instead of using a fixed number of new individuals per generation, the proposed approach alters the number of new individuals to be inserted in the generation according to a self-organizing process. Experimental results indicate that the performance of the proposed algorithm in the protein structure prediction problem is superior or similar to the performance of the standard random immigrants approach with the best rate of individual replacement.status: publishe

Diversity control in genetic algorithms for protein structure prediction

In recent years, there is a growing interest in using Genetic Algorithms (GAs) in the protein structure prediction problem. However, the search space in this problem is very complex, what results in premature convergence of the GAs in their standard form, as the population generally gets trapped into local optima. Based on this fact, the use of two different strategies that can help GAs to maintain or increase the diversity of the population in the protein structure prediction problem are investigated in this paper. These strategies are Hypermutation and Random Immigrants. A new form of codification of the protein structure in the GA using sorted angles database is still proposed. Experimental results with Crambin (PDB code 1CRN), Met-Enkephalin (PDB code 1PLW), and DNA-Ligand (PDB code 1ENH) show that strategies to increase or maintain the population diversity are interesting for the protein structure prediction problem.status: publishe

Instance-level accuracy versus bag-level accuracy in multi-instance learning

In multi-instance learning, instances are organized into bags, and a bag is labeled positive if it contains at least one positive instance, and neg- ative otherwise; the labels of the individual instances are not given. The task is to learn a classifier from this limited information. While the original task description involved learning an instance classifier, in the literature the task is often interpreted as learning a bag classifier. Depending on which of these two interpretations is used, it is more natural to evaluate classifiers according to how well they predict, respectively, instance labels or bag labels. In the literature, however, the two interpretations are often mixed, or the intended interpretation is left implicit. In this paper, we investigate the difference be- tween bag-level and instance-level accuracy, both analytically and empirically. We show that there is a substantial difference between these two, and bet- ter performance on one does not necessarily imply better performance on the other. It is therefore useful to clearly distinguish them, and always use the evaluation criterion most relevant for the task at hand.status: publishe

Knowledge discovery in panoramic X-rays for postmortem identification

The estimated age and sex of an unknown person are important indicators in post mortem identification. In this study models are built for estimating these indicators automatically using panoramic X-rays. The used filtered dataset contains 303 images where the age group (7 groups) and sex is known. Two different techniques for extracting the features from the images are compared: automatic extraction (AE) and principal component analysis (PCA). To improve the quality of the prediction, feature selection is applied afterwards. This was done using both filter and wrapper techniques. The wrapper technique makes use of evolutionary algorithms. A novel evolutionary technique, adaptive binary continuous particle swarm optimization (ABCPSO) is introduced. The obtained results from the novel technique are comparable to the best studied standard evolutionary techniques, and present superior performance in some cases although not statistically significant. The results show that when the optimal subset of features is used the sex prediction reaches an average accuracy of 63.1% (AE) and 73.9% (PCA). For age prediction the adapted average accuracy is 34.5% and 36.0%, respectively. The Naive Bayes classifier produced the best results among five well known classifiers.status: publishe

Instance-level accuracy versus bag-level accuracy in multi-instance learning

Algorithms and the Foundations of Software technolog

Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation

We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10 -11 to 5.0 × 10 -21). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10 -6). Our results provide new evidence for the role of DNA methylation in blood pressure regulation

A wavelet algorithm for the solution of the double layer potential equation over polygonal boundaries

In this paper we consider a piecewise linear collocation method for the solution of the double layer potential equation corresponding to Laplace's equation over polygonal domains. We give a wavelet algorithm for the computation of the corresponding stiffness matrix and for the solution of the arising matrix equation with no more than O(N x [logN]"8) arithmetic operations. The error of the resulting approximate solution is of order O(N"-"2 x [logN]"6). Finally, we give some remarks on the generalization of the algorithm to the piecewise cubic collocation and present numerical tests. (orig.)Available from TIB Hannover: RR 5549(106)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman