Genetics of migraine in the age of genome-wide association studies

Genetic factors importantly contribute to migraine. However, unlike for rare monogenic forms of migraine, approaches to identify genes for common forms of migraine have been of limited success. Candidate gene association studies were often negative and positive results were often not replicated or replication failed. Further, the significance of positive results from linkage studies remains unclear owing to the inability to pinpoint the genes under the peaks that may be involved in migraine. Problems hampering these studies include limited sample sizes, methods of migraine ascertainment, and the heterogeneous clinical phenotype. Three genome-wide association studies are available now and have successfully identified four new genetic variants associated with migraine. One new variant (rs1835740) modulates glutamate homeostasis, thus integrates well with current concepts of neurotransmitter disturbances. This variant may be more specific for severe forms of migraine such as migraine with aura than migraine without aura. Another variant (rs11172113) implicates the lipoprotein receptor LRP1, which may interact with neuronal glutamate receptors, thus also providing a link to the glutamate pathway. In contrast, rs10166942 is in close proximity to TRPM8, which codes for a cold and pain sensor. For the first time this links a gene explicitly implicated in pain related pathways to migraine. The potential function of the fourth variant rs2651899 (PRDM16) in migraine is unclear. All these variants only confer a small to moderate change in risk for migraine, which concurs with migraine being a heterogeneous disorder. Ongoing large international collaborations will likely identify additional gene variants for migraine

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Double jeopardy:subordinates' worldviews and poor performance as predictors of abusive supervision

Purpose - To test a moderated mediation model where a positive relationship between subordinates’ perceptions of a dangerous world—the extent to which an individual views the world as a dangerous place—and supervisory abuse is mediated by their submission to authority figures, and that this relationship is heightened for more poorly performing employees. Design/Methodology/Approach - Data were obtained from 173 subordinates and 45 supervisors working in different private sector organizations in Pakistan. Findings - Our model was supported. It appears that subordinates’ dangerous worldviews are positively associated with their perceptions of abusive supervision and that this is because such views are likely to lead to greater submission to authority figures. But this is only for those employees who are performing more poorly. Implications - We highlight the possibility that individual differences (worldviews, attitudes to authority figures, and performance levels) may lead employees to become victims of abusive supervision. As such, our research informs organizations on how they may better support supervisors in managing effectively their subordinate relationships and, in particular, subordinate poor performance. Originality/Value - We add to recent work exploring subordinate-focused antecedents of abusive supervision, finding support for the salience of the previously untested constructs of individual worldviews, authoritarian submission, and individual job performance. In so doing we also extend research on dangerous worldviews into a new organizational setting. Finally, our research takes place within a new Pakistani context, adding to the burgeoning non-US based body of empirical work into the antecedents and consequences of abusive supervision