MOTIVATION: Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis. RESULTS: Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals. AVAILABILITY AND IMPLEMENTATION: Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Over the last 10 years, there have been over 3300 genome-wide association studies (GWAS). Almost every GWAS study provides a Manhattan plot either as a main figure or in the supplement. Several software packages can generate a Manhattan plot, but they are all limited in the extent to which they can annotate gene-names, allele frequencies, and variants having high impact on gene function or provide any other added information or flexibility. Furthermore, in a conventional Manhattan plot, there is no way of distinguishing a locus identified due to a single variant with very significant p-value from a locus with multiple variants which appear to be in a haplotype block having very similar p-values. RESULTS: Here we present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations. The software also gives flexibility on how and where the user wants to display the annotations. The software can be downloaded from CRAN repository and also from the GitHub project page. CONCLUSIONS: We present a major step up to the existing conventional Manhattan plot generation tools. We hope this form of display along with the added annotations will bring more insight to the reader from this new Manhattan++ plot.

Abstract Summary: The development of computational resources to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. eQTL Explorer stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer provides a new and powerful tool to interrogate these very large and complex datasets. Availability: The application is freely available for non-commercial research. Contact: [email protected] Supplementary Information: Documentation, source code and a demonstration version are available at

One of the biggest obstacles for implementing congestion pricing is the pricing-time scale. The Internet traffic is highly variant and hard to control without a mechanism that operates on very low time-scales, i.e. on the order of round-trip-times (RTTs). However, pricing naturally operates on very large time-scales because of human involvement. So, in order to put tight control on congestion through pricing, new implementation methods and architectures are needed for congestion pricing. In order to solve this problem, we propose a novel approach pricing over congestion control (POCC). The essence of POCC is to overlay congestion pricing on top of an underlying congestion control scheme which enforces a much tighter control than pricing. This way congestion in the interior network is controlled very tightly, while pricing is done at time-scales large enough to incorporate human involvement.

Multi-agent systems require adaptability to perform effectively in complex and dynamic environments. This article shows that agents should be able to benefit from dynamically adapting their decision-making frameworks. A decision-making framework describes the set of multi-agent decision-making interactions exercised by members of an agent group in the course of pursuing a goal or set of goals. The decision-making interaction style an agent adopts with respect to other agents influences that agent's degree of autonomy. The article introduces the capability of Dynamic Adaptive Autonomy (DAA), which allows an agent to dynamically modify its autonomy along a defined spectrum (from command-driven to consensus to locally autonomous/master) for each goal it pursues. This article presents one motivation for DAA through experiments showing that the ‘best’ decision-making framework for a group of agents depends not only on the problem domain and pre-defined characteristics of the system, but also on run-time factors that can change during system operation. This result holds regardless of which performance metric is used to define ‘best’. Thus, it is possible for agents to benefit by dynamically adapting their decision-making frameworks to their situation during system operation.