Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package

Refinement for Ontology Evolution in Virtual Enterprises

Virtual enterprise is based on the premise that work should be done where it can be done most optimally. In virtual enterprises, geographical boundaries merge seamlessly. It enables organisations to act in a way of flexibility and ability to adapt to rapid changes on the fly. However, different parties in a virtual enterprise must understand each other before they go further details in business. Ontologies are such kinds of ideal baselines to assist parties to communicate. One of the essential research issues with ontology is how to deal with changes during their evolving cycle. Therefore, ontology refinement is a crucial component in ontology evolution. This paper presents a taxonomy structure focusing on the is-a relations. In particular, the concept of closeness measurement is introduced based on the “distance” estimation. An extended cluster analysis process is provided. According to the algorithm presented, a new concept is generated according to its attributes. Additionally, the refinement mechanisms for primitive operations are proposed. Unlike some other ontology refinement mechanisms which leave ontology consistency checking to human users after modification, our method emphasises the importance of consistency checking by applying description logics which is demonstrated based on the proposed ontology

PSMIX: an R package for population structure inference via maximum likelihood method

BACKGROUND: Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings. RESULTS: We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture. CONCLUSION: Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy, but more efficient computations. PSMIX and its supplemental documents are freely available at

E-Business Value Process Modelling

E-business development is a considerable complicated task because the underlying logics of e-business and its new processes that are originated from implementing enhanced information technologies to streamline business performance introduce many complex issues. One of the difficulties is to capture the dynamic aspects of e-business that can be used for monitoring the business performance, in a way that could be helpful for the business adaptation to meet competitive advantages. Among many dynamics of e-business, the value system is the most interested one that has recently been addressed. However a value system is at the strategic level with no formal approaches for its representation, which introduces a gap between system modelling and implementation in the e-business development. In this paper, we will investigate a so called value process that can be not only used for value system modeling, but executed for simulation of the resulted model. For the purpose of value process modelling, we will adopt the process algebra approach, which will be integrated with others such as workflow in our modelling environment

Epoxy Interlocking: A Novel Approach to Enhance FRP-to-concrete Bond Behavior

This paper presents a novel approach which can enhance the interfacial bond behavior between fiber reinforced polymer (FRP) composite material and concrete. Epoxy ribs are formed by grooving on the concrete surface before epoxy is applied. The dowel action from the epoxy ribs leads to an “epoxy interlocking” effect. The mechanism of the proposed epoxy interlocking approach was analyzed in this paper from both adhesion and interlocking aspects. Furthermore, the partial interaction of the epoxy interlocking was quantified and calibrated by experimental results. The epoxy interlocking in the tested specimens led to an 88.8% increase in bond strength on average. An analytical approach was proposed to quantify the average partial interaction for the individual epoxy ribs. The load-slip relationship for individual epoxy ribs was found to be related to concrete compression behavior. A parametric study was conducted analytically on the effects of groove depth and spacing, concrete strength and epoxy rib location. The reasonable results in the parametric study further verify the efficiency of the epoxy interlocking to enhance the bond performance between FRP and concrete

Nonparametric Estimator of False Discovery Rate Based on Bernšteǐn Polynomials

Under a local dependence assumption about the p-values, an estimator of the proportion π0 of true null hypotheses, having a closed-form expression, is derived based on Bernšteǐn polynomial density estimation. A nonparametric estimator of false discovery rate (FDR) is then obtained. These estimators are proved to be consistent, asymptotically unbiased, and normal. Confidence intervals for π0 and the FDR are also given. The usefulness of the proposed method is demonstrated through simulations and its application to a microarray dataset. Keywords: Bernsteın polynomials, bioinformatics, density estimation, false discovery rate, local dependence, microarray, mixture model, multiple compariso