MOTIVATION: Despite recent advances of modern GWAS methods, it is still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. RESULTS: We developed an R package rqt, which offers gene-level GWAS meta-analysis. The package can be easily included into bioinformatics pipeline or used stand-alone. We applied this tool to the analysis of Alzheimer's disease data from three datasets CHS, FHS and LOADFS. Test results from meta-analysis of three Alzheimer studies show its applicability for association testing. AVAILABILITY AND IMPLEMENTATION: The package rqt is freely available under the following link: https://github.com/izhbannikov/rqt. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The Stochastic Process Model (SPM) represents a general framework for modeling the joint evolution of repeatedly measured variables and time-to-event outcomes observed in longitudinal studies, i.e., SPM relates the stochastic dynamics of variables (e.g., physiological or biological measures) with the probabilities of end points (e.g., death or system failure). SPM is applicable for analyses of longitudinal data in many research areas; however, there are no publicly available software tools that implement this methodology. RESULTS: We developed an R package stpm for the SPM-methodology. The package estimates several versions of SPM currently available in the literature including discrete- and continuous-time multidimensional models and a one-dimensional model with time-dependent parameters. Also, the package provides tools for simulation and projection of individual trajectories and hazard functions. CONCLUSION: In this paper, we present the first software implementation of the SPM-methodology by providing an R package stpm, which was verified through extensive simulation and validation studies. Future work includes further improvements of the model. Clinical and academic researchers will benefit from using the presented model and software. The R package stpm is available as open source software from the following links: https://cran.r-project.org/package=stpm (stable version) or https://github.com/izhbannikov/spm (developer version).