Syed, H. | bioRxiv Science

bioRxiv2021

rareSurvival: rare variant association analysis for time-to-event outcomes.

SummaryRare variants have been proposed as contributing to the "missing heritability" of complex human traits. There has been much recent development of methodology to investigate association of complex traits with multiple rare variants within pre-defined "units" from sequence and array-based studies of the exome or genome. However, software for modelling time to event outcomes for rare variant associations has been under developed in comparison with binary and quantitative traits. We introduce a new command line application, rareSurvival, used for the analysis of rare variants with time to event outcomes. The program is compatible with high performance computing (HPC) clusters for batch processing. rareSurvival implements statistical methodology, which are a combination of widely used survival and gene-based analysis techniques such as the Cox proportional hazards model and the burden test. We introduce a novel piece of software that will be at the forefront of efforts to discover rare variants associated with a variety of complex diseases with survival endpoints. Availability & ImplementationrareSurvival is implemented in C#, available on Linux, Windows and Mac OS X operating systems. It is freely available (GNU General Public License, version 3) to download from https://www.liverpool.ac.uk/translational-medicine/research/statistical-genetics/software/. Download Mono for Linux or Mac OS X to run software. Contacthamzah.syed@liverpool.ac.uk Supplementary informationLinks to additional figures and tables are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies.

BackgroundMulti-omics studies are increasingly used to help understand the underlying mechanisms of clinical phenotypes, integrating information from the genome, transcriptome, epigenome, metabolome, proteome and microbiome. This integration of data is of particular use in rare disease studies where the sample sizes are often relatively small. Methods development for multi-omics studies is in its early stages due to the complexity of the different individual data types. There is a need for software to perform data simulation and power calculation for multi-omics studies to test these different methodologies and help calculate sample size before the initiation of a study. This software, in turn, will optimise the success of a study. ResultsThe interactive R shiny application MOPower described below simulates data based on three different omics using statistical distributions. It calculates the power to detect an association with the phenotype through analysis of n number of replicates using a variety of the latest multi-omics analysis models and packages. The simulation study confirms the efficiency of the software when handling thousands of simulations over ten different sample sizes. The average time elapsed for a power calculation run between integration models was approximately 500 seconds. Additionally, for the given study design model, power varied with the increase in the number of features affecting each method differently. For example, using MOFA had an increase in power to detect an association when the study sample size equally matched the number of features. ConclusionsMOPower addresses the need for flexible and user-friendly software that undertakes power calculations for multi-omics studies. MOPower offers users a wide variety of integration methods to test and full customisation of omics features to cover a range of study designs.

bioinformatics↗