Sign in
BioinformaticsbioRxiv
Preprint

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

Hamzah SyediD, Andrea JorgenseniD, Andrew P. MorrisiD

Abstract

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.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISSušak '21Jiang '21Li '20Sofer '21Han '25Schwartzent '25
60% semantic matchBioinformatics
Hana Sušak, Laura Serra, German M. Demidov+7 more·PLoS Computational Biology·Feb 19, 2021· 13 citations