SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with “time-to-event” outcomes

BMC Bioinformatics

Software

Hamzah Syed¹  Andrea L. Jorgensen¹  Andrew P. Morris² 

[1] Department of Biostatistics, University of Liverpool, Liverpool, UK;Department of Biostatistics, University of Liverpool, Liverpool, UK;Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK;

关键词: Genome-wide association study;    Pharmacogenetics;    Time to event;    Cox proportional hazards;    Weibull;    Survival analysis;    SNP-covariate interaction;   

DOI  :  10.1186/s12859-017-1683-z

 received in 2017-02-10, accepted in 2017-05-11,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundAnalysis of genome-wide association studies (GWAS) with “time to event” outcomes have become increasingly popular, predominantly in the context of pharmacogenetics, where the survival endpoint could be death, disease remission or the occurrence of an adverse drug reaction. However, methodology and software that can efficiently handle the scale and complexity of genetic data from GWAS with time to event outcomes has not been extensively developed.ResultsSurvivalGWAS_SV is an easy to use software implemented using C# and run on Linux, Mac OS X & Windows operating systems. SurvivalGWAS_SV is able to handle large scale genome-wide data, allowing for imputed genotypes by modelling time to event outcomes under a dosage model. Either a Cox proportional hazards or Weibull regression model is used for analysis. The software can adjust for multiple covariates and incorporate SNP-covariate interaction effects.ConclusionsWe introduce a new console application analysis tool for the analysis of GWAS with time to event outcomes. SurvivalGWAS_SV is compatible with high performance parallel computing clusters, thereby allowing efficient and effective analysis of large scale GWAS datasets, without incurring memory issues. With its particular relevance to pharmacogenetic GWAS, SurvivalGWAS_SV will aid in the identification of genetic biomarkers of patient response to treatment, with the ultimate goal of personalising therapeutic intervention for an array of diseases.

【 授权许可】

CC BY   
© The Author(s). 2017

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