Open Accessgwasurvivr: an R package for genome-wide survival analysis
Author(s) -
Abbas Rizvi,
Ezgi Karaesmen,
Martin Morgan,
Leah Preus,
Junke Wang,
Michael G. Sovic,
Theresa Hahn,
Lara E. SuchestonCampbell
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty920
Subject(s) - bioconductor , covariate , r package , computer science , software , scalability , data mining , source code , proportional hazards model , imputation (statistics) , software package , statistics , biology , missing data , database , mathematics , genetics , operating system , machine learning , computational science , gene
To address the limited software options for performing survival analyses with millions of SNPs, we developed gwasurvivr, an R/Bioconductor package with a simple interface for conducting genome-wide survival analyses using VCF (outputted from Michigan or Sanger imputation servers), IMPUTE2 or PLINK files. To decrease the number of iterations needed for convergence when optimizing the parameter estimates in the Cox model, we modified the R package survival; covariates in the model are first fit without the SNP, and those parameter estimates are used as initial points. We benchmarked gwasurvivr with other software capable of conducting genome-wide survival analysis (genipe, SurvivalGWAS_SV and GWASTools). gwasurvivr is significantly faster and shows better scalability as sample size, number of SNPs and number of covariates increases.
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