markplatts / gwasurvivr

GWAS Survival Package in R

Introduction

gwasurvivr can be used to perform survival analyses of imputed genotypes from Sanger and Michigan imputation servers and IMPUTE2 software. This vignette is a tutorial on how to perform these analyses. This package can be run locally on a Linux, Mac OS X, Windows or conveniently batched on a high performing computing cluster. gwasurvivr iteratively processes the data in chunks and therefore intense memory requirements are not necessary.
gwasurvivr package comes with three main functions to perform survival analyses using Cox proportional hazard (Cox PH) models depending on the imputation method used to generate the genotype data:

1. michiganCoxSurv: Performs survival analysis on imputed genetic data stored in compressed VCF files generated via Michigan imputation server.
2. sangerCoxSurv: Performs survival analysis on imputed genetic data stored in compressed VCF files generated via Sanger imputation server.
3. impute2CoxSurv: Performs survival analysis on imputed genetic data from IMPUTE2 output.
4. gdsCoxSurv: For files that are already in GDS format (originally in IMPUTE2 format), users can provide a path to their GDS file and perform survival analysis and avoid having to recompress their files each run.
5. plinkCoxSurv: For directly typed data (or imputed data that is thresholded in plink) that are plink format (.bed, .bim, .fam files), users can can perform survival analysis.

All functions fit a Cox PH model to each SNP including other user defined covariates and will save the results as a text file directly to disk that contains survival analysis results. gwasurvivr functions can also test for interaction of SNPs with a given covariate. See examples for further details.

Installation

This package is currently available on Bioconductor devel branch or by using devtools library for R >= 3.4 and going to the Sucheston Campbell Lab GitHub repository (this page). If using R 3.5, use BiocManager to install the package, if using R >= 3.4, BiocInstaller or biocLite can be used.

For R >= 3.5:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("gwasurvivr", version = "devel")

Alternatively:

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_github("suchestoncampbelllab/gwasurvivr")

For R >= 3.4 and R < 3.5:

source("https://bioconductor.org/biocLite.R")
biocLite("gwasurvivr")

How to use package

Please refer to the vignette for a detailed description on how to use gwasurvivr functions for survival analysis (Cox proportional hazard model).