Literature DB >> 30395168

gwasurvivr: an R package for genome-wide survival analysis.

Abbas A Rizvi1, Ezgi Karaesmen1, Martin Morgan2, Leah Preus3, Junke Wang3, Michael Sovic3, Theresa Hahn4, Lara E Sucheston-Campbell3,5.   

Abstract

SUMMARY: 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.
AVAILABILITY AND IMPLEMENTATION: gwasurvivr, including source code, documentation and vignette are available at: http://bioconductor.org/packages/gwasurvivr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2019        PMID: 30395168      PMCID: PMC7963072          DOI: 10.1093/bioinformatics/bty920

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

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Authors:  Jonathan Marchini; Bryan Howie; Simon Myers; Gil McVean; Peter Donnelly
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5.  SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with "time-to-event" outcomes.

Authors:  Hamzah Syed; Andrea L Jorgensen; Andrew P Morris
Journal:  BMC Bioinformatics       Date:  2017-05-19       Impact factor: 3.169

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Authors:  Sayantan Das; Lukas Forer; Sebastian Schönherr; Carlo Sidore; Adam E Locke; Alan Kwong; Scott I Vrieze; Emily Y Chew; Shawn Levy; Matt McGue; David Schlessinger; Dwight Stambolian; Po-Ru Loh; William G Iacono; Anand Swaroop; Laura J Scott; Francesco Cucca; Florian Kronenberg; Michael Boehnke; Gonçalo R Abecasis; Christian Fuchsberger
Journal:  Nat Genet       Date:  2016-08-29       Impact factor: 38.330

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Journal:  Nat Genet       Date:  2016-08-22       Impact factor: 38.330

10.  A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.

Authors:  Bryan N Howie; Peter Donnelly; Jonathan Marchini
Journal:  PLoS Genet       Date:  2009-06-19       Impact factor: 5.917

  10 in total
  29 in total

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