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

SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with “time-to-event” outcomes. Syed, H., Jorgensen, A. L, & Morris, A. P BMC Bioinformatics, 18(1):265, May, 2017.

Paper doi abstract bibtex

\textlessh2\textgreaterAbstract\textless/h2\textgreater \textlessh3\textgreaterBackground\textless/h3\textgreater Analysis 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. \textlessh3\textgreaterResults\textless/h3\textgreater SurvivalGWAS_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. \textlessh3\textgreaterConclusions\textless/h3\textgreater We 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.

@article{syed_survivalgwas_sv:_2017,
	title = {{SurvivalGWAS}\_SV: software for the analysis of genome-wide association studies of imputed genotypes with “time-to-event” outcomes},
	volume = {18},
	url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1683-z},
	doi = {10.1186/s12859-017-1683-z},
	abstract = {{\textless}h2{\textgreater}Abstract{\textless}/h2{\textgreater} {\textless}h3{\textgreater}Background{\textless}/h3{\textgreater} Analysis 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. {\textless}h3{\textgreater}Results{\textless}/h3{\textgreater} SurvivalGWAS\_SV is an easy to use software implemented using C\# and run on Linux, Mac OS X \&amp; 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. {\textless}h3{\textgreater}Conclusions{\textless}/h3{\textgreater} We 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.},
	language = {English},
	number = {1},
	journal = {BMC Bioinformatics},
	author = {Syed, Hamzah and Jorgensen, Andrea L and Morris, Andrew P},
	month = may,
	year = {2017},
	pages = {265},
}

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