Syed Hamzah, Jorgensen Andrea L, Morris Andrew P
Department of Biostatistics, University of Liverpool, Liverpool, UK.
Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK.
BMC Bioinformatics. 2017 May 19;18(1):265. doi: 10.1186/s12859-017-1683-z.
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.
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.
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.
对具有“事件发生时间”结局的全基因组关联研究（GWAS）进行分析越来越普遍，主要是在药物遗传学背景下，其中生存终点可能是死亡、疾病缓解或药物不良反应的发生。然而，能够有效处理来自具有事件发生时间结局的GWAS的遗传数据的规模和复杂性的方法和软件尚未得到广泛开发。
SurvivalGWAS_SV是一款易于使用的软件，用C#实现，可在Linux、Mac OS X和Windows操作系统上运行。SurvivalGWAS_SV能够处理大规模全基因组数据，通过在剂量模型下对事件发生时间结局进行建模来处理推算的基因型。分析使用Cox比例风险模型或威布尔回归模型。该软件可以对多个协变量进行调整，并纳入单核苷酸多态性-协变量交互效应。
我们推出了一种新的用于分析具有事件发生时间结局的GWAS的控制台应用分析工具。SurvivalGWAS_SV与高性能并行计算集群兼容，从而能够对大规模GWAS数据集进行高效分析，而不会出现内存问题。鉴于其与药物遗传学GWAS的特殊相关性，SurvivalGWAS_SV将有助于识别患者对治疗反应的遗传生物标志物，最终目标是为一系列疾病实现个性化治疗干预。
