ArticleBMC bioinformatics2017
SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with "time-to-event" outcomes.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Analysis of follow-up data in large biobank cohorts: a review of methodology.Frontiers in genetics · 2025Article
- Dopamine Pathway and Parkinson's Risk Variants Are Associated with Levodopa-Induced Dyskinesia.Movement disorders : official journal of the Movement Disorder Society · 2024Article
- A genome-wide association study on hematopoietic stem cell transplantation reveals novel genomic loci associated with transplant outcomes.Frontiers in immunology · 2024Article
- Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.Nature communications · 2021Article
- A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank.American journal of human genetics · 2020Article
- Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer.Clinical epigenetics · 2020Article
- Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record.BMC genomics · 2019Article
- gwasurvivr: an R package for genome-wide survival analysis.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAnalysis 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.
resultsSurvivalGWAS_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.
conclusionsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.