SurvivalGWAS_Power: a user friendly tool for power calculations in pharmacogenetic studies with “time to event” outcomes

Syed, Hamzah; Jorgensen, Andrea; Morris, Andrew P.

doi:10.1186/s12859-016-1407-9

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…Each additional covariate took approximately an extra 0.275 s for each individual SNP analysed. The Weibull regression analysis runtime varies greatly; this is due to the convergence criteria of the Newton-Raphson method used for estimation of all parameters [ 12 ]. Runtime is also dependent on missing values within the sample file and whether or not the genotype file is compressed.…”

Section: Resultsmentioning

confidence: 99%

“…More details about power and choice of method can be found in Syed et al [ 14 ]. Software for performing power calculations under a range of pharmacogenetic time to event scenarios is also available from Syed et al [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

“…The Weibull model is beneficial when the hazard ratio is not proportional over time or the data have an accelerated failure time feature. For more information on the estimation of the Weibull regression model parameters please refer to Syed et al [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

“…SurvivalGWAS_SV is the second program to be released under the SurvivalGWAS Suite, which also includes the complementary power calculator “SurvivalGWAS_Power” [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Syed¹,

Jorgensen²,

Morris³

2017

BMC Bioinformatics

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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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…SurvivalGWAS_SV is the second program to be released under the SurvivalGWAS Suite, which also includes the complementary power calculator “SurvivalGWAS_Power” [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Syed¹,

Jorgensen²,

Morris³

2017

BMC Bioinformatics

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“…the total number of SNPs [6][7][8]. Recently there have been improvements in the computation times using some approximations for single-marker Cox PH regression [9], however this approach still yields marginal effect size estimates as the markers are not fitted simultaneously.…”

mentioning

confidence: 99%

Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

Ojavee

Kousathanas

Baños

et al. 2020

Preprint

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Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-diagnosis of disease and time-to-event phenotypes. We show in extensive simulation work that our method provides insight into genetic effects underlying disease progression, achieving higher statistical power and improved genomic prediction as compared to other approaches. We develop a hybrid-parallel sampling scheme facilitating age-at-onset analyses in large-scale biobank data. In the UK Biobank, we find evidence for an infinitesimal contribution of many thousands of common genomic regions to variation in the onset of common complex disorders of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of age-at-onset reflecting the underlying genetic liability to disease. In contrast, while age-at-menopause and age-at-menarche are highly polygenic, we find higher variance contributed by low frequency variants. We find 360 independent 50kb regions for age-at-menarche with ≥ 95% posterior inclusion probability of contributing 0.001% to the genetic variance, 115 regions for age-at-menopause, 246 regions for age-at-diagnosis of HBP, 32 regions for CAD, and 56 regions for T2D. Genomic prediction into the Estonian Genome Centre data shows that BayesW gives higher prediction accuracy than other approaches.

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Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

et al. 2021

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While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.

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SurvivalGWAS_Power: a user friendly tool for power calculations in pharmacogenetic studies with “time to event” outcomes

Cited by 7 publications

References 9 publications

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

Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

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