ArticleBioinformatics (Oxford, England)2014
Power analysis and sample size estimation for sequence-based association studies.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Methodological opportunities in genomic data analysis to advance health equity.Nature reviews. Genetics · 2025Review
- The Korea Cohort Consortium: The Future of Pooling Cohort Studies.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2022Article
- Power and sample size calculations for high-throughput sequencing-based experiments.Briefings in bioinformatics · 2018Article
- Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies.Bioinformatics (Oxford, England) · 2018Article
- Guidelines for Large-Scale Sequence-Based Complex Trait Association Studies: Lessons Learned from the NHLBI Exome Sequencing Project.American journal of human genetics · 2016Article
- On Sample Size and Power Calculation for Variant Set-Based Association Tests.Annals of human genetics · 2016Article
- Genetic data simulators and their applications: an overview.Genetic epidemiology · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationStatistical methods have been developed to test for complex trait rare variant (RV) associations, in which variants are aggregated across a region, which is typically a gene. Power analysis and sample size estimation for sequence-based RV association studies are challenging because of the necessity to realistically model the underlying allelic architecture of complex diseases within a suitable analytical framework to assess the performance of a variety of RV association methods in an unbiased manner. SUMMARY: We developed SEQPower, a software package to perform statistical power analysis for sequence-based association data under a variety of genetic variant and disease phenotype models. It aids epidemiologists in determining the best study design, sample size and statistical tests for sequence-based association studies. It also provides biostatisticians with a platform to fairly compare RV association methods and to validate and assess novel association tests. AVAILABILITY AND IMPLEMENTATION: The SEQPower program, source code, multi-platform executables, documentation, list of association tests, examples and tutorials are available at http://bioinformatics.org/spower.
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Registered trials
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