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Bioinformatics. 2014 Aug 15;30(16):2377-8. doi: 10.1093/bioinformatics/btu296. Epub 2014 Apr 28.

Power analysis and sample size estimation for sequence-based association studies.

Author information

1
Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine and Department of Bioinformatics and Computational Biology, The University of Texas, M D Anderson Cancer Center, Houston, TX 77030, USA.

Abstract

MOTIVATION:

Statistical 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.

PMID:
24778108
PMCID:
PMC4133582
DOI:
10.1093/bioinformatics/btu296
[Indexed for MEDLINE]
Free PMC Article

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