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

Abstract

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. 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. The SEQPower program, source code, multi-platform executables, documentation, list of association tests, examples and tutorials are available at http://bioinformatics.org/spower.