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


CONCLUSIONS
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.


METHODS
The SEQPower program, source code, multi-platform executables, documentation, list of association tests, examples and tutorials are available at http://bioinformatics.org/spower.
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.
Gao T
GT
Wang
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.
Biao
B
Li
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.
Regie P Lyn
RP
Santos-Cortez
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.
Bo
B
Peng
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.
Suzanne M
SM
Leal
D000483:Alleles; D003627:Data Interpretation, Statistical; D004194:Disease / Q000235:genetics; D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D010641:Phenotype; D018401:Sample Size; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software