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

Gao T. Wang, Biao Li, Regie P. Lyn Santos-Cortez, Bo Peng, Suzanne M. Leal

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2377-2378
Number of pages2
JournalBioinformatics
Volume30
Issue number16
DOIs
StatePublished - Aug 15 2014

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

MD Anderson CCSG core facilities

  • Bioinformatics Shared Resource

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