A statistical simulation-based framework for sample size considerations in case-control SNP association studies

Abstract

In the thesis, a statistical simulation-based framework is presented that is intended for making sample size and power considerations prior to case-control association studies. It reviews biological background and biallelic single- and multiple-SNP disease models, with a focus on single-SNP models. Odds ratios, multiple testing, sample size, statistical power and the genomeSIM package are also reviewed. The framework is tested with the MAX stat method on a dominant disease model, demonstrating that it can be used for assessing whether different sample sizes are sufficient for detecting a causal SNP.