With the recent development of high-throughput DNA microarray and next-generation sequencing techniques for detecting various genomic variants, genome-wide association studies (GWASs) have become a popular strategy to discover genetic factors affecting common complex diseases. Many GWASs have successfully identified genetic risk factors associated with common diseases and have achieved substantial success in unveiling the genomic regions that are responsible for various aspects of the resulting phenotypes. However, identifying the underlying mechanism of disease-susceptible loci has proven to be difficult due to the complex genetic architecture of common diseases. The previously associated variants that have been revealed through GWASs explain only a small portion of the genetic factors in complex diseases. This rather limited finding is partly ascribed to the lack of intensive analysis on undiscovered genetic determinants, such as rare variants. In this context, the analysis of association studies has been steered from a common variant approach toward a rare variant approach to understand the complexity of genotype-phenotype associations. However, there is still room to improve the statistical analysis of GWAS data.