If multiple statistical tests are performed simultaneously, we should take into account multiple testing to properly control the false positive rate. In association studies performing statistical tests for a large number of correlated markers, the traditional Sidák correction is overly conservative and the permutation test is inefficient. This article discusses recently proposed approaches for correcting for multiple testing in association studies. We first explain basic concepts of statistics such as the p-value, false-positive rate, corrected p-value and family wise error rate. Then we discuss recently proposed methods in three categories: methods using multivariate normal distribution, methods calculating the effective number of tests and methods increasing the efficiency of permutation test. We compare the relative performance of these methods. Many of the methods are shown to be highly efficient and accurate compared to the traditional approaches and can readily be applied to the genome-wide datasets.

Key Concepts:

• In a statistical testing procedure performing multiple tests, multiple testing must be taken into account to properly control the false positive rate.
  
• In association studies, the traditional Sidák correction is overly conservative and the permutation test is inefficient.
  
• Recently proposed multiple testing correction methods are highly efficient and accurate and can be applied to the genome-wide datasets.