  In genome-wide association (GWA) studies the goal is to detect association
between one or more genetic markers and a given phenotype. The number of
genetic markers in a GWA study can be in the order hundreds of thousands and
therefore multiple testing methods are needed. This paper presents a set of
popular methods to be used to correct for multiple testing in GWA studies. All
are based on the concept of estimating an effective number of independent
tests. We compare these methods using simulated data and data from the TOP
study, and show that the effective number of independent tests is not additive
over blocks of independent genetic markers unless we assume a common value for
the local significance level. We also show that the reviewed methods based on
estimating the effective number of independent tests in general do not control
the familywise error rate.
