Genome wide association studies have brought to the forefront issues
related to multiple hypothesis testing and have motivated many lines
of research in developing new methods for performing multiple
testing. In this talk, I will present some new problems related to
multiple hypothesis testing. These include incorporating prior
information when performing multiple testing, computing multiple
testing corrections analytically and exploiting structure among the
tests to improve statistical power.  I will present some preliminary
work addressing each of these problems.