  Evolve and resequence studies provide a popular approach to simulate
evolution in the lab and explore its genetic basis. In this context, the
chi-square test, Fishers exact test, as well as the Cochran-Mantel-Haenszel
test are commonly used to infer genomic positions affected by selection from
temporal changes in allele frequency. However, the null model associated with
these tests does not match the null hypothesis of actual interest. Indeed due
to genetic drift and possibly other additional noise components such as pool
sequencing, the null variance in the data can be substantially larger than
accounted forby these common test statistics. This leads to p-values that are
systematically too small and therefore a huge number of false positive results.
Even, if the ranking rather than the actual p-values is of interest, a naive
application of the mentioned tests will give misleading results, as the amount
of over-dispersion varies from locus to locus. We therefore propose adjusted
statistics that take the over-dispersion into account while keeping the
formulas simple. This is particularly useful in genome-wide applications, where
millions of SNPs can be handled with little computational effort. We then apply
the adapted test statistics to real data fromDrosophila, and investigate how
in-formation from intermediate generations can be included when avail-able. The
obtained formulas may also be useful in other situations, provided that the
null variance either is known or can be estimated.
