A case-control design is a useful approach to detect association between
a genetic marker and a complex disease. The case-control data can be
displayed in a 2x3 contingency table (for a bi-allelic marker). To analyze
this 2x3 table, the trend test (1 degree of freedom) or Pearson's test
(2 degree of freedom) are simple and often employed. The trend test
depends on the underlying genetic model; otherwise it is not robust to
misspecification of the model. Pearson's test is robust but it has higher
degree of freedom. The maximum of three trend tests respectively optimal
for the four common genetic models (MAX) is more "efficiency robust"
than a single trend test and Pearson's test. Here, we present two-stage
analysis strategy. In the first stage, we select the underlying genetic
model using data. Then, in the second stage, we test association based
on the selected model. Simulation studies and applications to real data
are presented to demonstrate that the new strategy is preferable than
the existing ones (trend test, Pearson's test, and MAX) for case-control
genetic association studies.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.