Relative efficiency of trend tests with misspecified genetic models in stratified analyses of case-control or cohort data.

ABSTRACT:

Background/aims

Genetic single-nucleotide polymorphism (SNP) data are often analyzed using trend tests that rely on a specific assumption about the way that disease frequency varies across genotypes, but the validity of this assumption is not typically known. We explore the relative efficiency of trend tests in which the assumed model may or may not correspond to the true genetic model.

Methods

We derive formulae for the asymptotic relative efficiencies (AREs) comparing tests that assume different genetic models. We consider both unstratified and stratified tests, using both case-control and cohort data. We illustrate these formulae using realistic parameters and compare the calculated AREs to simulated relative efficiencies in finite samples.

Results

The AREs are ide

SUBMITTER: Sitlani CM 

PROVIDER: S-EPMC3190174 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature