Matthew Bryan
A common issue in Genome Wide Association Studies (GWAS) is relatedness between subjects due to restricting sampling to a confined homogenous population. As a result, the genetic data can be correlated across subject which may not be accounted for by common genetic testing methods. This presentation will discuss a method suggested by Timothy Thornton and Mary McPeek that is designed to account for relatedness between subjects for genetic data. The method uses a quasi-likelihood model approach to generate a quasi-likelihood score statistic. This statistic can be shown to be locally most powerful for testing genetic associations with a phenotype. Thus the method is expected to provide higher power for detecting small genetic effects.
