Likelihood Under Covariate Assumptions (luca)
In genetic association studies, there is increasing interest in understanding the joint effects of genetic and nongenetic factors. For rare diseases, the case-control study is the standard design and logistic regression is the standard method of inference. However, the power to detect statistical interaction is a concern, even with relatively large samples. LUCA implements maximum likelihood inference under
- independence of the genetic factor and nongenetic attributes in the control population,
- independence of the genetic factor and nongenetic attributes, plus Hardy-Weinberg proportions (HWP) in control genotype frequencies, or
- simple dependence between the genetic and nongenetic covariates in the control population.
Maximum likelihood under covariate assumptions offers improved precision of interaction estimators compared to the standard logistic regression approach which makes no assumptions on the distribution of covariates.
Windows users can also install the package via the "Packages" menu item.