Logic regression67 may be a particularly attractive regression-based approach, at least in theory, for the analysis of rare variants. Logic regression, which is similar in ways to the method proposed by Han and Pan,40 was initially proposed for analyzing sequence data and does not assume that variants have been collapsed a priori. Instead, it constructs, and then tests for association, combinations of variants held together through the creation of dummy independent variables. These variables are constructed from logical operators such as ‘AND’ and ‘OR’ that connect and combine sets of variants into potential predictors of the phenotype. There are many issues with logic regression and related approaches that are similar to the issues discussed previously in the context of selecting an optimal subset of rare variants40, 66. These include: computational burden; difficulty in obtaining p-values for each potential independent variable (or individual rare variant, as opposed to a collapsed group of rare variants); and the identification of the optimal, and hence the biologically most-plausible, set of genetic predictors. The development of regression analysis methods for rare variant association analyses is
