Regression models treat the phenotype as a dependent variable and collapsed sets of variants as independent or predictor variables. Such methods provide a flexible framework for assessing the contribution of collections of rare variants to a phenotype.28, 33 Such models can accommodate a number of additional predictor variables, including common variants, covariates such as gender and age, and interaction terms. Recently, Morris and Zeggini33 assessed the power of simple regression methods for testing collapsed sets of rare variants for association with a quantitative trait and found that such approaches are indeed intuitive, flexible and powerful. The authors compared the use of a simple tally of the number of rare variants possed by an individual across a large region as a predictor of a phenotype against the use of a simple indicator of the possession of any rare variant. They found that the use of a tally may be more powerful.33 However, they did not consider conditioning effects (Figure 1C) or problems associated with analyses involving many correlated predictor variables.33
