A better understanding of the genetic architecture of disease, as well as a better appreciation of the forms and functions of DNA sequence variation, will undoubtedly impact the choice of a statistical method for rare variant association studies. Thus, for example, methods which can accommodate covariates, previously identified genetic factors, allelic heterogeneity, and different sets of collapsed variants simultaneously, such as regression-based methods, are clearly advantageous. However, methods which can account for subtle synergistic effects of many loci within a defined region and/or different forms of variation that might contribute to gene function, such as those rooted in sequence or functional similarity56, 57, 87, 88 are also likely to be appropriate. It is arguable that, in general, variants or groups of variants should always be studied in a more comprehensive regression model that includes covariates and other confounding variables no matter how the collapsed set was initially identified. Such an approach might mitigate a range of concerns, for example about accommodating confounding variables and the functional assessment of variants.
