  Rare variants are hypothesized to be largely responsible for heritability and
susceptibility to disease in humans. So rare variants association studies hold
promise for understanding disease. Conversely though, the rareness of the
variants poses practical challenges; since these variants are present in few
individuals, it can be difficult to develop data-collection and statistical
methods that effectively leverage their sparse information. In this work, we
develop a novel Bayesian nonparametric model to capture how design choices in
rare variants association studies can impact their usefulness. We then show how
to use our model to guide design choices under a fixed experimental budget in
practice. In particular, we provide a practical workflow and illustrative
experiments on simulated data.
