# Bayesian nonparametric strategies for power maximization in rare variants association studies

arXiv: https://arxiv.org/abs/2112.02032 · HTML: https://mlsanity.com/paper/2112.02032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Improved prediction of future user activity in online A/B testing](https://mlsanity.com/paper/2402.03231/) (2024)

## Abstract (arXiv)

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.