Bayesian nonparametric strategies for power maximization in rare variants association studies

TL;DR

This paper introduces a Bayesian nonparametric model to optimize the design of rare variants association studies, addressing the challenge of sparse data and guiding experimental choices for better power within fixed budgets.

Contribution

It presents a novel Bayesian nonparametric framework that informs study design decisions in rare variants research, improving power and efficiency.

Findings

Model effectively captures the impact of design choices on study power

Guides optimal resource allocation in rare variants studies

Demonstrates practical workflow with simulated data

Abstract

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.