More for less: Predicting and maximizing genetic variant discovery via Bayesian nonparametrics

TL;DR

This paper introduces a Bayesian nonparametric approach to optimize resource allocation in genome sequencing studies, improving predictions of new variants and enabling better experimental planning.

Contribution

The paper presents a novel Bayesian nonparametric methodology that predicts variant discovery and guides resource allocation, accommodating changes in experimental conditions.

Findings

More accurate variant prediction than recent methods

Effective in optimizing budget allocation between quality and quantity

Applicable to real-world genomic data from gnomAD

Abstract

While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains non-trivial. Under a fixed budget, then, scientists face a natural trade-off between quantity and quality; they can spend resources to sequence a greater number of genomes (quantity) or spend resources to sequence genomes with increased accuracy (quality). Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible, and thus as many new scientific insights as possible. In this paper, we consider the common setting where scientists have already conducted a pilot study to reveal variants in a genome and are contemplating a follow-up study. We introduce a Bayesian nonparametric methodology to predict the number of new variants in the follow-up study based on the pilot study. When experimental conditions are kept constant between the pilot and follow-up, we demonstrate on real data from the gnomAD project that our prediction is more accurate than three recent proposals, and competitive with a more classic proposal. Unlike existing methods, though, our method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for (i) more realistic predictions and (ii) optimal allocation of a fixed budget between quality and quantity.