Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data

TL;DR

This paper introduces a Bayesian variational inference method for detecting rare genetic variants in heterogeneous sequencing data, offering improved efficiency and specificity over existing algorithms, with applications in tracking variant emergence.

Contribution

The paper presents a novel Bayesian model and variational EM algorithm for rare variant detection in NGS data, demonstrating enhanced computational efficiency and specificity compared to prior methods.

Findings

Comparable sensitivity and specificity to MCMC methods

Higher specificity than existing algorithms

Early detection of beneficial variants in yeast data

Abstract

The detection of rare variants is important for understanding the genetic heterogeneity in mixed samples. Recently, next-generation sequencing (NGS) technologies have enabled the identification of single nucleotide variants (SNVs) in mixed samples with high resolution. Yet, the noise inherent in the biological processes involved in next-generation sequencing necessitates the use of statistical methods to identify true rare variants. We propose a novel Bayesian statistical model and a variational expectation-maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of low coverage ($27\times$ and $298\times$) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants.