A variant selection framework for genome graphs.

Chirag Jain,Neda Tavakoli,Srinivas Aluru

doi:10.1093/bioinformatics/btab302

Abstract

MotivationVariation graph representations are projected to either replace or supplement conventional single genome references due to their ability to capture population genetic diversity and reduce reference bias. Vast catalogues of genetic variants for many species now exist, and it is natural to ask which among these are crucial to circumvent reference bias during read mapping.ResultsIn this work, we propose a novel mathematical framework for variant selection, by casting it in terms of minimizing variation graph size subject to preserving paths of length α with at most δ differences. This framework leads to a rich set of problems based on the types of variants [e.g. single nucleotide polymorphisms (SNPs), indels or structural variants (SVs)], and whether the goal is to minimize the number of positions at which variants are listed or to minimize the total number of variants listed. We classify the computational complexity of these problems and provide efficient algorithms along with their software implementation when feasible. We empirically evaluate the magnitude of graph reduction achieved in human chromosome variation graphs using multiple α and δ parameter values corresponding to short and long-read resequencing characteristics. When our algorithm is run with parameter settings amenable to long-read mapping (α = 10 kbp, δ = 1000), 99.99% SNPs and 73% SVs can be safely excluded from human chromosome 1 variation graph. The graph size reduction can benefit downstream pan-genome analysis.Availability and implementation https://github.com/AT-CG/VF.Supplementary informationSupplementary data are available at Bioinformatics online.

Highlights

High-throughput technologies enable rapid sequencing of numerous individuals in a species population and cataloging observed variants
We separately consider the problems of minimizing the number of positions at which variants are retained, and minimizing the total number of variants selected. We show that both problems are optimally solvable in polynomial time when only single nucleotide polymorphisms (SNPs) are considered and the goal is to preserve all paths of length a found in the complete variation graph
We empirically evaluate run-time performance and reduction in variation graph sizes achieved by the multiple algorithms that are proposed in this article

Summary

Introduction

High-throughput technologies enable rapid sequencing of numerous individuals in a species population and cataloging observed variants. This is leading to a switch from linear representation of a chosen reference genome to graph representations depicting multiple observed haplotypes. Graph representations more accurately reflect the sampled individuals within a population, and their use in genome mapping algorithms reduces reference bias and increases mapping accuracy when sequencing a new individual (Ballouz et al, 2019). The graphs invariably contain paths combining variants across haplotypes, but never seen in any observed haplotype. The number of such recombinant paths increases combinatorically with graph size, and is troublesome when mapping long reads which span greater distances.

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