KAGE: Fast alignment-free graph-based genotyping of SNPs and short indels

Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently...

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Bibliographic Details
Published inbioRxiv
Main Authors Grytten, Ivar, Knut Dagestad Rand, Sandve, Geir Kjetil
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 20.12.2021
Subjects
Bayesian analysis
Computer applications
Gene mapping
Genetic diversity
Genomes
Genotypes
Genotyping
Next-generation sequencing
Single-nucleotide polymorphism
Variation
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Summary:Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently and accurately predict genotypes. Two novel ideas improve both the speed and accuracy: a Bayesian model incorporates genotypes from thousands of individuals to improve prediction accuracy, and a computationally efficient method leverages correlation between variants. We show that the accuracy of KAGE is at par with the best existing alignment-free genotypers, while being an order of magnitude faster. Competing Interest Statement The authors have declared no competing interest.
DOI:10.1101/2021.12.03.471074