Accurate genotyping across variant classes and lengths using variant graphs

Jonas Andreas Sibbesen, Lasse Maretty, Anders Krogh*, Bent Petersen, Jonas Andreas Sibbesen, Siyang Liu, Christian Theil Have, Jette Bork-Jensen, Xiaosen Guo, Torben Hansen, Anders Krogh, Thorkild I.A. Sørensen, Oluf Borbye Pedersen, Jun Wang, Søren Brunak

*Corresponding author for this work
15 Citations (Scopus)

Abstract

Genotype estimates from short-read sequencing data are typically based on the alignment of reads to a linear reference, but reads originating from more complex variants (for example, structural variants) often align poorly, resulting in biased genotype estimates. This bias can be mitigated by first collecting a set of candidate variants across discovery methods, individuals and databases, and then realigning the reads to the variants and reference simultaneously. However, this realignment problem has proved computationally difficult. Here, we present a new method (BayesTyper) that uses exact alignment of read k-mers to a graph representation of the reference and variants to efficiently perform unbiased, probabilistic genotyping across the variation spectrum. We demonstrate that BayesTyper generally provides superior variant sensitivity and genotyping accuracy relative to existing methods when used to integrate variants across discovery approaches and individuals. Finally, we demonstrate that including a ‘variation-prior’ database containing already known variants significantly improves sensitivity.

Original languageEnglish
JournalNature Genetics
Volume50
Pages (from-to)1054-1059
Number of pages6
ISSN1061-4036
DOIs
Publication statusPublished - 1 Jul 2018

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