Accurate genotyping across variant classes and lengths using variant graphs

Research output: Research - peer-reviewJournal article – Annual report year: 2018

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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
Number of pages11
ISSN1061-4036
DOIs
StatePublished - 2018
CitationsWeb of Science® Times Cited: 0
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