Title​

Accurate genotyping across variant classes and lengths using variant graphs

Abstract

Genotype estimates from short-read sequencing data are typically based solely on alignments of reads to a linear reference, however reads originating from more complex variants like insertions or deletions 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 proven computationally difficult.

We here 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.