Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph

BackgroundDuring the last decade, the analysis of ancient DNA (aDNA) sequence has become a powerful tool for the study of past human populations. However, the degraded nature of aDNA means that aDNA molecules are short and frequently mutated by post-mortem chemical modifications. These features decrease read mapping accuracy and increase reference bias, in which reads containing non-reference alleles are less likely to be mapped than those containing reference alleles. Recently, alternative approaches for read mapping and genetic variation analysis have been developed that replace the linear reference by a variation graph which includes known alternative variants at each genetic locus. Here, we evaluate the use of variation graph software vg to avoid reference bias for ancient DNA and compare our approach to existing methods. ResultsWe used vg to align simulated and real aDNA samples to a variation graph containing 1000 Genome Project variants, and compared these with the same data aligned with bwa to the human linear reference genome. We show that use of vg leads to a balanced allelic representation at polymorphic sites, effectively removing reference bias, and more sensitive variant detection in comparison with bwa, especially for insertions and deletions (indels). Alternative approaches that use relaxed bwa parameter settings or filter bwa alignments can also reduce bias, but can have lower sensitivity than vg, particularly for indels. ConclusionsOur findings demonstrate that aligning aDNA sequences to variation graphs effectively mitigates the impact of reference bias when analysing aDNA, while retaining mapping sensitivity and allowing detection of variation, in particular indel variation, that was previously missed.