Unravelling reference bias in ancient DNA datasets.

MOTIVATION: The alignment of sequencing reads is a critical step in the characterization of ancient genomes. However, reference bias and spurious mappings pose a significant challenge, particularly as cutting-edge wet lab methods generate datasets that push the boundaries of alignment tools. Reference bias occurs when reference alleles are favoured over alternative alleles during mapping, whereas spurious mappings stem from either contamination or when endogenous reads fail to align to their correct position. Previous work has shown that these phenomena are correlated with read length but a more thorough investigation of reference bias and spurious mappings for ancient DNA has been lacking. Here, we use a range of empirical and simulated palaeogenomic datasets to investigate the impacts of mapping tools, quality thresholds, and reference genome on mismatch rates across read lengths. RESULTS: For these analyses, we introduce AMBER, a new bioinformatics tool for assessing the quality of ancient DNA mapping directly from BAM-files and informing on reference bias, read length cut-offs and reference selection. AMBER rapidly and simultaneously computes the sequence read mapping bias in the form of the mismatch rates per read length, cytosine deamination profiles at both CpG and non-CpG sites, fragment length distributions, and genomic breadth and depth of coverage. Using AMBER, we find that mapping algorithms and quality threshold choices dictate reference bias and rates of spurious alignment at different read lengths in a predictable manner, suggesting that optimized mapping parameters for each read length will be a key step in alleviating reference bias and spurious mappings. AVAILABILITY AND IMPLEMENTATION: AMBER is available for noncommercial use on GitHub (https://github.com/tvandervalk/AMBER.git). Scripts used to generate and analyse simulated datasets are available on Github (https://github.com/sdolenz/refbias_scripts).

Reconstruction of the Major Maternal and Paternal Lineages in the Feral New Zealand Kaimanawa Horses.

New Zealand has the fourth largest feral horse population in the world. The Kaimanawas (KHs) are feral horses descended from various domestic horse breeds released into the Kaimanawa ranges in the 19th and 20th centuries. Over time, the population size has fluctuated dramatically due to hunting, large-scale farming and forestry. Currently, the herd is managed by an annual round-up, limiting the number to 300 individuals to protect the native ecosystem. Here, we genotyped 96 KHs for uniparental markers (mitochondrial DNA, Y-chromosome) and assessed their genetic similarity with respect to other domestic horses. We show that at least six maternal and six paternal lineages contributed unequally to the KH gene pool, and today's KH population possibly represents two sub-populations. Our results indicate that three horse breeds, namely Welsh ponies, Thoroughbreds and Arabian horses had a major influence in the genetic-makeup of the extant KH population. We show that mitochondrial genetic diversity in KHs (π = 0.00687 ± 0.00355) is closer to that of the Sable Island horses (π = 0.0034 ± 0.00301), and less than other feral horse populations around the world. Our current findings, combined with ongoing genomic research, will provide insight into the population-specific genetic variation and inbreeding among KHs. This will largely advance equine research and improve the management of future breeding programs of these treasured New Zealand horse.