BioinformaticsGeneticsPeer-reviewed journal article
hapCon: Estimating contamination of ancient genomes by copying from reference haplotypes
Abstract
MotivationHuman ancient DNA (aDNA) studies have surged in recent years, revolutionizing the study of the human past. Typically, aDNA is preserved poorly, making such data prone to contamination from other human DNA. Therefore, it is important to rule out substantial contamination before proceeding to downstream analysis. As most aDNA samples can only be sequenced to low coverages (<1x average depth), computational methods that can robustly estimate contamination in the low coverage regime are needed. However, the ultra low-coverage regime (0.1x and below) remains a challenging task for existing approaches. ResultsWe present a new method to estimate contamination in aDNA for male individuals. It utilizes a Li&Stephens haplotype copying model for haploid X chromosomes, with mismatches modelled as genotyping error or contamination. We assessed an implementation of this new approach, hapCon, on simulated and down-sampled empirical aDNA data. Our results demonstrate that hapCon outperforms a commonly used tool for estimating male X contamination (ANGSD), with substantially lower variance and narrower confidence intervals, especially in the low coverage regime. We found that hapCon provides useful contamination estimates for coverages as low as 0.1x for SNP capture data (1240k) and 0.02x for whole genome sequencing data (WGS), substantially extending the coverage limit of previous male X chromosome based contamination estimation methods. Availability and ImplementationA implementation of our software (hapCON) using Python and C has been deposited at https://github.com/hyl317/hapROH. We make hapCon available as part of a python package (hapROH), which is available at the Python Package Index (https://pypi.org/project/hapROH) and can be installed via pip. The documentation provides example use cases as blueprints for custom applications (https://haproh.readthedocs.io).
- organism: human
- assay: whole-genome sequencing
- assay: DNA methylation
- research focus: variant calling
- research focus: variant effect prediction
- research focus: benchmarking
- approach: statistical model
- Citations
- 37
- FWCI
- 3.92
- Field percentile
- top 5%
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