Imputation of Ancient Whole Genome Sus scrofa DNA Introduces Biases Toward Main Population Components in the Reference Panel

Sequencing ancient DNA to high coverage is often limited by sample quality and cost. Imputing missing genotypes can potentially increase information content and quality of ancient data, but requires different computational approaches than modern DNA imputation. Ancient imputation beyond humans has not been investigated. In this study we report results of a systematic evaluation of imputation of three whole genome ancient Sus scrofa samples from the Early and Late Neolithic (similar to 7,100-4,500 BP), to test the utility of imputation. We show how issues like genetic architecture and, reference panel divergence, composition and size affect imputation accuracy. We evaluate a variety of imputation methods, including Beagle5, GLIMPSE, and Impute5 with varying filters, pipelines, and variant calling methods. We achieved genotype concordance in most cases reaching above 90%; with the highest being 98% with similar to 2,000,000 variants recovered using GLIMPSE. Despite this high concordance the sources of diversity present in the genotypes called in the original high coverage genomes were not equally imputed leading to biases in downstream analyses; a trend toward genotypes most common in the reference panel is observed. This demonstrates that the current reference panel does not possess the full diversity needed for accurate imputation of ancient Sus, due to missing variations from Near Eastern and Mesolithic wild boar. Imputation of ancient Sus scrofa holds potential but should be approached with caution due to these biases, and suggests that there is no universal approach for imputation of non-human ancient species.

Automated summary

This study evaluated the imputation of ancient Sus scrofa genomes and found that the accuracy of imputation is affected by factors such as genetic architecture and divergence in the reference panel. Despite achieving high genotype concordance, the imputed genotypes did not fully capture the diversity present in the original genomes, leading to biases in downstream analyses.