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bioRxiv · 10.1101/2024.01.23.576850

A high coverage Mesolithic aurochs genome and effective leveraging of ancient cattle genomes using whole genome imputation.

Abstract

Ancient genomic analyses are often restricted to utilising pseudo-haploid data due to low genome coverage. Leveraging low coverage data by imputation to calculate phased diploid genotypes that enable haplotype-based interrogation and SNP calling at unsequenced positions is highly desirable. This has not been investigated for ancient cattle genomes despite these being compelling subjects for archaeological, evolutionary and economic reasons. Here we test this approach by sequencing a Mesolithic European aurochs (18.49x; 9852-9376 calBC), an Early Medieval European cow (18.69x; 427-580 calAD), and combine these with published individuals; two ancient and three modern. We downsample these genomes (0.25x, 0.5x, 1.0x, 2.0x) and impute diploid genotypes, utilising a reference panel of 171 published modern cattle genomes that we curated for 21.7 million (Mn) phased single-nucleotide polymorphisms (SNPs). We recover high densities of correct calls with an accuracy of >99.1% at variant sites for the lowest downsample depth of 0.25x, increasing to >99.5% for 2.0x (transversions only, minor allele frequency (MAF) [≥]2.5%). The recovery of SNPs correlates with coverage, on average 58% of sites are recovered for 0.25x increasing to 87% for 2.0x, utilising an average of 3.5 million (Mn) transversions (MAF [≥]2.5%), even in the aurochs which is temporally and morphologically distinct from the reference panel. Our imputed genomes behave similarly to directly called data in allele-frequency-based analyses; for example consistently identifying runs of homozygosity >2mb, including a long homozygous region in the Mesolithic European aurochs.

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BibTeXRIS

Erven, J. A. M., Scheu, A., Verdugo, M. P., Cassidy, L., Chen, N., Gehlen, B., Street, M., Madsen, O., Mullin, V. E.. 2024-01-24. A high coverage Mesolithic aurochs genome and effective leveraging of ancient cattle genomes using whole genome imputation.. https://doi.org/10.1101/2024.01.23.576850

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