Imputation of ancient human genomes.
Details
Serval ID
serval:BIB_6B67ECE9FE90
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Imputation of ancient human genomes.
Journal
Nature communications
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Publication state
Published
Issued date
20/06/2023
Peer-reviewed
Oui
Volume
14
Number
1
Pages
3660
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Publication Status: epublish
Abstract
Due to postmortem DNA degradation and microbial colonization, most ancient genomes have low depth of coverage, hindering genotype calling. Genotype imputation can improve genotyping accuracy for low-coverage genomes. However, it is unknown how accurate ancient DNA imputation is and whether imputation introduces bias to downstream analyses. Here we re-sequence an ancient trio (mother, father, son) and downsample and impute a total of 43 ancient genomes, including 42 high-coverage (above 10x) genomes. We assess imputation accuracy across ancestries, time, depth of coverage, and sequencing technology. We find that ancient and modern DNA imputation accuracies are comparable. When downsampled at 1x, 36 of the 42 genomes are imputed with low error rates (below 5%) while African genomes have higher error rates. We validate imputation and phasing results using the ancient trio data and an orthogonal approach based on Mendel's rules of inheritance. We further compare the downstream analysis results between imputed and high-coverage genomes, notably principal component analysis, genetic clustering, and runs of homozygosity, observing similar results starting from 0.5x coverage, except for the African genomes. These results suggest that, for most populations and depths of coverage as low as 0.5x, imputation is a reliable method that can improve ancient DNA studies.
Keywords
Humans, Genotyping Techniques/methods, Genome, Human/genetics, DNA, Ancient, Genotype, Genome-Wide Association Study/methods, Polymorphism, Single Nucleotide
Pubmed
Web of science
Open Access
Yes
Create date
26/06/2023 11:21
Last modification date
08/08/2024 6:35