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. 2024 Feb 28;52(4):1602-1612.
doi: 10.1093/nar/gkad1232.

Reconstructing DNA methylation maps of ancient populations

Arielle Barouch  1   2 , Yoav Mathov  1   3 , Eran Meshorer  1   3 , Benjamin Yakir  4 , Liran Carmel  1
Affiliations

Reconstructing DNA methylation maps of ancient populations

Arielle Barouch et al. Nucleic Acids Res. .

Abstract

Studying premortem DNA methylation from ancient DNA (aDNA) provides a proxy for ancient gene activity patterns, and hence valuable information on evolutionary changes in gene regulation. Due to statistical limitations, current methods to reconstruct aDNA methylation maps are constrained to high-coverage shotgun samples, which comprise a small minority of available ancient samples. Most samples are sequenced using in-situ hybridization capture sequencing which targets a predefined set of genomic positions. Here, we develop methods to reconstruct aDNA methylation maps of samples that were not sequenced using high-coverage shotgun sequencing, by way of pooling together individuals to obtain a DNA methylation map that is characteristic of a population. We show that the resulting DNA methylation maps capture meaningful biological information and allow for the detection of differential methylation across populations. We offer guidelines on how to carry out comparative studies involving ancient populations, and how to control the rate of falsely discovered differentially methylated regions. The ability to reconstruct DNA methylation maps of past populations allows for the development of a whole new frontier in paleoepigenetic research, tracing DNA methylation changes throughout human history, using data from thousands of ancient samples.

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Figures

Graphical Abstract
Graphical Abstract
Figure 1.
Figure 1.
(A) Percentage of CpG sites with coverage larger than a certain threshold, for each of the pooled cohorts. (B) Percentage of windows (genomic regions spanning 31 consecutive CpG positions) with coverage larger than a certain threshold, for each of the pooled cohorts. (C) Average coverage as a function of the distance to the nearest target SNP.
Figure 2.
Figure 2.
(A) C→T ratio in the pooled samples versus DNA methylation measured in modern human bone. CpGs were binned according to their measured methylation level in modern human bone. For each bin, an average C→T ratio was computed. Ust ‘Ishim (shotgun sequencing 42×) and the Altai Neanderthal (shotgun sequencing 52×) were added for comparison. ‘r’ denotes the Pearson correlation. (B) Mean methylation as a function of the effective coverage of the sample. Horizontal line shows mean methylation in Bone2.
Figure 3.
Figure 3.
(A) DNA methylation in CGIs, housekeeping genes promoters, ALU elements and across the genome. (B) Pearson correlations between the reconstructed DNA methylation of Caribbean_CER and Mongolia_BA_IA, and modern DNA methylation in human tissues and cell types. The ancient populations cluster with osteoblast. Correlations were computed based on genome segmentation, see Materials and methods. (C) Heatmap of Bone2 methylation versus Caribbean_CER methylation with 50 bins per axis. Match between these two methylation maps is manifested by the hot regions around high and low methylation levels. (D) Ribbon plots showing measured and reconstructed DNA methylation patterns in two random genomic regions. Methylation is color coded from low methylation in green to high methylation in red.
Figure 4.
Figure 4.
Absolute value of the difference between the average methylation in the cohort and the average methylation in Ust ‘Ishim, for the 873 modern human-derived DMRs.
See this image and copyright information in PMC

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