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Cited by 155Forensic and Genetic ResearchGenetic diversity and population structureGenetic Associations and Epidemiology

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Accurate detection of identity-by-descent segments in human ancient DNA

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Ringbauer, Harald, Huang, Yilei, Akbari, Ali, Mallick, Swapan, Olalde, Iñigo, Patterson, Nick, Reich, David·2024·Nature Genetics
DOI 10.1038/s41588-023-01582-wPMC PMC10028887

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Abstract

Long DNA segments shared between two individuals, known as identity-by-descent (IBD), reveal recent genealogical connections. Here we introduce ancIBD, a method for identifying IBD segments in ancient human DNA (aDNA) using a hidden Markov model and imputed genotype probabilities. We demonstrate that ancIBD accurately identifies IBD segments >8 cM for aDNA data with an average depth of >0.25× for whole-genome sequencing or >1× for 1240k single nucleotide polymorphism capture data. Applying ancIBD to 4,248 ancient Eurasian individuals, we identify relatives up to the sixth degree and genealogical connections between archaeological groups. Notably, we reveal long IBD sharing between Corded Ware and Yamnaya groups, indicating that the Yamnaya herders of the Pontic-Caspian Steppe and the Steppe-related ancestry in various European Corded Ware groups share substantial co-ancestry within only a few hundred years. These results show that detecting IBD segments can generate powerful insights into the growing aDNA record, both on a small scale relevant to life stories and on a large scale relevant to major cultural-historical events.

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Figure E1: Run-time Benchmarks of ancIBD. To benchmark run-times, we applied ancIBD on empirical ancient DNA data in .hdf5 format imputed at 1240k sites. We used the imputed hdf5 file from the Eurasian application (Fig. 3), choosing samples and pairs at random. Left: For each sample pair, all autosomes are screened for IBD. In one experiment all pairs of samples were run independently, leading to a linear dependency on pair number, as expected. In a second experiment, all samples were loaded into memory, and then each sample pair was screened for IBD. The apparent sub-linear behavior is due to the fact that loading n samples scales slower than the actual runtime of n·(n = 1)/2 sample pairs. Right: We depict the runtimes normalized per sample pair when screening all pairs of sample batches of various sizes for IBD. We visualize the loading time (the time it takes to load the hdf5 genotype data into memory), the pre-processing time (including preparing the transition and emission matrix), as well as the runtime of screening for IBD that includes the forward-backward algorithm as well as post-processing. Due to the decrease in the impact of the time to load the data, which scales linearly with batch size while the number of sample pair scales quadratically, we observe substantially increased runtimes per pair.
Figure E1: Run-time Benchmarks of ancIBD. To benchmark run-times, we applied ancIBD on empirical ancient DNA data in .hdf5 format imputed at 1240k sites. We used the imputed hdf5 file from the Eurasian application (Fig. 3), choosing samples and pairs at random. Left: For each sample pair, all autosomes are screened for IBD. In one experiment all pairs of samples were run independently, leading to a linear dependency on pair number, as expected. In a second experiment, all samples were loaded into memory, and then each sample pair was screened for IBD. The apparent sub-linear behavior is due to the fact that loading n samples scales slower than the actual runtime of n·(n = 1)/2 sample pairs. Right: We depict the runtimes normalized per sample pair when screening all pairs of sample batches of various sizes for IBD. We visualize the loading time (the time it takes to load the hdf5 genotype data into memory), the pre-processing time (including preparing the transition and emission matrix), as well as the runtime of screening for IBD that includes the forward-backward algorithm as well as post-processing. Due to the decrease in the impact of the time to load the data, which scales linearly with batch size while the number of sample pair scales quadratically, we observe substantially increased runtimes per pair.CC BY-NC-ND · PubMed Central
Figure E2: IBD sharing matrix of various Copper & Bronze Age West Eurasian Groups for four IBD length scales. As in Fig. 5, but for shared IBD [8 − 12 cM], [12 − 16 cM], [16 − 20 cM], >20 cM long. We used ancIBD to infer IBD segments between all pairs of groups and visualize the fraction of pairs that share at least one IBD for each pair of populations and for the four different IBD length bins.
Figure E2: IBD sharing matrix of various Copper & Bronze Age West Eurasian Groups for four IBD length scales. As in Fig. 5, but for shared IBD [8 − 12 cM], [12 − 16 cM], [16 − 20 cM], >20 cM long. We used ancIBD to infer IBD segments between all pairs of groups and visualize the fraction of pairs that share at least one IBD for each pair of populations and for the four different IBD length bins.CC BY-NC-ND · PubMed Central
Figure 1: Overview of the ancIBD algorithm. a) Sketch of the ancIBD Hidden Markov Model. The ancIBD HMM has five states: one background state of no allele sharing, and four states modeling the four possible IBD sharing states between two phased diploid genomes. We model phase switch errors within a true IBD segment as a transition between the four IBD states. b) Visualization of the full pipeline to call IBD. First, ancient DNA data is imputed and phased using published software (GLIMPSE) and a panel of modern reference haplotypes, and then our HMM (ancIBD) is applied to screen for IBD. The final output consists of two .tsv tables, one listing all inferred IBD segments and one listing IBD summary statistics for each pair of individuals.
Figure 1: Overview of the ancIBD algorithm. a) Sketch of the ancIBD Hidden Markov Model. The ancIBD HMM has five states: one background state of no allele sharing, and four states modeling the four possible IBD sharing states between two phased diploid genomes. We model phase switch errors within a true IBD segment as a transition between the four IBD states. b) Visualization of the full pipeline to call IBD. First, ancient DNA data is imputed and phased using published software (GLIMPSE) and a panel of modern reference haplotypes, and then our HMM (ancIBD) is applied to screen for IBD. The final output consists of two .tsv tables, one listing all inferred IBD segments and one listing IBD summary statistics for each pair of individuals.CC BY-NC-ND · PubMed Central
Figure 2: Performance of ancIBD on simulated IBD segments. a: Power and segment length errors. We copied in IBD segments of lengths 4,8,12,16,20 cM into synthetic diploid samples. We simulated shotgun-like and 1240k-like data (as described in Supp. Note S2) and visualize false positive, power, and length bias for 1x, 0.5x, and 0.25x coverage (rows). For each parameter set and IBD length, we simulated 500 replicates of pairs of chromosome 3, each pair with one copied-in IBD segment. The power (or recall) of detecting IBD segments of each simulated length is indicated in the text next to the corresponding gray vertical bar. b: False positive rate. We downsampled high-quality empirical aDNA data without IBD segments (Supp. Tab. 1F) to establish false positive rates of IBD segments for various coverage and IBD lengths (described in Supp. Note S5). We visualized expected IBD sharing assuming various constant population sizes to contextualize the false positive rates.
Figure 2: Performance of ancIBD on simulated IBD segments. a: Power and segment length errors. We copied in IBD segments of lengths 4,8,12,16,20 cM into synthetic diploid samples. We simulated shotgun-like and 1240k-like data (as described in Supp. Note S2) and visualize false positive, power, and length bias for 1x, 0.5x, and 0.25x coverage (rows). For each parameter set and IBD length, we simulated 500 replicates of pairs of chromosome 3, each pair with one copied-in IBD segment. The power (or recall) of detecting IBD segments of each simulated length is indicated in the text next to the corresponding gray vertical bar. b: False positive rate. We downsampled high-quality empirical aDNA data without IBD segments (Supp. Tab. 1F) to establish false positive rates of IBD segments for various coverage and IBD lengths (described in Supp. Note S5). We visualized expected IBD sharing assuming various constant population sizes to contextualize the false positive rates.CC BY-NC-ND · PubMed Central
Figure 3: Inferring biological relatives in the ancient DNA record using long IBD inferred with ancIBD. a: Inferred IBD among pairs of 10,156 ancient Eurasian individuals. The plot visualizes both the count (y-axis) as well as the summed length (x-axis) of all IBD at least 12 cM long. b: Simulated IBD among pairs of relatives. For each relative class, we simulated 100 replicates using the software PEDSIM [Caballero et al., 2019], as described in Supp. Note S6. As in a we depict the summed length and the count of all IBD at least 12 cM long. c: Inferred IBD among four ancient English Neolithic individuals, who lived about 5,700 years ago and were entombed at Hazleton North long cairn. A full pedigree was previously reconstructed using first and second-degree relatives inferred using pairwise SNP matching rates [Fowler et al., 2022]. We depict all IBD at least 12 cM long. The four individuals were genotyped using 1240k aDNA capture (I12438: 3.7x average coverage on target, I12440: 2.1x, I13896:1.1x, I12439: 6.7x)
Figure 3: Inferring biological relatives in the ancient DNA record using long IBD inferred with ancIBD. a: Inferred IBD among pairs of 10,156 ancient Eurasian individuals. The plot visualizes both the count (y-axis) as well as the summed length (x-axis) of all IBD at least 12 cM long. b: Simulated IBD among pairs of relatives. For each relative class, we simulated 100 replicates using the software PEDSIM [Caballero et al., 2019], as described in Supp. Note S6. As in a we depict the summed length and the count of all IBD at least 12 cM long. c: Inferred IBD among four ancient English Neolithic individuals, who lived about 5,700 years ago and were entombed at Hazleton North long cairn. A full pedigree was previously reconstructed using first and second-degree relatives inferred using pairwise SNP matching rates [Fowler et al., 2022]. We depict all IBD at least 12 cM long. The four individuals were genotyped using 1240k aDNA capture (I12438: 3.7x average coverage on target, I12440: 2.1x, I13896:1.1x, I12439: 6.7x)CC BY-NC-ND · PubMed Central
Figure 4: A geographically distant pair of ancient biological relatives detected with ancIBD. When screening ancient Eurasian individuals for IBD segments (see Fig. 3), we detected a pair of biological relatives whose remains were buried 1410 km apart, one in central Mongolia and one in Southern Russia. (Panel a). The two individuals were previously published in two different publications [Narasimhan et al., 2019, Jeong et al., 2020]. Both individuals are archaeologically associated with the Afanasievo culture and genetically cluster with other Afanasievo individuals [Narasimhan et al., 2019, Jeong et al., 2020]. Panel b: Posterior of non-IBD state on Chromosome 12, which has the longest inferred IBD segment (39.1 cM long, indicated as a dark blue bar). We also plot opposing homozygotes (gray dots up), whose absence is a necessary signal of IBD. Only SNPs where both markers have an imputed genotype probability greater than 0.99 are plotted. Panel c: Plot of all inferred IBD segments longer than 12 cM. Panel d: Histogram of inferred IBD segment lengths, as well as theoretical expectations for various types of relatives (calculated using formulas described in [Ringbauer et al., 2021]. Panels b-d were all created using default plotting functions bundled into the ancIBD software package.
Figure 4: A geographically distant pair of ancient biological relatives detected with ancIBD. When screening ancient Eurasian individuals for IBD segments (see Fig. 3), we detected a pair of biological relatives whose remains were buried 1410 km apart, one in central Mongolia and one in Southern Russia. (Panel a). The two individuals were previously published in two different publications [Narasimhan et al., 2019, Jeong et al., 2020]. Both individuals are archaeologically associated with the Afanasievo culture and genetically cluster with other Afanasievo individuals [Narasimhan et al., 2019, Jeong et al., 2020]. Panel b: Posterior of non-IBD state on Chromosome 12, which has the longest inferred IBD segment (39.1 cM long, indicated as a dark blue bar). We also plot opposing homozygotes (gray dots up), whose absence is a necessary signal of IBD. Only SNPs where both markers have an imputed genotype probability greater than 0.99 are plotted. Panel c: Plot of all inferred IBD segments longer than 12 cM. Panel d: Histogram of inferred IBD segment lengths, as well as theoretical expectations for various types of relatives (calculated using formulas described in [Ringbauer et al., 2021]. Panels b-d were all created using default plotting functions bundled into the ancIBD software package.CC BY-NC-ND · PubMed Central
Figure 5: Inferred IBD segments between various Copper & Bronze Age West Eurasian Groups. We visualize IBD segments 12–16 centimorgan long (for IBD sharing in other length classes see Fig. E2). We applied ancIBD to identify IBD segments between all pairs of 304 West Eurasian ancient individuals (all previously published data, see Supp. Tab. 1c) organized into 24 Archaeological groups. For each pair of groups, we plot the fraction of all possible pairs of individuals that share at least one IBD 12–16 centimorgan long, which we obtained by dividing the total number of pairs that share such IBD segments by the total number of all possible pairs: Between two different groups of n1 and n2 individuals, one has n1·n2 pairs, while within a group (on the diagonal in the figure) of size n one has n(n – 1)/2 pairs.
Figure 5: Inferred IBD segments between various Copper & Bronze Age West Eurasian Groups. We visualize IBD segments 12–16 centimorgan long (for IBD sharing in other length classes see Fig. E2). We applied ancIBD to identify IBD segments between all pairs of 304 West Eurasian ancient individuals (all previously published data, see Supp. Tab. 1c) organized into 24 Archaeological groups. For each pair of groups, we plot the fraction of all possible pairs of individuals that share at least one IBD 12–16 centimorgan long, which we obtained by dividing the total number of pairs that share such IBD segments by the total number of all possible pairs: Between two different groups of n1 and n2 individuals, one has n1·n2 pairs, while within a group (on the diagonal in the figure) of size n one has n(n – 1)/2 pairs.CC BY-NC-ND · PubMed Central

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  1. Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics) 627–628 (Springer, 2006).

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Metadata

Authors
Ringbauer, H., Huang, Y., Akbari, A., Mallick, S., Olalde, I., Patterson, N., & Reich, D.
Affiliations
  • Harvard University (US)
  • Max Planck Institute for Evolutionary Anthropology (DE)
  • Broad Institute (US)
Corresponding author
  • Ringbauer, Harald
  • Reich, David
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2023-12-20
Volume
56
Issue
1
Pages
143-151
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-023-01582-w
PMC
PMC10028887
OpenAlex
W4390070832
Cited by
155
References
65
Citations by year
2026: 62 · 2025: 49 · 2024: 32 · 2023: 12
FWCI
25.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • Max-Planck-Gesellschaft
  • U.S. Department of Health & Human Services | National Institutes of Health (HG012287)
  • John Templeton Foundation (61220)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
identity by descent, ancient DNA, hidden Markov models, genotype imputation, coancestry, Corded Ware, Yamnaya, Steppe ancestry

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