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Cited by 330Forensic and Genetic ResearchGenetic diversity and population structureForensic Anthropology and Bioarchaeology Studies

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Estimating genetic kin relationships in prehistoric populations

Open access
Monroy Kuhn, Jose Manuel, Jakobsson, Mattias, Günther, Torsten·2018·PLOS ONE
DOI 10.1371/journal.pone.0195491PMC PMC5912749

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Abstract

Archaeogenomic research has proven to be a valuable tool to trace migrations of historic and prehistoric individuals and groups, whereas relationships within a group or burial site have not been investigated to a large extent. Knowing the genetic kinship of historic and prehistoric individuals would give important insights into social structures of ancient and historic cultures. Most archaeogenetic research concerning kinship has been restricted to uniparental markers, while studies using genome-wide information were mainly focused on comparisons between populations. Applications which infer the degree of relationship based on modern-day DNA information typically require diploid genotype data. Low concentration of endogenous DNA, fragmentation and other post-mortem damage to ancient DNA (aDNA) makes the application of such tools unfeasible for most archaeological samples. To infer family relationships for degraded samples, we developed the software READ (Relationship Estimation from Ancient DNA). We show that our heuristic approach can successfully infer up to second degree relationships with as little as 0.1x shotgun coverage per genome for pairs of individuals. We uncover previously unknown relationships among prehistoric individuals by applying READ to published aDNA data from several human remains excavated from different cultural contexts. In particular, we find a group of five closely related males from the same Corded Ware culture site in modern-day Germany, suggesting patrilocality, which highlights the possibility to uncover social structures of ancient populations by applying READ to genome-wide aDNA data. READ is publicly available from https://bitbucket.org/tguenther/read.

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Crossref · 10.1371/journal.pone.0195491

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Figures

Fig 1 Outline of the general READ workflow to estimate the degree of relationship between two individuals.
Fig 1 Outline of the general READ workflow to estimate the degree of relationship between two individuals.CC BY · PubMed Central
Fig 2 Simulation based estimates of false positive and false negative rates for different numbers of SNPs. The analysis is based on pairs of individuals with known degree of relationship in the 1000 genomes data. (A) All degrees of relationship, (B) only first degree relatives and (C) only second degree relatives. For pairs known to be related but not classified correctly (“False negative”) we distinguish between pairs classified as unrelated and classified as related but to a wrong degree. Error rates were estimated for 1,000, 2,500, 5,000, 10,000 and 50,000 overlapping SNPs between the pair of individuals.
Fig 2 Simulation based estimates of false positive and false negative rates for different numbers of SNPs. The analysis is based on pairs of individuals with known degree of relationship in the 1000 genomes data. (A) All degrees of relationship, (B) only first degree relatives and (C) only second degree relatives. For pairs known to be related but not classified correctly (“False negative”) we distinguish between pairs classified as unrelated and classified as related but to a wrong degree. Error rates were estimated for 1,000, 2,500, 5,000, 10,000 and 50,000 overlapping SNPs between the pair of individuals.CC BY · PubMed Central
Fig 3 Effect of allelic errors on READ’s performance. The simulations are identical to those conducted for Fig 2 but including a certain proportion of wrongly called alleles. The rates of false positives and false negatives were calculated accordingly. Error rates were estimated for 1,000, 2,500, 5,000, 10,000 and 50,000 overlapping SNPs between the pair of individuals.
Fig 3 Effect of allelic errors on READ’s performance. The simulations are identical to those conducted for Fig 2 but including a certain proportion of wrongly called alleles. The rates of false positives and false negatives were calculated accordingly. Error rates were estimated for 1,000, 2,500, 5,000, 10,000 and 50,000 overlapping SNPs between the pair of individuals.CC BY · PubMed Central
Fig 4 Number of SNP sites covered in both individuals dependent on the sequencing coverage for each individual. This figure shows expected number of SNP sites with overlapping data for two individuals for different combinations of sequencing depths. The contour lines mark different numbers of SNPs including those used in the simulations (see Fig 2). The total number of SNPs is set to 1,156,468, identical to what has been used in the simulations and similar to the 1.2 million SNPs used in the empirical data set [35]. The calculation assumes a Poisson distribution of sequencing coverage across the genome [49] and that coverage at each SNP site and individual is independent.
Fig 4 Number of SNP sites covered in both individuals dependent on the sequencing coverage for each individual. This figure shows expected number of SNP sites with overlapping data for two individuals for different combinations of sequencing depths. The contour lines mark different numbers of SNPs including those used in the simulations (see Fig 2). The total number of SNPs is set to 1,156,468, identical to what has been used in the simulations and similar to the 1.2 million SNPs used in the empirical data set [35]. The calculation assumes a Poisson distribution of sequencing coverage across the genome [49] and that coverage at each SNP site and individual is independent.CC BY · PubMed Central
Fig 5 READ estimates for the Srubnaya sample. (A) Sorted non-normalized average P0 values for all pairwise comparisons between Srubnaya individuals. Error bars show two standard errors of the mean. The solid horizontal line indicates the median value used for normalization. Dashed lines show the cutoffs used to classify the related individuals. The gray areas around dashed lines indicate 95% confidence intervals for the cutoffs accounting for the uncertainty in estimating the average P0 in the pair used to set the baseline for unrelated individuals. (B) A histogram of the non-normalized average P0 values, vertical dashed and solid lines indicate the same as in (A). A similar plot is produced as output when running READ.
Fig 5 READ estimates for the Srubnaya sample. (A) Sorted non-normalized average P0 values for all pairwise comparisons between Srubnaya individuals. Error bars show two standard errors of the mean. The solid horizontal line indicates the median value used for normalization. Dashed lines show the cutoffs used to classify the related individuals. The gray areas around dashed lines indicate 95% confidence intervals for the cutoffs accounting for the uncertainty in estimating the average P0 in the pair used to set the baseline for unrelated individuals. (B) A histogram of the non-normalized average P0 values, vertical dashed and solid lines indicate the same as in (A). A similar plot is produced as output when running READ.CC BY · PubMed Central
Fig 6 Kin-relationship among males at the Corded Ware site in Esperstedt, Germany. The five individuals, their inferred degree of relationship and their uniparental haplogroups. The dashed line between I1540 and I1538 shows a second degree relationship missed by READ.
Fig 6 Kin-relationship among males at the Corded Ware site in Esperstedt, Germany. The five individuals, their inferred degree of relationship and their uniparental haplogroups. The dashed line between I1540 and I1538 shows a second degree relationship missed by READ.CC BY · PubMed Central
Fig 7 Population distributions of average P0 before normalization. The boxplots show all non-normalized average P0 scores (one per pair of individuals) per culture. CAP and SG indicate whether the individuals were subject to SNP capture or shotgun sequencing, respectively. A broader chronological/geographical context is shown on the left.
Fig 7 Population distributions of average P0 before normalization. The boxplots show all non-normalized average P0 scores (one per pair of individuals) per culture. CAP and SG indicate whether the individuals were subject to SNP capture or shotgun sequencing, respectively. A broader chronological/geographical context is shown on the left.CC BY · PubMed Central

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  1. Van Rossum G, et al. Python Programming Language. In: USENIX Annual Technical Conference. vol. 41; 2007.

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Metadata

Authors
Monroy Kuhn, J. M., Jakobsson, M., & Günther, T.
Affiliations
Uppsala University (SE)
Corresponding author
  • Jakobsson, Mattias
  • Günther, Torsten
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2018-04-23
Volume
13
Issue
4
Pages
e0195491
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0195491
PMC
PMC5912749
OpenAlex
W2950454348
Cited by
330
References
79
Citations by year
2026: 31 · 2025: 47 · 2024: 53 · 2023: 62 · 2022: 40 · 2021: 42 · 2020: 23 · 2019: 19 · 2018: 11
FWCI
14.33
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
Funders
  • Erasmus Mundus Master Programme in Evolutionary Biology
  • Consejo Nacional de Ciencia y Tecnología
  • European Research Council (311413)
  • Vetenskapsrådet
  • Wenner-Gren-Foundations
  • Riksbankens Jubileumsfond
  • Knut och Alice Wallenbergs Stiftelse
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
  • Social Sciences › Arts and Humanities › Archeology (arts and humanities) › Forensic Anthropology and Bioarchaeology Studies
Keywords
genetic relatedness, ancient DNA, prehistoric humans, Corded Ware culture, patrilocality, social structure, degraded DNA, shotgun sequencing, human remains
MeSH terms
DNA, Ancient, Computer Simulation, DNA, Mitochondrial, Family, Germany, Haplotypes, Humans, Male, Models, Genetic, Software, Sequence Analysis, DNA, Polymorphism, Single Nucleotide, Chromosomes, Human, Y, DNA Fragmentation

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