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Benchmarking kinship estimation tools for ancient genomes using pedigree simulations
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  • Şevval Aktürk,
  • Igor Mapelli,
  • Merve N. Güler,
  • Kanat Gürün,
  • Büşra Katırcıoğlu,
  • Kıvılcım Vural,
  • Ekin Sağlıcan,
  • Mehmet Çetin,
  • Reyhan Yaka,
  • Elif Sürer,
  • Gözde Atağ,
  • Sevim Seda Çokoğlu,
  • Arda Sevkar,
  • N. Ezgi Altınışık,
  • Dilek Koptekin,
  • Mehmet Somel
Şevval Aktürk
Hacettepe University

Corresponding Author:[email protected]

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Igor Mapelli
Middle East Technical University
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Merve N. Güler
Middle East Technical University
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Kanat Gürün
Middle East Technical University
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Büşra Katırcıoğlu
Middle East Technical University
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Kıvılcım Vural
Middle East Technical University
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Ekin Sağlıcan
Middle East Technical University
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Mehmet Çetin
Middle East Technical University
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Reyhan Yaka
Middle East Technical University
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Elif Sürer
Middle East Technical University
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Gözde Atağ
Middle East Technical University
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Sevim Seda Çokoğlu
Middle East Technical University
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Arda Sevkar
Hacettepe University
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N. Ezgi Altınışık
Hacettepe University
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Dilek Koptekin
Middle East Technical University
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Mehmet Somel
Middle East Technical University
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Abstract

There is growing interest in uncovering genetic kinship patterns in past societies using low-coverage paleogenomes. Here, we benchmark four tools for kinship estimation with such data: lcMLkin, NgsRelate, KIN, and READ, which differ in their input, IBD estimation methods, and statistical approaches. We used pedigree and ancient genome sequence simulations to evaluate these tools when only a limited number (1K to 50K) of shared SNPs (with minor allele frequency ≥0.01) are available. The performance of all four tools was comparable using ≥20K SNPs. We found that first-degree related pairs can be accurately classified even with 1K SNPs, with 85% F1 scores using READ and 96% using NgsRelate or lcMLkin. Distinguishing third-degree relatives from unrelated pairs or second-degree relatives was also possible with high accuracy (F1 >90%) with 5K SNPs using NgsRelate and lcMLkin, while READ and KIN showed lower success (69% and 79%, respectively). Meanwhile, noise in population allele frequencies and inbreeding (first cousin mating) led to deviations in kinship coefficients, with different sensitivities across tools. We conclude that using multiple tools in parallel might be an effective approach to achieve robust estimates on ultra-low coverage genomes.
Submitted to Molecular Ecology Resources
26 Jan 2024Review(s) Completed, Editorial Evaluation Pending
30 Jan 2024Editorial Decision: Revise Minor
15 Mar 20241st Revision Received
20 Mar 2024Submission Checks Completed
20 Mar 2024Assigned to Editor
20 Mar 2024Review(s) Completed, Editorial Evaluation Pending
28 Mar 2024Editorial Decision: Accept