READv2: Advanced and user-friendly detection of biological relatedness in archaeogenomics

Alaçamlı, Erkin; Naidoo, Thijessen; Aktürk, Şevval; Güler, Merve N.; Mapelli, Igor; Vural, Kıvılcım Başak; Somel, Mehmet; Malmström, Helena; Günther, Torsten

doi:10.1101/2024.01.23.576660

Cited by 3 publications

(2 citation statements)

References 64 publications

(92 reference statements)

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“…We ran the software “ READv2 ” (Alaçamlı et al, 2024) to estimate close kin up to third-degree relatedness between each pair of individuals from the same population using Dataset 1 (see below). READv2 calculates the kinship coefficient (θ) between pseudo-haploid genome pairs.…”

Section: Methodsmentioning

confidence: 99%

Out-of-Anatolia: cultural and genetic interactions during the Neolithic expansion in the Aegean

Koptekin,

Aydoğan,

Karamurat

et al. 2024

Preprint

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Western Anatolia has been a crucial yet elusive element in the Neolithic expansion from the Fertile Crescent to Europe. Using 30 new palaeogenomes from Anatolia c.8000-6000 BCE we describe the early Holocene genetic landscape of Western Anatolia, which reveals population continuity since the late Upper Pleistocene. Our findings indicate that the Neolithisation of Western Anatolia in the 7thmillennium BCE was a multifaceted process, characterised by the assimilation of Neolithic practices by indigenous groups and the influx of populations from the east, their admixed descendants eventually laying the foundations of Neolithic Southeast Europe. Intriguingly, the observed diversity in material culture among Aegean Early Neolithic communities correlates with their geographical distances but not their genetic differences, signifying a decoupling between cultural developments and genetic admixture processes.

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Section: Methodsmentioning

confidence: 99%

Out-of-Anatolia: cultural and genetic interactions during the Neolithic expansion in the Aegean

Koptekin,

Aydoğan,

Karamurat

et al. 2024

Preprint

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“…Among the tools tested, KIN performs the most sophisticated estimation, which includes inference of both ROH and shared IBD segments using HMMs, calculating likelihoods for kinship degree assignment, and classifying parent-offspring and sibling pairs (we note that the recently released READv2 also distinguishes parentoffspring and siblings; Alaçamlı et al, 2024). KIN also differed from the other tools in estimating all simulated parent-offspring pairs' kinship coefficients as precisely as 0.25 due to the authors having constrained the parameter optimization space for this relationship type (Popli et al, 2023).…”

Section: F I G U R Ementioning

confidence: 99%

Benchmarking kinship estimation tools for ancient genomes using pedigree simulations

Aktürk,

Mapelli,

Güler

et al. 2024

Molecular Ecology Resources

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There is growing interest in uncovering genetic kinship patterns in past societies using low‐coverage palaeogenomes. 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 (1 to 50 K, with minor allele frequency ≥0.01) of shared SNPs are available. The performance of all four tools was comparable using ≥20 K SNPs. We found that first‐degree related pairs can be accurately classified even with 1 K 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 5 K 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.

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DeepKin: Predicting relatedness from low-coverage genomes and paleogenomes with convolutional neural networks

Güler,

Yılmaz,

Katırcıoğlu

et al. 2024

Preprint

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DeepKinis a novel tool designed to predict relatedness from genomic data using convolutional neural networks (CNNs). Traditional methods for estimating relatedness often struggle when genomic data is limited, as with paleogenomes and degraded forensic samples.DeepKinaddresses this challenge by leveraging two CNN models trained on simulated genomic data to classify relatedness up to the third-degree and to identify parent-offspring and sibling pairs. Our benchmarking showsDeepKinperforms comparably or better than the widely used toolREADv2.We validatedDeepKinon empirical paleogenomes from two paleological sites, demonstrating its robustness and adaptability across different genetic backgrounds, with accuracy >90% above 10K shared SNPs. By capturing information across genomic segments,DeepKinoffers a new methodological path for relatedness estimation in settings with highly degraded samples, with applications in ancient DNA, as well as forensic and conservation genetics.

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READv2: Advanced and user-friendly detection of biological relatedness in archaeogenomics

Cited by 3 publications

References 64 publications

Out-of-Anatolia: cultural and genetic interactions during the Neolithic expansion in the Aegean

Out-of-Anatolia: cultural and genetic interactions during the Neolithic expansion in the Aegean

Benchmarking kinship estimation tools for ancient genomes using pedigree simulations

DeepKin: Predicting relatedness from low-coverage genomes and paleogenomes with convolutional neural networks

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