Biology subjects

Aktürk, S.

Publications and source records attributed to Aktürk, S..

2 recordsLinked to original sources

bioRxiv2024

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

The possibility to obtain genome-wide ancient DNA data from multiple individuals has facilitated an unprecedented perspective into prehistoric societies. Studying biological relatedness in these groups requires tailored approaches for analyzing ancient DNA due to its low coverage, post-mortem damage, and potential ascertainment bias. Here we present READv2 (Relatedness Estimation from Ancient DNA version 2), an improved Python 3 re-implementation of the most widely used tool for this purpose. While providing increased portability and making the software future-proof, we are also able to show that READv2 (a) is orders of magnitude faster than its predecessor; (b) has increased power to detect pairs of relatives using optimized default parameters; and, when the number of overlapping SNPs is sufficient, (c) can differentiate between full-siblings and parent-offspring, and (d) can classify pairs of third-degree relatedness. We further use READv2 to analyze a large empirical dataset that has previously needed two separate tools to reconstruct complex pedigrees. We show that READv2 yields results and precision similar to the combined approach but is faster and simpler to run. READv2 will become a valuable part of the archaeogenomic toolkit in providing an efficient and user-friendly classification of biological relatedness from pseudohaploid ancient DNA data.

bioinformatics↗

bioRxiv2023

Benchmarking kinship estimation tools for ancient genomes using pedigree simulations

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.

genetics↗