GRUPS-rs, a high-performance ancient DNA genetic relatedness estimation software relying on pedigree simulations

Maël Lefeuvre, Michael David Martin, Flora Jay, Marie-Claude Marsolier, Céline Bon
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Abstract

Background: The study of fine-grain genetic kinship ties (parents, siblings, cousins, etc.) from ancient remains is now gaining significant interest within the field of paleogenetics, as a means of deciphering the social organization of past societies. However, kinship analyses are in practice often quite difficult to apply within paleogenetic studies, and may carry a high degree of uncertainty in the results they provide, especially when applied on low coverage and/or highly degraded samples, or when studying poorly characterized populations. To overcome these challenges, most of the available kinship estimation methods either refrain from inferring ties beyond the second degree (e.g., half-siblings), and/or rely on the use of a cohort of individuals to obtain a satisfactory statistical significance. Thus, the current state of the art remains intrinsically limited when attempting to estimate kinship on a small number of individuals, or when trying to detect more distant relationships (e.g., cousins). Methods:Here, we present GRUPS-rs:an update and complete reimplementation of GRUPS (Get Relatedness Using Pedigree Simulations), an ancient DNA kinship estimation software based on the methods originally developed in (Martin et al. 2017).GRUPS-rs both computes an estimate of relatedness from randomly sampled pseudo-haploidized variant calls, and leverages high-definition pedigree simulations to bypass the use of a cohort of individuals. Results: We highlight that GRUPS and GRUPS-rs are especially suitable to perform kinship analysis on a restricted number of ancient samples, and can provide a sufficient statistical significance to estimate genetic relatedness past the second degree, while taking into account user-defined contamination and sequencing error estimates. Importantly, GRUPS-rs offers an estimated 14000-fold speed-up in runtime performance compared to its predecessor — allowing the joint estimation of kinship between dozens of individuals in a matter of minutes — and is now bundled with a user-friendly Shiny interface, in which users can interactively visualize their results. Conclusions: The GRUPS kinship estimation method is now fully operational in its "GRUPS-rs" implementation, whose use is particularly recommended when analyzing a restricted number of low coverage DNA samples.
GRUPS-rs,一个依赖于血统模拟的高性能古 DNA 遗传亲缘关系估计软件
背景:对古代遗存中细粒度遗传亲缘关系(父母、兄弟姐妹、表兄弟姐妹等)的研究,作为解读过去社会组织的一种手段,目前在古遗传学领域正受到越来越多的关注。然而,实际上亲缘关系分析在古遗传学研究中的应用往往相当困难,其结果可能具有很高的不确定性,尤其是在应用于低覆盖率和/或高度退化的样本时,或在研究特征不明显的人群时。为了克服这些挑战,大多数可用的亲缘关系估计方法要么避免推断二等亲以上的关系(如同父异母兄弟姐妹),要么依赖于使用一组个体来获得令人满意的统计意义。因此,在试图估计少量个体的亲属关系或试图检测较远的关系(如堂兄弟姐妹)时,目前的技术水平仍然存在内在的局限性、方法:在此,我们介绍 GRUPS-rs:GRUPS(Get Relatedness Using Pedigree Simulations)的更新和完整再实现,GRUPS-rs 是基于(马丁等人,2017 年)最初开发的方法的古 DNA 亲缘关系估计软件。GRUPS-rs 既能从随机取样的伪单倍体化变异调用中计算亲缘关系估计值,又能利用高清血统模拟绕过使用个体队列:我们强调,GRUPS 和 GRUPS-rs 特别适用于对数量有限的古代样本进行亲缘关系分析,并能提供足够的统计意义来估计二等以上的遗传亲缘关系,同时考虑到用户定义的污染和测序误差估计。重要的是,GRUPS-rs 的运行速度比其前身估计提高了 14000 倍,可以在几分钟内联合估计几十个个体之间的亲缘关系,而且现在还捆绑了用户友好的 Shiny 界面,用户可以在其中交互式地可视化他们的结果:结论:GRUPS 亲缘关系估计方法现已在其 "GRUPS-rs "实现中全面运行,在分析数量有限的低覆盖率 DNA 样本时,尤其推荐使用该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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