Suppr超能文献

评估 qpAdm 的性能:用于研究群体混合的统计工具。

Assessing the performance of qpAdm: a statistical tool for studying population admixture.

机构信息

Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.

The Max Planck-Harvard Research Center for the Archaeoscience of the Ancient Mediterranean, Cambridge, MA 02138, USA.

出版信息

Genetics. 2021 Apr 15;217(4). doi: 10.1093/genetics/iyaa045.

Abstract

qpAdm is a statistical tool for studying the ancestry of populations with histories that involve admixture between two or more source populations. Using qpAdm, it is possible to identify plausible models of admixture that fit the population history of a group of interest and to calculate the relative proportion of ancestry that can be ascribed to each source population in the model. Although qpAdm is widely used in studies of population history of human (and nonhuman) groups, relatively little has been done to assess its performance. We performed a simulation study to assess the behavior of qpAdm under various scenarios in order to identify areas of potential weakness and establish recommended best practices for use. We find that qpAdm is a robust tool that yields accurate results in many cases, including when data coverage is low, there are high rates of missing data or ancient DNA damage, or when diploid calls cannot be made. However, we caution against co-analyzing ancient and present-day data, the inclusion of an extremely large number of reference populations in a single model, and analyzing population histories involving extended periods of gene flow. We provide a user guide suggesting best practices for the use of qpAdm.

摘要

qpAdm 是一种用于研究具有两个或多个来源群体混合历史的人群祖先的统计工具。使用 qpAdm,可以确定适合感兴趣群体的人口历史的合理混合模型,并计算模型中每个来源群体可归因的祖先相对比例。虽然 qpAdm 在人类(和非人类)群体的人口历史研究中被广泛使用,但相对而言,评估其性能的工作做得还很少。我们进行了一项模拟研究,以评估 qpAdm 在各种情况下的行为,以便确定潜在弱点领域,并为使用建立推荐的最佳实践。我们发现,qpAdm 是一种强大的工具,在许多情况下都能产生准确的结果,包括数据覆盖率低、存在高比例的缺失数据或古代 DNA 损伤、或无法进行二倍体调用的情况。然而,我们警告不要共同分析古代和现代数据,不要在单个模型中包含大量的参考群体,也不要分析涉及基因流延长的人口历史。我们提供了一份用户指南,建议使用 qpAdm 的最佳实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8b9/8049561/2a3faef087bd/iyaa045f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验