用分层软聚类法 tangleGen 推断祖先。

IF 6.2 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Genome research Pub Date : 2024-10-21 DOI:10.1101/gr.279399.124
Klara Elisabeth Burger, Solveig Klepper, Ulrike von Luxburg, Franz Baumdicker
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引用次数: 0

摘要

了解人口的遗传祖先对许多科学和社会领域都至关重要。它有助于更好地了解人类进化史,促进个性化医疗,帮助法医鉴定,并让个人与自己的家谱根源建立联系。ADMIXTURE 等现有方法大大提高了我们推断祖先的能力。然而,这些方法通常只适用于固定数量的独立祖先人群。因此,这些方法虽然能让我们深入了解基因混杂的情况,但并不包括层次解释。特别是,错综复杂的祖先种群结构仍然难以解开。具有一致遗传结构的替代方法,如分层聚类,可能会在解释推断的祖先方面带来好处。在这里,我们介绍一种软聚类工具 tangleGen,它将利用图论概念的分层机器学习框架 Tangles 移植到了群体遗传学领域。tangleGen 从分层的角度看待种群的组成和结构,提高了推断祖先关系的可解释性。此外,tangleGen 还增加了一层新的可解释性,因为它可以确定造成聚类结构的 SNPs。我们利用模拟数据和来自 1000 基因组计划的数据展示了 tangleGen 在推断祖先关系方面的能力和优势。
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Inferring ancestry with the hierarchical soft clustering approach tangleGen.

Understanding the genetic ancestry of populations is central to numerous scientific and societal fields. It contributes to a better understanding of human evolutionary history, advances personalized medicine, aids in forensic identification, and allows individuals to connect to their genealogical roots. Existing methods, such as ADMIXTURE, have significantly improved our ability to infer ancestries. However, these methods typically work with a fixed number of independent ancestral populations. As a result, they provide insight into genetic admixture, but do not include a hierarchical interpretation. In particular, the intricate ancestral population structures remain difficult to unravel. Alternative methods with a consistent inheritance structure, such as hierarchical clustering, may offer benefits in terms of interpreting the inferred ancestries. Here, we present tangleGen, a soft clustering tool that transfers the hierarchical machine learning framework Tangles, which leverages graph theoretical concepts, to the field of population genetics. The hierarchical perspective of tangleGen on the composition and structure of populations improves the interpretability of the inferred ancestral relationships. Moreover, tangleGen adds a new layer of explainability, as it allows identifying the SNPs that are responsible for the clustering structure. We demonstrate the capabilities and benefits of tangleGen for the inference of ancestral relationships, using both simulated data and data from the 1000 Genomes Project.

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来源期刊
Genome research
Genome research 生物-生化与分子生物学
CiteScore
12.40
自引率
1.40%
发文量
140
审稿时长
6 months
期刊介绍: Launched in 1995, Genome Research is an international, continuously published, peer-reviewed journal that focuses on research that provides novel insights into the genome biology of all organisms, including advances in genomic medicine. Among the topics considered by the journal are genome structure and function, comparative genomics, molecular evolution, genome-scale quantitative and population genetics, proteomics, epigenomics, and systems biology. The journal also features exciting gene discoveries and reports of cutting-edge computational biology and high-throughput methodologies. New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are presented electronically on the journal''s web site where appropriate. The journal also provides Reviews, Perspectives, and Insight/Outlook articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context.
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