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Automatic Identification of Dendritic Branches and their Orientation

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Published on: September 17, 2021

自动检测n级家庭成员的自动检测

Emil M Pedersen^1,2, Jette Steinbach¹, Carsten B Pedersen¹

¹National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark.

Frontiers in genetics

|December 29, 2025

概括

在家族遗传学研究中,LTFHPlus中的新R功能能够有效地识别任何程度的亲属. 这些工具通过快速处理大型数据集,提高了基因分析的可扩展性和互操作性.

科学领域:

遗传学遗传学是一种遗传学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

基于家族的遗传研究需要精确识别到一定程度的亲属.
现有的工具有局限性,包括对二度亲属的限制,返回整个血统,或需要广泛的前/后处理.

研究的目的:

在R包LTFHPlus中引入五个新的函数:prepare_graph,get_kinship,graph_to_trio,get_relations,以及Relation_per_proband_plot. 这五个函数包括:prepare_graph,get_kinship,graph_to_trio,get_relations,以及Relation_per_proband_plot.
通过有效识别任意程度的亲属并促进下游分析,解决现有工具的局限性.

主要方法:

使用graph包,从人口级三组数据构建一个定向图.
实现用于亲属矩阵计算,三人重建,关系标记和可视化的功能.
利用公开可用的 minnbreast 数据集用于图形构造和性能评估.

主要成果:

该LTFHPlus包成功构建了一个图表,其中包括28,081个个体和30,720个家族边缘.
识别500个人的高达三级亲属的中位数运行时间为0.03秒.
亲属关系矩阵计算的中位运行时间为1.57秒 (单线程).

结论:

新的LTFHPlus功能提供了一种可复制,可扩展和可互操作的解决方案,用于将家庭信息集成到遗传分析中.
这些工具克服了以前的局限性,使得基于家庭的基因研究更加有效和灵活.

关键词:

一个R包一个R包基于家庭的研究.遗传流行病学遗传流行病学图形理论中的图形理论.亲属关系矩阵亲属关系矩阵血统分析分析血统分析三元数据三元数据

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