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在ARG,血统和遗传亲属关系矩阵上

Brieuc Lehmann¹, Hanbin Lee², Luke Anderson-Trocmé³

¹Department of Statistical Science, University College London, WC1E 7HB, UK.

bioRxiv : the preprint server for biology

|March 17, 2025

概括

这项研究将遗传相关性概念统一为"分支相关性",并引入了使用祖先重组图进行大规模遗传相关性矩阵计算的高效算法. 这些方法可以分析数百万个基因组,推进人口遗传学研究.

科学领域:

遗传学是一个遗传学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

遗传关系对于人口,定量和关联遗传学研究至关重要.
遗传关联矩阵 (GRM) 存储双向相关性,但面临计算挑战 (二次时间/空间复杂性).
现有的GRM包括基于血统,基因型和祖先重组图 (ARG) 的定义,ARG-GRM显示了改善的人口结构捕获.

研究的目的:

将遗传关系的各种定义统一在一个框架下,引入"分支关系"和"分支GRM".
为大规模的GRM操作开发高效的计算方法,特别是基于ARG的相关性.
为了实现基因组数据集的可扩展分析,包括数百万个人.

主要方法:

引入了"分支相关性"和"分支GRM",使用对定量特征的添加模型.
开发了一种高效的算法,用于计算涉及分支GRM和没有显式GRM形成的向量产品,利用ARG的树序编码.
实现了树序列的随机主要组件算法,可扩展到数百万个基因组.

主要成果:

通过法国-加拿大队列案例研究证明了分支相关性和血统相关性之间的关系.
衍生算法显著降低了分支GRM操作的计算复杂性.
随机的主要组件算法有效地扩展到大型基因组数据集.

结论:

将各种遗传相关性概念整合到统一的"分支相关性"框架中.
利用ARG树序列编码为分支GRM计算提供了高效,可扩展的算法.
实现了以前由于计算限制而难以处理的大规模基因组分析.

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