Fast variance component analysis using large-scale ancestral recombination graphs

Jiazheng Zhu, Georgios Kalantzis, Ali Pazokitoroudi, Árni Freyr Gunnarsson, Hrushikesh Loya, Han Chen, Sriram Sankararaman, Pier Francesco Palamara
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Abstract

Recent algorithmic advancements have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from genomic data in large cohorts. These inferred ARGs provide a detailed representation of genealogical relatedness along the genome and have been shown to complement genotype imputation in complex trait analyses by capturing the effects of unobserved genomic variants. An inferred ARG can be used to construct a genetic relatedness matrix, which can be leveraged within a linear mixed model for the analysis of complex traits. However, these analyses are computationally infeasible for large datasets. We introduce a computationally efficient approach, called ARG-RHE, to estimate narrow-sense heritability and perform region-based association testing using an ARG. ARG-RHE relies on scalable randomized algorithms to estimate variance components and assess their statistical significance, and can be applied to multiple quantitative traits in parallel. We conduct extensive simulations to verify the computational efficiency, statistical power, and robustness of this approach. We then apply it to detect associations between 21,374 genes and 52 blood-related traits, using an ARG inferred from genotype data of 337,464 individuals from the UK Biobank. In these analyses, combining ARG-based and imputation-based testing yields 8% more gene-trait associations than using imputation alone, suggesting that inferred genome-wide genealogies may effectively complement genotype imputation in the analysis of complex traits.
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利用大规模祖先重组图进行快速变异成分分析
最近算法的进步使得从大型队列中的基因组数据推断全基因组祖先重组图(ARG)成为可能。这些推断出的祖先重组图提供了沿基因组的谱系亲缘关系的详细表述,并通过捕捉未观察到的基因组变异的影响,对复杂性状分析中的基因型估算进行了补充。推断的 ARG 可用于构建遗传亲缘关系矩阵,该矩阵可在线性混合模型中用于分析复杂性状。然而,对于大型数据集来说,这些分析在计算上是不可行的。我们引入了一种计算效率高的方法,称为 ARG-RHE,利用 ARG 估算狭义遗传率并进行基于区域的关联测试。ARG-RHE 依靠可扩展的随机算法来估计方差成分并评估其统计意义,可并行应用于多个数量性状。我们进行了大量模拟,以验证这种方法的计算效率、统计能力和稳健性。然后,我们利用从英国生物库中 337,464 个个体的基因型数据中推断出的 ARG,将其用于检测 21,374 个基因与 52 个血液相关性状之间的关联。在这些分析中,将基于 ARG 的检测和基于估算的检测结合起来所产生的基因-性状关联比单独使用估算多 8%,这表明在复杂性状的分析中,推断出的全基因组谱系可以有效地补充基因型估算。
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