OBJECTIVES Host genetics have been recently reported to affect human microbiome composition. We previously developed a statistical framework, microbiomeGWAS, to identify host genetic variants associated with microbiome composition by testing a distance matrix. However, statistical power depends on the choice of a microbiome distance matrix. To achieve more robust statistical power, we aim to extend microbiomeGWAS to test the association with many distance matrices, which are defined based on multilevel taxa abundances and phylogenetic information. METHODS The main challenge is to accurately and rapidly evaluate the significance for millions of SNPs. We propose methods for approximating p values by correcting for the multiple testing introduced by testing many distance matrices and by correcting for the skewness and kurtosis of score statistics. RESULTS The accuracy of p value approximation was verified by simulations. We applied our method to a set of 147 lung cancer patients with 16S rRNA microbiome profiles from nonmalignant lung tissues. We show that correcting for skewness and kurtosis eliminated dramatic deviations in the quantile-quantile plot. CONCLUSION We developed computationally efficient methods for identifying host genetic variants associated with microbiome composition by testing many distance matrices. The algorithms are implemented in the package microbiomeGWAS (https://github.com/lsncibb/microbiomeGWAS).

中文翻译：

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通过测试多个 Beta 多样性矩阵来识别与微生物组组成相关的宿主遗传变异。

目标 最近有报道称宿主遗传学会影响人类微生物组的组成。我们之前开发了一个统计框架 microbiomeGWAS，通过测试距离矩阵来识别与微生物组组成相关的宿主遗传变异。然而，统计功效取决于微生物组距离矩阵的选择。为了获得更强大的统计能力，我们的目标是扩展微生物组GWAS来测试与许多距离矩阵的关联，这些距离矩阵是根据多级分类群丰度和系统发育信息定义的。方法 主要挑战是准确、快速地评估数百万个 SNP 的重要性。我们提出了通过校正通过测试许多距离矩阵引入的多重测试以及通过校正分数统计的偏度和峰度来近似p值的方法。结果通过模拟验证了p值近似的准确性。我们将我们的方法应用于一组 147 名肺癌患者，这些患者具有来自非恶性肺组织的 16S rRNA 微生物组谱。我们证明，校正偏度和峰度消除了分位数-分位数图中的显着偏差。结论我们开发了计算有效的方法，通过测试许多距离矩阵来识别与微生物组组成相关的宿主遗传变异。这些算法在 microbiomeGWAS 包 (https://github.com/lsncibb/microbiomeGWAS) 中实现。