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本文引用的文献

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MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition.宏基因组关联分析（MicrobiomeGWAS）：一种用于识别与微生物组组成相关的宿主遗传变异的工具。
Genes (Basel). 2022 Jul 9;13(7):1224. doi: 10.3390/genes13071224.
2
Host genetic variation impacts microbiome composition across human body sites.宿主基因变异会影响人体各部位的微生物组组成。
Genome Biol. 2015 Sep 15;16(1):191. doi: 10.1186/s13059-015-0759-1.
3
Investigation of the association between the fecal microbiota and breast cancer in postmenopausal women: a population-based case-control pilot study.绝经后女性粪便微生物群与乳腺癌之间关联的调查：一项基于人群的病例对照试点研究。
J Natl Cancer Inst. 2015 Jun 1;107(8). doi: 10.1093/jnci/djv147. Print 2015 Aug.
4
Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test.使用MiRKAT（基于微生物组回归的核关联测试）进行微生物组分析研究中的测试。
Am J Hum Genet. 2015 May 7;96(5):797-807. doi: 10.1016/j.ajhg.2015.04.003.
5
Diversity and composition of the adult fecal microbiome associated with history of cesarean birth or appendectomy: Analysis of the American Gut Project.与剖宫产或阑尾切除术史相关的成人粪便微生物群的多样性和组成：美国肠道计划分析
EBioMedicine. 2014 Dec 1;1(2-3):167-172. doi: 10.1016/j.ebiom.2014.11.004.
6
Complex host genetics influence the microbiome in inflammatory bowel disease.复杂的宿主遗传学影响炎症性肠病中的微生物组。
Genome Med. 2014 Dec 2;6(12):107. doi: 10.1186/s13073-014-0107-1. eCollection 2014.
7
Human genetics shape the gut microbiome.人类遗传学塑造了肠道微生物组。
Cell. 2014 Nov 6;159(4):789-99. doi: 10.1016/j.cell.2014.09.053.
8
Human gut microbiome and risk for colorectal cancer.人类肠道微生物组与结直肠癌风险。
J Natl Cancer Inst. 2013 Dec 18;105(24):1907-11. doi: 10.1093/jnci/djt300. Epub 2013 Dec 6.
9
Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing.质量过滤极大地提高了 Illumina 扩增子测序的多样性估计。
Nat Methods. 2013 Jan;10(1):57-9. doi: 10.1038/nmeth.2276. Epub 2012 Dec 2.
10
Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits.鼠类肠道微生物群由宿主遗传学定义，并调节代谢特征的变化。
PLoS One. 2012;7(6):e39191. doi: 10.1371/journal.pone.0039191. Epub 2012 Jun 18.

通过测试多个β多样性矩阵来鉴定与微生物组组成相关的宿主基因变异体。

Identifying Host Genetic Variants Associated with Microbiome Composition by Testing Multiple Beta Diversity Matrices.

作者信息

Hua Xing, Goedert James J, Landi Maria Teresa, Shi Jianxin

机构信息

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, Md., USA.

出版信息

Hum Hered. 2016;81(2):117-126. doi: 10.1159/000448733. Epub 2017 Jan 12.

DOI:10.1159/000448733
PMID:28076867
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6540989/
Abstract

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).

摘要

目标

最近有报道称宿主基因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——微生物组全基因组关联研究（microbiomeGWAS），通过测试距离矩阵来识别与微生物组组成相关的宿主基因变异。然而，统计效力取决于微生物组距离矩阵的选择。为了获得更强健的统计效力，我们旨在扩展微生物组全基因组关联研究，以测试与许多基于多级分类群丰度和系统发育信息定义的距离矩阵的关联。

方法

主要挑战在于准确且快速地评估数百万个单核苷酸多态性（SNP）的显著性。我们提出了通过校正测试多个距离矩阵所引入的多重检验以及校正得分统计量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。我们表明，校正偏度和峰度消除了分位数 - 分位数图中的显著偏差。

结论

我们开发了计算效率高的方法，通过测试多个距离矩阵来识别与微生物组组成相关的宿主基因变异。这些算法在微生物组全基因组关联研究软件包（https://github.com/lsncibb/microbiomeGWAS）中实现。