Meier Richard, Thompson Jeffrey A, Chung Mei, Zhao Naisi, Kelsey Karl T, Michaud Dominique S, Koestler Devin C
Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA.
Department of Public Health and Community Medicine, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA 02111, USA.
Stat Appl Genet Mol Biol. 2019 Nov 8;18(6):/j/sagmb.2019.18.issue-6/sagmb-2019-0027/sagmb-2019-0027.xml. doi: 10.1515/sagmb-2019-0027.
Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.
最近的研究发现，肠道和口腔中的微生物群与包括癌症在内的肠道疾病有关。如果能发现常驻微生物在口腔和肠道之间呈现出一致的模式，那么疾病状态就有可能通过对口腔样本进行分析来进行非侵入性评估。目前，尚无普遍适用的方法来检测此类关联。在此，我们提出了一个贝叶斯框架，用于识别在身体部位之间相对于一个表型变量呈现一致模式的微生物。对于给定的操作分类单元（OTU），使用贝叶斯回归模型来获得各层间丰度的马尔可夫链蒙特卡罗估计值，计算相关统计量，并基于其后验分布进行正式检验。广泛的模拟研究证明了该方法的总体可行性，并提供了有关影响其性能的因素的信息。将我们的方法应用于一个包含77名胰腺癌患者的口腔和肠道微生物群样本的数据集，发现了几个OTU在疾病亚型方面在肠道和口腔之间呈现出一致的模式。我们的方法对于中等样本量和中等关联强度具有良好的功效，并且可以使用任何当前已建立的贝叶斯分析程序灵活地扩展到其他研究环境。
