One of the major research questions regarding human microbiome studies is the feasibility of designing interventions that modulate the composition of the microbiome to promote health and to cure disease. This requires extensive understanding of the modulating factors of the microbiome, such as dietary intake, as well as the relation between microbial composition and phenotypic outcomes, such as body mass index (BMI). Previous efforts have modeled these data separately, employing two-step approaches that can produce biased interpretations of the results. Here, we propose a Bayesian joint model that simultaneously identifies clinical covariates associated with microbial composition data and predicts a phenotypic response using information contained in the compositional data. Using spike-and-slab priors, our approach can handle high-dimensional compositional as well as clinical data. Additionally, we accommodate the compositional structure of the data via balances and overdispersion typically found in microbial samples. We apply our model to understand the relations between dietary intake, microbial samples and BMI. In this analysis we find numerous associations between microbial taxa and dietary factors that may lead to a microbiome that is generally more hospitable to the development of chronic diseases, such as obesity. Additionally, we demonstrate on simulated data how our method outperforms two-step approaches and also present a sensitivity analysis.

中文翻译：

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用于同时识别协变量关联和表型结果的微生物组数据的贝叶斯模型

关于人类微生物组研究的主要研究问题之一是设计干预措施的可行性，这些干预措施可调节微生物组的组成以促进健康和治愈疾病。这需要对微生物组的调节因素（例如饮食摄入）以及微生物组成与表型结局（例如体重指数（BMI））之间的关系有广泛的了解。先前的工作已经采用两步方法分别对这些数据进行建模，这可能会产生对结果的偏见。在这里，我们提出了一种贝叶斯联合模型，该模型同时识别与微生物成分数据相关的临床协变量，并使用成分数据中包含的信息预测表型反应。使用先验先验的先验，我们的方法可以处理高维成分和临床数据。此外，我们通过通常在微生物样品中发现的平衡和过度分散来适应数据的组成结构。我们应用我们的模型来了解饮食摄入，微生物样本和BMI之间的关系。在此分析中，我们发现微生物分类群与饮食因素之间存在许多关联，这些关联可能会导致通常更适合于肥胖等慢性疾病发展的微生物组。此外，我们在模拟数据上演示了我们的方法胜过两步法，并且还提供了灵敏度分析。我们应用我们的模型来了解饮食摄入，微生物样本和BMI之间的关系。在此分析中，我们发现微生物分类群与饮食因素之间存在许多关联，这些关联可能会导致通常更适合于肥胖等慢性疾病发展的微生物组。此外，我们在模拟数据上演示了我们的方法胜过两步法，并且还提供了灵敏度分析。我们应用我们的模型来了解饮食摄入，微生物样本和BMI之间的关系。在此分析中，我们发现微生物分类群与饮食因素之间存在许多关联，这些关联可能会导致通常更适合于肥胖等慢性疾病发展的微生物组。此外，我们在模拟数据上演示了我们的方法胜过两步法，并且还提供了灵敏度分析。