Motivation Most existing microbiome association analyses focus on the association between microbiome and conditional mean of health or disease-related outcomes, and within this vein, vast computational tools and methods have been devised for standard binary or continuous outcomes. However, these methods tend to be limited either when the underlying microbiome-outcome association occurs somewhere other than the mean level, or when distribution of the outcome variable is irregular (e.g. zero-inflated or mixtures) such that conditional outcome mean is less meaningful. We address this gap by investigating association analysis between microbiome compositions and conditional outcome quantiles. Results We introduce a new association analysis tool named MiRKAT-IQ within the Microbiome Regression-based Kernel Association Test framework using Integrated Quantile regression models to examine the association between microbiome and the distribution of outcome. For an individual quantile, we utilize the existing kernel machine regression framework to examine the association between that conditional outcome quantile and a group of microbial features (e.g. microbiome community compositions). Then, the goal of examining microbiome association with the whole outcome distribution is achieved by integrating all outcome conditional quantiles over a process, and thus our new MiRKAT-IQ test is robust to both the location of association signals (e.g. mean, variance, median) and the heterogeneous distribution of the outcome. Extensive numerical simulation studies have been conducted to show the validity of the new MiRKAT-IQ test. We demonstrate the potential usefulness of MiRKAT-IQ with applications to actual biological data collected from a previous microbiome study. Availability and implementation R codes to implement the proposed methodology is provided in the MiRKAT package, which is available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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使用集成分位数回归模型测试微生物组关联

动机大多数现有的微生物组关联分析侧重于微生物组与健康或疾病相关结果的条件平均值之间的关联，在这种情况下，已经为标准二元或连续结果设计了大量的计算工具和方法。然而，当潜在的微生物组-结果关联发生在平均水平以外的某个地方，或者当结果变量的分布不规则（例如零膨胀或混合）时，这些方法往往会受到限制，从而条件结果平均值的意义不大。我们通过研究微生物组组成和条件结果分位数之间的关联分析来解决这一差距。结果我们在基于微生物组回归的核关联测试框架中引入了一种名为 MiRKAT-IQ 的新关联分析工具，使用集成分位数回归模型来检查微生物组与结果分布之间的关联。对于单个分位数，我们利用现有的内核机器回归框架来检查该条件结果分位数与一组微生物特征（例如微生物群落组成）之间的关联。然后，通过整合一个过程中的所有结果条件分位数来实现检查微生物组与整个结果分布的关联的目标，因此我们的新 MiRKAT-IQ 测试对于关联信号的位置（例如均值、方差、中值）都具有鲁棒性以及结果的异质分布。广泛的数值模拟研究已证明新的 MiRKAT-IQ 测试的有效性。我们展示了 MiRKAT-IQ 的潜在用途，适用于从之前的微生物组研究中收集的实际生物数据。可用性和实施​​ MiRKAT 包中提供了用于实施所提议方法的 R 代码，该包可在 CRAN 上获取。补充信息 补充数据可在生物信息学在线获取。