A highly adaptive microbiome-based association test for survival traits

Hyunwook Koh,Huilin Li,Alexandra E Livanos,Martin J Blaser

doi:10.1186/s12864-018-4599-8

Abstract

BackgroundThere has been increasing interest in discovering microbial taxa that are associated with human health or disease, gathering momentum through the advances in next-generation sequencing technologies. Investigators have also increasingly employed prospective study designs to survey survival (i.e., time-to-event) outcomes, but current item-by-item statistical methods have limitations due to the unknown true association pattern. Here, we propose a new adaptive microbiome-based association test for survival outcomes, namely, optimal microbiome-based survival analysis (OMiSA). OMiSA approximates to the most powerful association test in two domains: 1) microbiome-based survival analysis using linear and non-linear bases of OTUs (MiSALN) which weighs rare, mid-abundant, and abundant OTUs, respectively, and 2) microbiome regression-based kernel association test for survival traits (MiRKAT-S) which incorporates different distance metrics (e.g., unique fraction (UniFrac) distance and Bray-Curtis dissimilarity), respectively.ResultsWe illustrate that OMiSA powerfully discovers microbial taxa whether their underlying associated lineages are rare or abundant and phylogenetically related or not. OMiSA is a semi-parametric method based on a variance-component score test and a re-sampling method; hence, it is free from any distributional assumption on the effect of microbial composition and advantageous to robustly control type I error rates. Our extensive simulations demonstrate the highly robust performance of OMiSA. We also present the use of OMiSA with real data applications.ConclusionsOMiSA is attractive in practice as the true association pattern is unpredictable in advance and, for survival outcomes, no adaptive microbiome-based association test is currently available.

Highlights

There has been increasing interest in discovering microbial taxa that are associated with human health or disease, gathering momentum through the advances in next-generation sequencing technologies
Optimal microbiome-based association test (OMiAT) [14] is a further adaptive test which approximates to an optimal test adaptively throughout the sum of powered score tests (SPU) [20] and microbiome regression-based kernel association test (MiRKAT) tests
We introduce a new set of association tests, namely, microbiome-based survival analysis using linear and non-linear bases of Operational taxonomic unit (OTU) (MiSALN), which weigh rare, mid-abundant, and abundant OTUs, respectively, and its adaptive testing method, Optimal MiSALN (OMiSALN), to ensure a robust performance for OTUs with low or high abundance

Summary

Introduction

There has been increasing interest in discovering microbial taxa that are associated with human health or disease, gathering momentum through the advances in next-generation sequencing technologies. OMiSA approximates to the most powerful association test in two domains: 1) microbiome-based survival analysis using linear and non-linear bases of OTUs (MiSALN) which weighs rare, mid-abundant, and abundant OTUs, respectively, and 2) microbiome regression-based kernel association test for survival traits (MiRKAT-S) which incorporates different distance metrics (e.g., unique fraction (UniFrac) distance and Bray-Curtis dissimilarity), respectively. A majority of existing methods (e.g., LEfSe [10], STAMP [11], DESeq2 [12], and metagenomeSeq-fit Zig [13]) relate aggregated microbial abundance for each taxon with health or disease outcome [14] These methods are subject to a substantial loss of power as its underlying assumption - the same effect direction for all associated OTUs - is violated (e.g., within a taxon of interest, some OTUs are symbiotic, while others are pathogenic) [14]. By including SPU tests in the search space, OMiAT robustly discovers rare, mid-abundant, and abundant associated lineages along with the functionality of Optimal MiRKAT

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Discussion

Conclusion