A highly adaptive microbiome-based association test for survival traits.
BMC Genomics
; 19(1): 210, 2018 03 20.
Article
in En
| MEDLINE
| ID: mdl-29558893
ABSTRACT
BACKGROUND:
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. 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.RESULTS:
We 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.CONCLUSIONS:
OMiSA 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.Key words
Full text:
1
Database:
MEDLINE
Main subject:
Computer Simulation
/
Genetic Markers
/
Computational Biology
/
Diabetes Mellitus, Type 1
/
High-Throughput Nucleotide Sequencing
/
Microbiota
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Animals
/
Humans
/
Male
Language:
En
Journal:
BMC Genomics
Journal subject:
GENETICA
Year:
2018
Type:
Article
Affiliation country:
United States