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Accounting for Unobserved Confounding to Reduce False Discoveries in Microbiome Research

Abstract Number:

2452 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Chih-Ting Yang (1), Meghan Shilts (2), Zhouwen Liu (2), Tebeb Gebretsadik (3), Christian Rosas-Salazar (2), Suman Das (2), Tina Hartert (2), Chris McKennan (4), Yu Shyr (2), Siyuan Ma (1)

Institutions:

(1) Vanderbilt University, N/A, (2) Vanderbilt University Medical Center, N/A, (3) Vanderbilt University, School of Medicine, N/A, (4) The University of Chicago, N/A

Co-Author(s):

Meghan Shilts  
Vanderbilt University Medical Center
Zhouwen Liu  
Vanderbilt University Medical Center
Tebeb Gebretsadik  
Vanderbilt University, School of Medicine
Christian Rosas-Salazar  
Vanderbilt University Medical Center
Suman Das  
Vanderbilt University Medical Center
Tina Hartert  
Vanderbilt University Medical Center
Chris McKennan  
The University of Chicago
Yu Shyr  
Vanderbilt University Medical Center
Siyuan Ma  
Vanderbilt University

First Author:

Chih-Ting Yang  
Vanderbilt University

Presenting Author:

Chih-Ting Yang  
N/A

Abstract Text:

Microbiome research often conducts differential abundance analysis (DA) to identify microbial features associated with covariates of interest. Recently, concerns with false discoveries from DA have increased, and related statistical research usually attributes this to compositionality (microbial abundances are relative). In this work, we examine another potential cause: unobserved, microbiome-wide confounding (e.g., population structures, unmeasured technical effects). Such effects, often ignored during DA, have been noted to inflate false discovery rates (FDR) in molecular epidemiology, where research shows low-dimensional factor structures of the data can act as surrogates for confounding and be adjusted for to control FDR. We demonstrate systemic, real-data-based evidence that unobserved confounding consistently inflates FDR in microbiome DA. However, existing factor-based correction methods with simple modifications can effectively address this. We implement such methods with open-source software, to be conveniently integrated with existing DA. Our work is one of the first efforts to evaluate and correct for unobserved confounding to control FDR in microbiome DA.

Keywords:

False discovery rate|Unobserved confounding|Microbiome|Differential abundance|Latent factor models|

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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