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Evaluation of microbiome association models under realistic and confounded conditions

View ORCID ProfileJakob Wirbel, View ORCID ProfileMorgan Essex, View ORCID ProfileSofia Kirke Forslund, View ORCID ProfileGeorg Zeller
doi: https://doi.org/10.1101/2022.05.09.491139
Jakob Wirbel
1Structural and Computational Biology Unit (SCB), EMBL Heidelberg, Germany
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Morgan Essex
2Experimental and Clinical Research Center, a cooperation of the Max-Delbrück Center for Molecular Medicine and Charité-Universitätsmedizin, Berlin, Germany
3Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
4Charité-Universitätsmedizin Berlin, Berlin, Germany
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Sofia Kirke Forslund
1Structural and Computational Biology Unit (SCB), EMBL Heidelberg, Germany
2Experimental and Clinical Research Center, a cooperation of the Max-Delbrück Center for Molecular Medicine and Charité-Universitätsmedizin, Berlin, Germany
3Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
4Charité-Universitätsmedizin Berlin, Berlin, Germany
5German Center for Cardiovascular Research (DZHK), partner site Berlin
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  • For correspondence: sofia.forslund@mdc-berlin.de zeller@embl.de
Georg Zeller
1Structural and Computational Biology Unit (SCB), EMBL Heidelberg, Germany
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  • For correspondence: sofia.forslund@mdc-berlin.de zeller@embl.de
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Abstract

Testing for differential abundance is a crucial task in metagenome-wide association studies, complicated by technical or biological confounding and a lack of consensus regarding statistical methodology. Here, we developed a framework for benchmarking differential abundance testing methods based on implanting signals into real data. This strategy yields a ground truth for benchmarking while retaining the statistical characteristics of real metagenomic data, which we quantitatively validated in comparison to previous approaches. Our benchmark revealed dramatic issues with elevated false discovery rates or limited sensitivity for the majority of methods with the exception of limma, linear models and the Wilcoxon test. When additionally modeling confounders, we observed these issues to be exacerbated, but also that linear mixed-effect models or the blocked Wilcoxon test effectively address them. Exploratory analysis of cardiometabolic disease cohorts illustrates the confounding potential of medications and the need to consider confounders to prevent spurious associations in real-world applications.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 10, 2022.
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Evaluation of microbiome association models under realistic and confounded conditions
Jakob Wirbel, Morgan Essex, Sofia Kirke Forslund, Georg Zeller
bioRxiv 2022.05.09.491139; doi: https://doi.org/10.1101/2022.05.09.491139
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Evaluation of microbiome association models under realistic and confounded conditions
Jakob Wirbel, Morgan Essex, Sofia Kirke Forslund, Georg Zeller
bioRxiv 2022.05.09.491139; doi: https://doi.org/10.1101/2022.05.09.491139

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