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Multiple-testing correction in metabolome-wide association studies

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Title: Multiple-testing correction in metabolome-wide association studies
Authors: Peluso, A
Glen, R
Ebbels, T
Item Type: Journal Article
Abstract: Background: The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction. Methods: Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets. Results: Both the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.
Issue Date: 12-Feb-2021
Date of Acceptance: 15-Jan-2021
URI: http://hdl.handle.net/10044/1/86950
DOI: 10.1186/s12859-021-03975-2
ISSN: 1471-2105
Publisher: BioMed Central
Journal / Book Title: BMC Bioinformatics
Volume: 22
Copyright Statement: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Sponsor/Funder: European Molecular Biology Laboratory
National Institutes of Health
Funder's Grant Number: 654241
RO1HL133932
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
Biochemistry & Molecular Biology
FWER
MWAS
MWSL
Multiple testing
Permutation
Correlated tests
Correlated tests
FWER
MWAS
MWSL
Multiple testing
Permutation
Genetic Markers
Metabolome
Metabolomics
Models, Biological
Statistical Distributions
Genetic Markers
Statistical Distributions
Models, Biological
Metabolomics
Metabolome
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status: Published
Article Number: ARTN 67
Appears in Collections:Department of Metabolism, Digestion and Reproduction



This item is licensed under a Creative Commons License Creative Commons