Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies
BMC Genomics, vol. 23, pp. 546
Abstract
Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.
Authors 7
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Radboud University Nijmegen · Radboud University Medical Center · Radboud Institute for Molecular Life Sciences
Affiliation as printed
Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Geert Grooteplein Zuid 26-28, Nijmegen, 6525 GA, Netherlands
Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands
Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Nijmegen, Netherlands
Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Nijmegen, Netherlands
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Leiden University Medical Center
Affiliation as printed
Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
Leiden Computational Biology Center, LUMC, Leiden, Netherlands
Molecular Epidemiology, LUMC, Leiden, Netherlands
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Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Delft Bioinformatics Lab, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE, Netherlands
Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
Delft Bioinformatics Lab, TU Delft, Delft, Netherlands
Leiden Computational Biology Center, LUMC, Leiden, Netherlands
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Leiden University Medical Center · Max Planck Institute for Biology of Ageing
Affiliation as printed
Max Planck Institute for the Biology of Ageing, Cologne, Germany
Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
Molecular Epidemiology, LUMC, Leiden, Netherlands
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Radboud University Nijmegen · Radboud University Medical Center
Affiliation as printed
Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands
Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Nijmegen, Netherlands
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Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Delft Bioinformatics Lab, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE, Netherlands
Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
Delft Bioinformatics Lab, TU Delft, Delft, Netherlands
Leiden Computational Biology Center, LUMC, Leiden, Netherlands
Molecular Epidemiology, LUMC, Leiden, Netherlands
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Peter A.C. ’t Hoen corresponding
Radboud University Nijmegen · Radboud University Medical Center · Radboud Institute for Molecular Life Sciences
Affiliation as printed
Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Geert Grooteplein Zuid 26-28, Nijmegen, 6525 GA, Netherlands
Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Nijmegen, Netherlands
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