Print Email Facebook Twitter Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies Title Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies Author Niehues, Anna (Radboud University Medical Center) Bizzarri, Daniele (Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Slagboom, P. Eline (Leiden University Medical Center; Max Planck Institute for Biology of Ageing) van Gool, Alain J. (Radboud University Medical Center) van den Akker, E.B. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) 't Hoen, Peter A.C. (Radboud University Medical Center) Date 2022 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. Subject Clinical surrogatesExpression profilingMeta-analysisMetabolomicsMulti-omicsPopulation cohort studyPredictorsSurrogate outcomesSurrogatesTranscriptomics To reference this document use: http://resolver.tudelft.nl/uuid:d146aeb3-49df-4c9d-be42-e3c257a3d152 DOI https://doi.org/10.1186/s12864-022-08771-7 ISSN 1471-2164 Source BMC Genomics, 23 (1) Part of collection Institutional Repository Document type journal article Rights © 2022 , , Anna Niehues, Daniele Bizzarri, M.J.T. Reinders, P. Eline Slagboom, Alain J. van Gool, E.B. van den Akker, Peter A.C. 't Hoen Files PDF s12864_022_08771_7.pdf 1.2 MB Close viewer /islandora/object/uuid:d146aeb3-49df-4c9d-be42-e3c257a3d152/datastream/OBJ/view