Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.

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 pre

SUBMITTER: Niehues A 

PROVIDER: S-EPMC9339202 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature