Rocky 2022

Age and sex are historically understudied factors in biomedical studies even though many complex traits and diseases vary by these factors in their incidence and presentation. Hence, there is a critical need for analytical frameworks that can aid scientists in systematically bridging gaps in understanding age- and sex-specific genetic and molecular mechanisms. Hundreds of thousands of publicly-available gene expression profiles present an invaluable, yet untapped, opportunity for addressing this need. However, the bottleneck is that a vast majority of these profiles do not have age and sex labels. Therefore, we first ~30,000 samples associated with age and sex information and then trained machine learning (ML) models to predict these variables from gene expression values. Specifically, we trained one-vs-rest logistic regression classifiers with elastic-net regularization to classify transcriptome samples into age groups separately for females and males. Overall, the classifiers are able to discriminate between age-groups in a biologically meaningful way in each sex across technologies. The weights of these predictive models also serve as ‘gene signatures’ characteristic of different age groups in males and females. We also inferred genomewide sex-biased genes within each age group. Enrichment analysis of these gene signatures helped us identify age- and sex-associated multi-tissue and pan-body molecular phenomena (e.g. general immune response, inflammation, metabolism, hormone response). Our curated dataset, gene signatures, and enrichment results will be valuable resources to aid scientists in studying age- and sex-specific health and disease processes.