Andrew R. Marderstein

The paper reports on the All of Us multi-omics cohort, identifying 182,246 QTLs and demonstrating rare variant effects across proteomic and transcriptomic data.

The paper develops and validates a clinicogenomic risk model to predict second primary cancers in cancer survivors using genetic, clinical, and lifestyle factors.

The paper uses single-nucleus multiome sequencing to analyze gene regulatory changes in human skeletal muscle after endurance exercise, identifying fiber-type specific regulatory programs and transcription factor networks.

The paper predicts the regulatory effects of non-coding variants across brain and heart cellular contexts using deep learning, identifying rare variants with significant developmental impacts.

This study integrates single-cell multi-omics data from human foetal blood to show that trisomy restructures enhancer-gene maps, leading to dysregulated erythroid differentiation and haematological abnormalities in Down's Syndrome.

This study identifies gene-drug interactions that stratify breast cancer risk using polygenic scores and UK Biobank data, finding significant associations with corticosteroid use.

This paper develops a statistical framework to detect gene-environment interactions by focusing on variance-associated genetic variants, finding higher discovery rates for BMI and disease-related traits.

This paper evaluates the clinical impact of polygenic risk scores across 23 diseases using UK Biobank data, showing improved prediction accuracy and actionable risk mitigation strategies.

The paper analyzes bulk RNA-seq data to identify factors influencing immune cell infiltration in human tissues, finding associations with age, sex, and genetic variants.