Maik Pietzner

The paper uses genome-wide association testing on embeddings from a medical-history foundation model to identify genetic signals linked to biological pathways and drug targets.

The paper characterizes regulatory variants in human neurons using lentiMPRA and shows that variant effects are influenced by regulatory element activity and sequence context, not population frequency.

This study integrates genetic variation with plasma protein measurements in South Asians to identify pQTLs and build a proteogenomic disease network for understudied populations.

This study examines the proteomic and clinical impact of human knockouts in British South Asians, identifying significant protein under- and over-expression and developing a machine learning model to predict variant benignity.

The paper uses nanoparticle enriched mass spectrometry proteomics in British South Asians to identify novel variant-protein-disease mechanisms through integrative analysis of exome sequencing and proteomic data.

The paper identifies 679 diseases associated with increased risk for severe COVID-19 and Long COVID using UK Biobank health records, highlighting complex multimorbidity patterns and genetic risk loci.

This study identifies 40 circulating proteins associated with cancer risk using MR and colocalization, highlighting potential therapeutic targets and adverse effects of protein-altering interventions.

This study identifies damaging LXRα mutations linked to reduced triglycerides but increased liver enzyme levels and hepatotoxicity, suggesting LXR's role in liver health and cautioning against its inhibition for MASLD treatment.

The paper investigates sex differences in the genetic regulation of the plasma proteome and finds most genetic effects are similar between sexes, with few sex-differential effects that do not translate to disease risk.

This study identifies novel genetic risk variants and potential drug targets for heart failure using GWAS and Mendelian randomization proteomics.

This study uses GWAS and Mendelian randomization to show that lower sclerostin levels increase risk of hypertension, type 2 diabetes, myocardial infarction, and atherogenic lipid profiles.

The paper develops genetic scores for 17,227 molecular traits using multi-omic data from INTERVAL and validates them across ancestries, demonstrating their utility in pathway analysis and disease association discovery.

The paper uses antibody-based proteomics to identify disease-causing genes and proteins by analyzing plasma proteome data from 1,180 individuals, finding new gene-protein-disease links and improving causal gene assignment in GWAS loci.

This study identifies ELF5 as a risk gene for severe COVID-19 using genetic and single-cell expression data, showing its specific expression in respiratory epithelial cells.

The paper identifies molecular and metabolic signatures of cardiometabolic syndrome using extreme phenotype groups and shows changes after bariatric surgery.

This study identifies IL-18 as a biomarker for reduced lung cancer risk using genetic, pre-diagnostic, and tumor expression data, with potential therapeutic implications.

The paper compares pQTLs across two proteomic platforms, finding shared and platform-specific pQTLs and identifying factors contributing to platform differences.

This study identifies OAS1 as a protective factor against COVID-19 severity and susceptibility using Mendelian randomization and case-control analyses.

The paper integrates genomic and proteomic data to identify genetic variants influencing host proteins interacting with SARS-CoV-2, revealing potential drug targets and disease mechanisms.

This study identifies genetic associations with metabolite levels across multiple cohorts and links these variants to clinical outcomes like type 2 diabetes and macular telangiectasia type 2.

This study identifies 11 genetic loci associated with salivary metabolites in 1,419 individuals, revealing novel saliva-specific associations and potential clinical applications.