Aroon Dinesh Hingorani

This study identifies 17 genetic loci associated with sleep duration and lipid levels, offering potential druggable targets for dyslipidemia in individuals with sleep disturbances.

The paper introduces MR-Fish, a method using proteomics and Mendelian randomisation to identify causal relationships between proteins and diseases, finding causal links for several proteins associated with conditions like T2DM and CHD.

This study uses Mendelian randomization to investigate the causal role of lipids and statin interventions on multiple sclerosis risk and severity, finding HDL-C increases risk while RAC2 reduces it.

The paper uses proteogenomic integration to identify CXCL10 as a mediator of IL-6 signaling effects on atherosclerosis, showing its association with cardiovascular disease risk and lesion characteristics.

This study evaluates polygenic risk scores for population screening, individual risk prediction, and risk stratification, finding poor performance across all three applications.

The paper uses Mendelian randomization and clinical/biological evidence to prioritize lipid-modifying drug targets for coronary heart disease prevention.

The paper identifies aging mechanisms that predict age-related disease multimorbidities in humans using literature analysis and clinical data integration.

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

This study uses Mendelian randomization to identify 23 potential drug targeting mechanisms for Parkinson's disease, including four drug repurposing opportunities and two drugs that may increase PD risk.

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 evaluates how polygenic risk scores for coronary artery disease (CAD) relate to future events in individuals with and without CAD, finding attenuated or inverse associations in established cases.

This paper introduces a mathematical framework to improve Mendelian randomization analysis for drug targets by addressing horizontal pleiotropy when proteins are the risk factors.

The paper discusses how integrating druggable target genes with disease susceptibility genes can improve drug development success rates by reducing target specification errors.