Chris Finan

The paper integrates multi-modal omics data to identify therapeutic pathways in atherosclerosis for coronary heart disease, highlighting amino acid metabolism and 16 relevant proteins.

This paper identifies and prioritizes drug targets for major depressive disorder using genetic methods, finding that 82% of approved depression/anxiety drugs have significant genetic targets.

This study identifies 80 genetic loci and 61 effector genes associated with dilated cardiomyopathy through GWAS and integrates transcriptomic data to uncover biological pathways and cellular states involved in the disease.

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.

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.

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.

The paper analyzes genetic variants at the PCSK9 locus to assess their phenotypic effects on lipid levels and disease outcomes, finding concordance with PCSK9 inhibitor trials for myocardial infarction but discordance for type 2 diabetes.

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

This study conducts a large GWAS on heart failure, identifying 12 genetic loci with associations to CAD, atrial fibrillation, and reduced left ventricular function, and provides causal evidence for several risk factors.

This paper uses transcriptomic imputation to identify gene-tissue associations linked to Anorexia Nervosa risk, finding significant links in both brain and metabolic tissues.

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

The paper connects GWAS loci to druggable genes, compounds, and clinical indications to identify drug development opportunities and design a genotyping array for druggable genome-wide association studies.