Emanuele Di Angelantonio

This study expands the human plasma proteome analysis using an enhanced SomaScan platform, identifying new genetic associations and disease mechanisms beyond the classical secretome.

This study uses 3D diffusion autoencoders to derive latent cardiac MRI phenotypes, finds they share a genetic basis with cardiometabolic traits, and shows predictive power for disease risk.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

SAFE-LD estimates linkage disequilibrium from GWAS summary statistics using pseudo-genotypes that preserve in-sample LD while protecting privacy.

This study identifies metabolic reaction fluxes that interact with CAD risk alleles, revealing their role as amplifiers or buffers in disease susceptibility.

This study identifies genetic determinants of blood gene expression and splicing, linking them to molecular phenotypes and health outcomes through QTL analysis and colocalization.

This study evaluates the cost-effectiveness of polygenic risk score-stratified screening for abdominal aortic aneurysm compared to current strategies.

The paper identifies misexpression of inactive genes in whole blood and links it to nearby rare structural variants using RNA and genome sequencing data.

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 develops genetically personalised organ-specific metabolic models to identify associations between metabolic fluxes and disease risk, finding 97 fluxes linked to coronary artery disease.

The paper reports LTL measurements in 474,074 UK Biobank participants, identifying factors like age, sex, ethnicity, and white cell count associated with LTL variation.

The paper uses polygenic scores to identify plasma proteins causally linked to cardiometabolic diseases and finds some are targets for existing therapies.

This study uses Mendelian randomization to identify plasma proteins associated with stroke risk and potential therapeutic targets.

This study uses Mendelian randomization to examine if ACE inhibitors or cardiometabolic risk factors affect lung ACE2 and TMPRSS2 expression or plasma ACE2 levels, finding no overall association.

The paper characterizes the genetic structure of South Asian populations, identifies founder effects and high homozygosity, and develops a custom SNP array for genetic studies in the region.

This study identifies 5,552 blood-cell trait-variant associations across 746,667 individuals from 5 global populations, including 71 novel loci and ancestry-specific variants not found in European ancestry populations.

This study identifies 5,106 new genetic variants associated with blood cell traits using large-scale GWAS, highlighting the polygenic and monogenic basis of hematopoiesis.

The paper identifies novel gene-trait associations and pathways involving rare variants in metabolic biomarkers linked to cardiovascular disease risk.

This study compares machine learning and univariate methods for constructing polygenic scores for 26 blood cell traits using UK Biobank and INTERVAL data, showing improved performance with learning methods.

This study performs GWAS on blood cell flow-cytometry phenotypes, identifies variant-trait associations, and links them to cellular structures and disease mechanisms.

The paper examines the effects of CETP and HMGCR genetic inhibition on lipoprotein profiles and CHD risk, finding distinct impacts on cholesterol and triglyceride compositions.

This study identifies genetic loci associated with household income and links them to biological pathways and cognitive ability, showing genetic differences between income and education.

This study evaluates how adding polygenic risk scores and clinical biochemistry markers to conventional CVD risk models improves prediction accuracy and informs statin therapy decisions in a UK primary care setting.

This study identifies 15 common and 9 low-frequency protein-coding variants associated with body fat distribution, implicating novel genes related to lipid homeostasis and type 2 diabetes.

The paper analyzes rare coding variants in smoking and drinking behavior using exome chip data, finding that they contribute to phenotypic variation but require larger samples for individual variant associations.

This paper develops a genomic risk score for coronary artery disease using 1.7 million variants and tests it in UK Biobank, showing it outperforms conventional risk factors in predicting CAD risk.