Eleftheria Zeggini

TRIDENT is a framework for robust multi-trait GWAS that identifies 66 novel osteoarthritis signals by combining earlier and larger GWAS meta-analyses and applying translational annotation.

The paper identifies diet-responsive cis-pQTLs in humans following short-term animal product restriction, revealing diet-dependent genetic regulation of proteins involved in metabolism and immunity.

This study identifies 20 genetic loci associated with knee alignment and shows genetic correlation with knee osteoarthritis, suggesting osteoarthritis may causally influence alignment.

This study links plasma proteome data to genetics in Ugandan individuals, identifying novel pQTLs and proteins causally associated with type 2 diabetes.

This study investigates causal links between T2D genetic clusters and non-cardiovascular comorbidities, identifying potential drug repurposing opportunities.

The paper identifies 38 high-confidence effector genes in osteoarthritis using molecular and functional genomics evidence from transcriptome analysis of primary tissues.

The paper identifies genetic regulators of 184 neuro-related proteins in human plasma and links them to disease traits and drug targets.

This study integrates multi-omics data to identify genetic variants and molecular mechanisms associated with type 2 diabetes and highlights potential drug targets.

This study identifies metabolites causally linked to type 2 diabetes and its complications, revealing genetic regulation effects and metabolic pathways involved.

HistoGWAS performs genome-wide association studies on histological traits using AI tools and variance component models, identifying four genome-wide significant loci in GTEx data.

This study investigates the genetic relationship between type 2 diabetes and schizophrenia, finding no strong causal link but identifying shared genomic loci and biological pathways.

The paper examines DNA methylation profiles in infrapatellar fat pad and blood from osteoarthritis patients, identifies mQTLs, and uses Mendelian randomization to explore disease mechanisms.

This study identifies genetic correlations and colocalizing signals between type 2 diabetes and osteoarthritis, highlighting shared pathways and effector genes.

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.

This study identifies genetic determinants of hip joint space width and finds they have opposing causal effects on hip osteoarthritis risk through distinct mechanisms.

The paper identifies genes involved in human bone disease by analyzing transcriptomic changes during zebrafish scale regeneration.

This study uses multivariable Mendelian randomization to show that higher bone mineral density causally increases osteoarthritis risk independently of body mass index.

The paper maps the osteocyte transcriptome in mice, identifies 1239 genes, and links them to skeletal disease susceptibility in humans.

The paper identifies 14 genes involved in osteoarthritis using mutant mice and validates their role through functional studies and genome editing.

The paper uses whole-genome sequencing to analyze the genetic architecture of 257 cardiometabolic protein biomarkers, identifying 131 sequence variant associations and constructing polygenic scores that explain up to 45% of protein variation.

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

This study identifies molecular subgroups of osteoarthritis and develops a 7-gene classifier to stratify patients, while uncovering potential therapeutic compounds.

This study identifies CASC20 and KIF26B as genetic loci associated with heterotopic ossification through genome-wide association and functional analyses.

This study assesses the transferability of lipid loci across African, Asian, and European populations, finding high consistency but also population-specific variations.

This paper calls copy number variants from whole-genome sequencing data of 6,898 individuals using a regression tree-based method, identifying 1,320 CNVs and their potential clinical impact.

This paper uses Mendelian Randomization to show that higher circulating sclerostin levels causally reduce bone mineral density and increase fracture risk.

This study expands the genetic landscape of COPD by identifying 82 risk loci, revealing associations with lung function, cell types, and comorbidities.

The paper identifies 279 new genetic signals for lung function across multiple ancestries, showing associations with COPD and pleiotropic effects.

This study identifies 64 new genetic signals for osteoarthritis using UK Biobank data, including 52 novel loci, and highlights potential therapeutic targets with existing drugs.

This study evaluates very low depth whole genome sequencing for complex trait association studies, showing it can efficiently discover association signals comparable to imputed GWAS chip designs.