Philippe Froguel

The paper develops a deep learning model combining clinical data, gene expression, and DNA methylation to distinguish NASH from steatosis and identifies new genes involved in liver disease severity.

The paper identifies genetic clusters underlying T2D and hypertension comorbidity using partitioned polygenic scores, improving risk prediction for individuals with heightened susceptibility.

This study identifies biallelic SREK1 variants causing a Prader-Willi-like syndrome with obesity and hypotonia in consanguineous children.

The paper introduces a framework for time-varying GWAS to identify age-varying genetic effects on BMI across childhood, finding 28 genome-wide significant variants, including a novel one in DAOA.

The paper identifies shared genetic regulation across mRNA, proteins, and metabolites in blood, revealing networks and mediators of GWAS variants.

This study shows that HDAC inhibition by trichostatin A disrupts β-cell function and identity through epigenome-wide remodeling in both mouse and human β-cell models.

The paper examines time-of-day-dependent transcriptome and metabolome variations in human NAFL and NASH livers, finding disruptions in rhythmic gene expression and metabolites.

The paper identifies hypermethylation of PNLIPRP1 in T2D patients and shows its association with acinar-to-ductal metaplasia and PDAC risk.

This study identifies genetic variants associated with glycaemic response to GLP-1 receptor agonists using observational and trial data, finding variants in GLP-1R and ARRB1 linked to HbA1c reduction.

This study examines DNA methylation changes linked to hyperglycaemia exposure in a longitudinal cohort over six years but finds no robust associations reaching FDR significance.

The paper links DNA methylation changes in HSCs from large for gestational age neonates to altered quiescence via transcriptional network disruption.

The paper identifies TSPAN8 variants associated with metabolomic regulation and metabolic diseases using CNV analysis and functional characterization in human induced pluripotent stem cells.

This study performs a GWAS on random glucose measurements in 493,036 individuals, identifying 128 loci associated with glycaemic traits and providing insights into diabetes biology and treatment.

The paper identifies age-related DNA methylation changes in human skeletal muscle through a meta-analysis and integrates these with transcriptomic and proteomic data, revealing enriched age-related gene expression patterns.

The paper examines how sex and underlying disease influence the genetic link between prolonged QT interval and sudden cardiac death risk using logistic regression and Mendelian randomization.

This study identifies three new susceptibility genes for calcific aortic valve stenosis through GWAS and TWAS, highlighting roles in inflammation, mineralization, and blood vessel integrity.

This study investigates the association between genetic variants in PMAT and OCT1 transporters and metformin gastrointestinal intolerance in type 2 diabetes patients.

The paper uses machine learning to predict glycaemic traits from multi-omics data in longitudinal cohorts, identifying key predictors like methylation marks and metabolic variables.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.