Miriam S. Udler

The paper examines how social determinants of health modify polygenic risk scores for cardiometabolic diseases and shows that integrating social and genetic factors improves disease prediction.

The paper decomposes polygenic risk of coronary artery disease into distinct biological processes using latent factor decomposition of genetic variants.

The Rare Genomes Project provides remote genomic sequencing to rare disease families, achieving a 23.3% diagnostic solve rate and highlighting the role of research in overcoming clinical diagnostic barriers.

The paper develops a pipeline to compare polygenic scores (PGS) for detecting genetic interactions between adiposity and cardiometabolic risk factors, finding that interaction-based PGS (iPGS) most consistently identifies these interactions.

The paper identifies ancestry-specific T2D risk variants in South Asian populations through exome-wide association study and functional validation.

The paper investigates why carriers of pathogenic variants in monogenic metabolic conditions show variable disease severity, finding support for variant effect sizes, polygenic backgrounds, and epistasis.

The paper finds that functional variants in TAS2R38 associate with lower postprandial glucose levels, independent of dietary and lifestyle factors.

The paper identifies plasma proteomic markers linked to polygenic risk of type 2 diabetes and related comorbidities using proteogenomic data from biobanks and clinical trials.

The paper conducts a large-scale rare variant GWAS meta-analysis of type 2 diabetes, identifying novel variants in monogenic diabetes genes and assessing their pathogenicity.

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 genetic subtypes of type 2 diabetes across diverse ancestries, revealing ancestry-specific risk profiles and BMI thresholds.

The paper introduces LipocyteProfiler, a high-throughput microscopy assay to profile lipid-storing cells and identifies cellular programs linked to metabolic traits like insulin resistance and lipodystrophy.

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

This paper develops an automated pipeline to collect, unify, and translate genetic variants associated with monogenic diabetes into protein sequences, creating a database for proteomics and structural biology analysis.

This paper clusters T2D genetic loci into ten groups using bNMF, identifying distinct biological mechanisms and clinical associations.

The paper develops a polygenic score incorporating rare and common variants to improve diabetes diagnosis using HbA1C levels, identifying more undiagnosed cases than chance.

The paper identifies variance-quantitative trait loci (vQTLs) and gene-environment interactions (GEIs) for cardiometabolic serum biomarkers using UK Biobank data.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This study compares genome sequencing with standard-of-care genetic testing, finding similar diagnostic yields but longer turnaround times for genome sequencing.

This study identifies genetic loci associated with daytime napping and finds potential causal links between napping and cardiometabolic traits.

The paper identifies LOC157273 as a negative regulator of PPP1R3B and glycogen deposition in human hepatocytes, linking it to type 2 diabetes risk.

This study identifies 96 genome-wide significant loci associated with dietary intake variation using a multi-trait GWAS meta-analysis across 283,119 individuals.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.

The paper investigates the impact of rare protein truncating variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals, finding increased risk for several disorders and associations with other phenotypes.

This paper clusters T2D genetic loci using multi-trait associations to identify disease mechanisms and subtypes, finding distinct clinical outcomes linked to each cluster.

The paper reanalyzes T2D GWAS data to identify a rare X chromosome variant associated with increased T2D risk in males.