José C. Florez

The paper identifies DNA methylation changes linked to cumulative hyperglycaemia in type 1 diabetes and links some to diabetic kidney disease through Mendelian randomization.

This paper develops and validates multi-ancestry polygenic risk scores for type 2 diabetes prediction and complications across diverse populations.

The paper analyzes over 800,000 human genomes to assess penetrance of clinically relevant variants, finding that 95% of pLoF variants in severe diseases have explanations for lack of disease manifestation.

The paper identifies genetic variants near SLC2A2 that influence glycemic response to glimepiride in type 2 diabetes patients.

The paper identifies DNA methylation biomarkers associated with diabetic kidney disease progression in type 1 diabetes using a prospective cohort study.

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 paper conducts a multi-ancestry time-to-event GWAS to identify genetic variants associated with incident cardiovascular disease in people with type 2 diabetes, finding three novel loci and confirming known CAD variants.

Rare-variant depletion in 1kb windows of 76,156 gnomAD genomes, versus feature-adjusted mutation expectations, marks regulatory elements and outperforms seven alternative scores at flagging functional non-coding variants.

The paper develops a method to infer compound heterozygosity from exome sequencing data, achieving high accuracy in phasing rare variants for recessive disease diagnosis.

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.

The paper identifies a gene-diet interaction modifying the association between macronutrient intake and glycemic traits, highlighting challenges in detecting such interactions due to measurement error and sample size.

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 performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

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

This study identifies novel genetic loci for BMI, height, and WHRadjBMI in Hispanic/Latino populations and shows the benefits of ancestral diversity in GWAS.

This study identifies 35 genes associated with circulating lipid levels using rare coding variants from 170,000 exomes across multiple ancestries.

This study shows that Mct11 deficiency in mice disrupts hepatic glucose and lipid metabolism, mirroring T2D features, and identifies MCT11 as a potential therapeutic target for T2D.

The paper identifies differentially methylated loci in individuals with end-stage kidney disease due to diabetic kidney disease, linking them to key pathways and genes.

This study applies an improved GWAS strategy to age-related diseases, identifying new genetic loci by using multiple reference panels, analyzing the X chromosome, and testing non-additive models.

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 five novel gene-diet interaction effects on HbA1c using UK Biobank data, highlighting interactions with dietary patterns and traits.

This paper introduces cov-LDSC, a method to estimate heritability and its enrichment in tissue-specific gene sets in admixed populations, showing high concordance across populations but population-specific differences for some traits.

The paper identifies rare non-coding variants from whole genome sequencing in the TOPMed program, showing they contribute significantly to type 2 diabetes heritability.

The paper evaluates potential drug targets using loss-of-function genetic variants from gnomAD, highlighting the importance of deep curation and targeted recall studies for drug discovery.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.