Josée Dupuis

This study benchmarks four Bayesian colocalization methods to validate MR-identified protein-trait associations for cardiometabolic traits, finding SharePro most frequently supports significant associations.

MOLA identifies multiomic loops in datasets, showing better capture of epigenetic alterations in Influenza A virus data and associations with progression in breast cancer.

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.

This paper improves power to detect gene-environment interactions by adapting SharePro to account for genetic effect heterogeneity in fine-mapping.

RSparsePro improves fine-mapping accuracy in the presence of LD mismatch by using a probabilistic graphical model and variational inference, outperforming SuSiE in simulations and real applications.

The paper investigates the association of common and rare variants with Alzheimer's Disease in 13,371 diverse individuals using whole-genome sequencing, identifying significant genetic loci and population-specific associations.

SharePro improves genetic colocalization analysis by handling multiple causal signals, showing increased power and efficiency in simulations and real data.

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.

This study uses whole genome sequence data from the ADSP to identify 17 significant variants in five genomic regions associated with Alzheimer's disease.

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.

The paper analyzes inflammation biomarkers using whole-genome sequencing from the TOPMed consortium, identifying both single-variant and rare variant associations with inflammatory traits.

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.

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.

RAREsim simulates rare genetic variants using real data parameters and annotations to improve method evaluation for complex trait studies.

This study identifies eight novel genetic signals associated with asthma-COPD overlap through a genome-wide association analysis involving multiple cohorts.

This paper introduces FHAT and FHAT-O methods to incorporate family history in genetic association tests, showing improved power and type I error control in exome-wide analyses of dementia and hypertension.

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

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 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.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper systematically studies uORF variants in 15,708 genomes, showing they are under strong negative selection and contribute to disease.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

This paper introduces a unified method for detecting gene-environment interactions involving rare variants using a generalized linear mixed effect model, showing it maintains correct type I error and high power in simulations and replicates a known association.

The paper identifies genetic variants affecting HbA1c levels in multi-ancestry cohorts, showing ancestry-specific impacts on diabetes diagnosis via HbA1c.