Sarah Urbut

Exposure accumulation drives age-dependent disease architectures and polygenic risk scores

The paper shows that environmental variance increases with age, reducing heritability and PRS accuracy for multiple traits and diseases, suggesting younger cohorts are better for risk prediction models.

Instability of high polygenic risk classification and mitigation by integrative scoring

The paper evaluates the instability of polygenic risk scores (PRS) across different publications and introduces an integrative scoring framework to improve classification consistency.

Aladynoulli: A Bayesian approach to disease progression modeling for genomic discovery and clinical prediction

ALADYNOULLI integrates longitudinal health records and genetic data to model disease progression, identifying interpretable disease signatures with cross-population consistency and improving clinical prediction.

Association between plausible genetic factors and weight loss from GLP1-RA and bariatric surgery: a multi-ancestry study in 10 960 individuals from 9 biobanks

This study investigates genetic factors influencing weight loss from GLP1-RA and bariatric surgery in 10,960 individuals across 9 biobanks, finding no strong genetic associations with GLP1-RA effectiveness but a modest link between BMI polygenic scores and reduced bariatric surgery weight loss.

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

Decoding Genetics, Ancestry, and Geospatial Context for Precision Health

The paper analyzes genetic, ancestral, and geospatial data from a biobank to explore their relationships with health outcomes in a historically diverse population.

Genomic and phenomic landscape of clonal hematopoiesis in over a million ancestrally diverse participants

This study analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions

The paper introduces flexible statistical methods for genomic studies with multiple conditions, improving power and effect estimates by accounting for arbitrary correlations in effect sizes.