Jennifer Wessel

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

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.

The paper estimates heritability for height and BMI using whole-genome sequence data and finds that rare variants contribute significantly to the missing heritability.

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 identifies genetic variants and transcripts associated with glycemic traits by analyzing exome-array data from 144,600 individuals, highlighting tissue-specific roles in glucose regulation.

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

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.

This study identifies 16 causal coding variants in type 2 diabetes using fine-mapping and shows that one third of signals are false leads.