M. Grace Gordon

The paper introduces Decima, a model that predicts cell type- and condition-specific gene expression from DNA sequence using single-cell RNA sequencing data.

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

The paper introduces ember, an entropy-based framework to assess gene specificity in single-cell RNA sequencing data, revealing unexpected patterns in gene expression across tissues and mouse strains.

This paper introduces scITD, a method to identify multicellular gene expression patterns that distinguish disease individuals, showing its application in SLE and COVID-19 datasets.

The paper introduces MEOTIVE, a statistical framework to identify genetic effects on gene expression variability, revealing new mechanisms of genome regulation and associations with autoimmune disease risk.

This paper introduces SURGE, a method to discover context-specific eQTLs from single-cell RNA-seq data, showing its relevance in disease through colocalization analysis.

The paper uses deep mutational scanning in yeast to show that a toxic conformation of α-synuclein is stable under chemical perturbations, while mutations affecting toxicity are influenced by these perturbations.

The paper identifies genetic variants affecting chromatin accessibility in T cells and links them to gene expression and autoimmune disease.