Ayellet V. Segrè

The paper uses AI-derived retinal thickness phenotypes from 2D images to identify novel genetic loci for neurodegenerative diseases.

This study performs genome-wide association meta-analyses on optic nerve structure biomarkers, identifying novel loci and potential drug targets independent of intraocular pressure.

This study identifies glucocorticoid-induced transcriptomic changes in human trabecular meshwork and Schlemm's canal cells using RNA-seq, finding genes associated with glaucoma and intraocular pressure.

REECAP uses contrastive learning to link retinal aging patterns with genetic loci associated with eye diseases, identifying 178 loci including 27 linked to age-related eye diseases.

The paper identifies a metabolomic signature of resilience to glaucoma in high-risk individuals and validates pyruvate's protective role in a mouse model.

This paper introduces a tissue-aware machine learning framework to improve understanding and diagnosis of Mendelian and rare diseases by leveraging tissue-specific gene mechanisms.

ECLIPSER identifies causal cell types and genes for complex traits by integrating GWAS loci with single-cell RNA-seq data through e/sQTL mapping and enrichment analysis.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper evaluates methods for detecting differential gene expression, differential transcript expression, and alternative splicing using internal synthetic controls in RNA-seq data.

This study analyzes RNA-seq data from 6,700 normal tissues to identify somatic mutations and their association with age and tissue proliferation, finding tissue-specific cancer mutation hotspots.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

The paper analyzes tissue-specific gene expression and splicing across 8527 RNA-seq samples to identify therapeutic targets and prioritize disease-associated genes.

The paper develops a method to infer allelic heterogeneity in GWAS and eQTL datasets, identifying 4152 loci with strong evidence of AH across different traits.