Rodrigo Bonazzola

This study evaluates scRNA-seq foundation models for plant perturbation response prediction, finding that performance varies significantly with evaluation protocol and that preserving gene identity improves generalization.

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 assesses the impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx, finding that local ancestry adjustment improves power but has limited effect on colocalization probabilities.

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 improves causal gene identification and transcriptome prediction by integrating fine-mapping and QTL tissue-sharing information into transcriptomic imputation models.

The paper uses matrix factorization to analyze eQTLs across 49 human tissues, identifying tissue-specific and shared genetic regulation patterns and their potential transcription factor drivers.

GTEx v8 maps expression and splicing QTLs in 49 tissues from 838 donors: cis-eQTLs cover 94.7% of protein-coding genes, and cross-tissue QTL sharing mirrors cell-type composition.

The paper identifies trans-acting genes associated with complex traits by correlating cis-predicted gene expression with observed expression across tissues.

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.

This study identifies novel type 2 diabetes genes through integrative cross-tissue gene expression analysis using GWAS summary statistics.