Alvaro Barbeira

This study performs a multi-tissue splicing-TWAS to identify breast cancer susceptibility genes, finding 240 significant genes and highlighting the importance of splicing over expression.

This study performs a joint transcriptome-wide association study across multiple tissues to identify new candidate susceptibility genes for breast cancer, finding 380 genes at 129 loci with 22 remaining significant after conditioning on known variants.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

The paper introduces a scalable framework integrating total and allele-specific gene expression to improve detection of genetic effects on gene expression, showing a 29% power gain equivalent to increased sample size.

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.

PhenomeXcan integrates GWAS and transcriptomic data to identify gene-trait associations, providing a resource for translational research with biological context.

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.

This paper introduces PTWAS, a probabilistic TWAS framework that detects gene expression-trait associations and estimates tissue-specific causal effects using eQTL data and GWAS summary statistics.

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.

The paper identifies distinct regulatory patterns and trait associations of lncRNA genes compared to protein-coding genes across human tissues.

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.

This paper evaluates transcriptome-wide association studies (TWAS) for causal gene prioritization, showing its strengths and limitations in identifying causal genes at GWAS loci.

The paper introduces MulTiXcan, a method that improves association detection by integrating eQTL data across tissues, showing it identifies more significant genes than single-tissue analyses.

The paper identifies cancer-specific eQTLs by modeling genotype-tumor purity interactions in heterogeneous tumor gene expression data, showing most risk variants affect tumor microenvironment cells.

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.

LeafCutter quantifies RNA splicing variation from short-read RNA-seq data without transcript annotations, identifying more sQTLs and improving disease gene associations.

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