Princy Parsana

The paper aggregates RNA-seq data to infer consensus and tissue-specific gene co-expression networks, finding that larger sample sizes improve network quality and trait heritability annotations.

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.

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 identifies brain-wide and region-specific gene co-expression modules using RNA-seq data and links them to disease risk variants and biological processes.

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.

This study shows that removing top principal components from gene expression data improves gene co-expression network reconstruction by reducing false discoveries, using GTEx data and simulated examples.

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 examines how the X chromosome and sex influence gene regulation and disease by analyzing gene expression and regulatory variation in whole blood from 922 individuals.