Ana Viñuela

MetaboXcan predicts plasma metabolites from genetic data and associates them with complex traits, identifying disease risk genes and metabolites in chronic kidney disease.

This study identifies XBP1 expression in pancreatic islets as a genetic factor associated with poor glycaemic control and increased T2DM risk in South and East Asians, particularly in young non-obese onset diabetes.

The paper investigates how genetic variants affect gene expression in human pancreatic islets and identifies candidate genes linked to type 2 diabetes.

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.

This study develops machine learning models to predict liver fat content using multi-omic and clinical data from NAFLD patients, achieving higher accuracy than existing tools.

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 identifies smoking-associated DNA methylation and gene expression changes in adipose tissue and links some to cardio-metabolic health outcomes.

The paper quantifies age-related changes in gene expression levels, splicing, and variance across tissues and identifies environmental and genetic influences on these changes in a twin cohort.

The paper uses whole genome sequence and RNA-seq to predict causal variants affecting gene expression and identifies high confidence causal variants enriched in GWAS associations.

The paper uses pathway-based factor analysis on gene expression data to identify heritable phenotypes linked to age, finding 69 significant associations across KEGG pathways.

The paper uses RNA-seq and twin data to quantify gene-gene and gene-environment interactions affecting allelic specific expression.