Heather E. Wheeler

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

The paper analyzes expression and splicing quantitative trait loci (e/sQTL) across multiple tissues in the TOPMed dataset and finds that secondary e/sQTL signals are still discoverable, with many GWAS signals colocalizing with these e/sQTL.

This study improves proteome-wide association study results by using multi-ancestry data, finding more protein-phenotype associations and better prediction models.

The paper integrates genomics and transcriptomics data from human heart samples to identify regulatory mechanisms linking genetic variation to dilated cardiomyopathy.

This study shows that MASHR improves transcriptome prediction across populations, leading to more replicable discoveries in multi-ethnic transcriptome-wide association studies.

The paper quantifies similarity in causal effects across local ancestries in admixed individuals and finds high genetic correlation (radmix=0.95) within individuals.

The paper develops genetic predictors of plasma protein abundance in diverse populations and demonstrates their utility in proteome-wide association studies (PWAS) for complex traits.

This study develops polygenic transcriptome risk scores (PTRS) to improve the portability of polygenic risk scores across ancestries, showing better performance in African populations compared to traditional PRS.

This study conducts a multi-ethnic GWAS and PrediXcan analysis of prostate cancer, identifying significant gene-tissue pairs and SNPs in African American and Japanese American cohorts.

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

ANEVA quantifies genetic regulatory variation using allelic expression data, improving transcriptome analysis and aiding in rare disease diagnosis.

This study conducts GWAS and PrediXcan in African Americans to identify genetic associations with schizophrenia and bipolar disorder, finding PRMT7 linked to schizophrenia and a candidate locus for bipolar disorder.

The paper conducts GWAS and transcriptome-wide association studies in Hispanic cohorts to identify genetic regulators of lipid traits, finding significant gene-tissue-phenotype associations that replicate in other populations.

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

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 investigates genetic architecture of gene expression across diverse populations, finding that predictive performance varies due to allele frequency differences and highlighting the need for diverse population sampling.

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.