Skip Navigation
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Your Environment. Your Health.

Texas A&M University: Dataset Details, ID=GSE179250

Superfund Research Program

Data Management and Analysis Core

Project Leader: Efstratios N. Pistikopoulos
Grant Number: P42ES027704
Funding Period: 2022-2027
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

Project-Specific Links

Connect with the Grantees

Visit the grantee's eNewsletter page Visit the grantee's Twitter page Visit the grantee's Facebook page

Title: A resource for integrated genomic analysis of the human liver

Accession Number: GSE179250

Link to Dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179250

Repository: Gene Expression Omnibus (GEO)

Data Type(s): Gene Expression

Experiment Type(s): Expression profiling by high throughput sequencing

Organism(s): Homo sapiens

Summary: In this study, we generated whole-transcriptome RNA-Seq from n=192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq; PMID: 32913098) and previous liver eQTL (microarray; GSE32504, GSE25935, GSE9588, and GSE24293) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.

Publication(s) associated with this dataset:
Back
to Top