A resource for integrated genomic analysis of the human liver
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA742971 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15013500_1.fastq.gz | Fastqsanger.gz | |||
| SRR15013500_2.fastq.gz | Fastqsanger.gz | |||
| SRR15013600_1.fastq.gz | Fastqsanger.gz | |||
| SRR15013600_2.fastq.gz | Fastqsanger.gz | |||
| SRR15013501_1.fastq.gz | Fastqsanger.gz |
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Project description: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) and previous liver eQTL (microarray) 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.
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