Evidence map›Paper›PMID 36071064›Full record

SynthesisScientific reports2022

A resource for integrated genomic analysis of the human liver.

Yi-Hui Zhou, Paul J Gallins, Amy S Etheridge, Dereje Jima, Elizabeth Scholl, Fred A Wright, Federico Innocenti

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. SIEVEseq: unified differential expression, variability, and skewness analyses using RNA-Seq data.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Yi-Hui ZhouDepartment of Biological Sciences, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA. yihui_zhou@ncsu.edu.ORCID 0000-0002-4092-7463
Paul J GallinsBioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
Amy S EtheridgeDivision of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.
Dereje JimaBioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
Elizabeth SchollBioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
Fred A WrightDepartment of Biological Sciences, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
Federico InnocentiDivision of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA. Federico6061@protonmail.com.
North Carolina State University · USUniversity of North Carolina at Chapel Hill · US

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Hazel B Nichols · 2001 to 2026
$36.3M
Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Efstratios Pistikopoulos · 2017 to 2026
$21.2M
Chromatin regions, genes and pathways that confer susceptibility to chemical-induced DNA damageR01ES029911 · NIEHS · TEXAS A&M AGRILIFE RESEARCH · PI RUSYN, IVAN, THREADGILL, DAVID W. · 2019 to 2023
$3.3M
NIEHS NIH HHS P30 ES010126NIEHS NIH HHS P42 ES027704NIEHS NIH HHS R01 ES029911
6 · The paper itself

Abstract

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.

Indexed as

Genome-Wide Association StudyQuantitative Trait LociBayes TheoremGenomicsHumansLiverPolymorphism, Single Nucleotide

Identifiers

PMID36071064
PMCPMC9452507
OpenAlexW4294992659

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.