Evidence map›Paper›PMID 19825846›Full record

ArticleHuman molecular genetics2010

Genome-wide analysis of allelic expression imbalance in human primary cells by high-throughput transcriptome resequencing.

Graham A Heap, Jennie H M Yang, Kate Downes, Barry C Healy, Karen A Hunt, Nicholas Bockett, Lude Franke, Patrick C Dubois, Charles A Mein, Richard J Dobson and 6 more

Abstract read
In one paragraph

Article in Human molecular genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers.

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0cells of the map it votes in
119citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

119 citing papers in PubMed.

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  19. ASElux: an ultra-fast and accurate allelic reads counter.Bioinformatics (Oxford, England) · 2018
    Article
  20. Article

59 more citing papers are in PubMed but not listed here.

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

16 authors.

Graham A HeapCentre for Digestive Diseases, Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Jennie H M Yang
Kate Downes
Barry C Healy
Karen A Hunt
Nicholas Bockett
Lude Franke
Patrick C Dubois
Charles A Mein
Richard J Dobson
Thomas J Albert
Matthew J Rodesch
David G Clayton
John A Todd
David A van Heel
Vincent Plagnol

Funding

Medical Research Council G0700545Wellcome Trust 061858Wellcome Trust 079895Wellcome Trust WT084743MA
6 · The paper itself

Abstract

Many disease-associated variants identified by genome-wide association (GWA) studies are expected to regulate gene expression. Allele-specific expression (ASE) quantifies transcription from both haplotypes using individuals heterozygous at tested SNPs. We performed deep human transcriptome-wide resequencing (RNA-seq) for ASE analysis and expression quantitative trait locus discovery. We resequenced double poly(A)-selected RNA from primary CD4(+) T cells (n = 4 individuals, both activated and untreated conditions) and developed tools for paired-end RNA-seq alignment and ASE analysis. We generated an average of 20 million uniquely mapping 45 base reads per sample. We obtained sufficient read depth to test 1371 unique transcripts for ASE. Multiple biases inflate the false discovery rate which we estimate to be approximately 50% for random SNPs. However, after controlling for these biases and considering the subset of SNPs that pass HapMap QC, 4.6% of heterozygous SNP-sample pairs show evidence of imbalance (P < 0.001). We validated four findings by both bacterial cloning and Sanger sequencing assays. We also found convincing evidence for allelic imbalance at multiple reporter exonic SNPs in CD6 for two samples heterozygous at the multiple sclerosis-associated variant rs17824933, linking GWA findings with variation in gene expression. Finally, we show in CD4(+) T cells from a further individual that high-throughput sequencing of genomic DNA and RNA-seq following enrichment for targeted gene sequences by sequence capture methods offers an unbiased means to increase the read depth for transcripts of interest, and therefore a method to investigate the regulatory role of many disease-associated genetic variants.

Indexed as

Genome-Wide Association StudyAllelesAllelic ImbalanceBase PairingBiasCells, CulturedComputational BiologyDiseaseEpigenesis, GeneticFalse Positive ReactionsGene Expression ProfilingGenetic LociHeterozygoteHigh-Throughput Screening AssaysHumansPolymorphism, Single NucleotideRNA, Messenger

Identifiers

PMID19825846
PMCPMC2792152

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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 OpenQuestion graph.