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Exon-Specific QTLs Skew the Inferred Distribution of Expression QTLs Detected Using Gene Expression Array Data

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Title
Exon-Specific QTLs Skew the Inferred Distribution of Expression QTLs Detected Using Gene Expression Array Data
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0030629
Pubmed ID
Authors

Jean-Baptiste Veyrieras, Daniel J. Gaffney, Joseph K. Pickrell, Yoav Gilad, Matthew Stephens, Jonathan K. Pritchard

Abstract

Mapping of expression quantitative trait loci (eQTLs) is an important technique for studying how genetic variation affects gene regulation in natural populations. In a previous study using Illumina expression data from human lymphoblastoid cell lines, we reported that cis-eQTLs are especially enriched around transcription start sites (TSSs) and immediately upstream of transcription end sites (TESs). In this paper, we revisit the distribution of eQTLs using additional data from Affymetrix exon arrays and from RNA sequencing. We confirm that most eQTLs lie close to the target genes; that transcribed regions are generally enriched for eQTLs; that eQTLs are more abundant in exons than introns; and that the peak density of eQTLs occurs at the TSS. However, we find that the intriguing TES peak is greatly reduced or absent in the Affymetrix and RNA-seq data. Instead our data suggest that the TES peak observed in the Illumina data is mainly due to exon-specific QTLs that affect 3' untranslated regions, where most of the Illumina probes are positioned. Nonetheless, we do observe an overall enrichment of eQTLs in exons versus introns in all three data sets, consistent with an important role for exonic sequences in gene regulation.

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Geographical breakdown

Country Count As %
United States 4 8%
Norway 1 2%
Canada 1 2%
Unknown 43 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 35%
Researcher 15 31%
Student > Bachelor 4 8%
Professor 3 6%
Student > Postgraduate 3 6%
Other 5 10%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 73%
Biochemistry, Genetics and Molecular Biology 5 10%
Medicine and Dentistry 2 4%
Mathematics 1 2%
Social Sciences 1 2%
Other 1 2%
Unknown 3 6%