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Understanding mechanisms underlying human gene expression variation with RNA sequencing

Abstract

Understanding the genetic mechanisms underlying natural variation in gene expression is a central goal of both medical and evolutionary genetics, and studies of expression quantitative trait loci (eQTLs) have become an important tool for achieving this goal1. Although all eQTL studies so far have assayed messenger RNA levels using expression microarrays, recent advances in RNA sequencing enable the analysis of transcript variation at unprecedented resolution. We sequenced RNA from 69 lymphoblastoid cell lines derived from unrelated Nigerian individuals that have been extensively genotyped by the International HapMap Project2. By pooling data from all individuals, we generated a map of the transcriptional landscape of these cells, identifying extensive use of unannotated untranslated regions and more than 100 new putative protein-coding exons. Using the genotypes from the HapMap project, we identified more than a thousand genes at which genetic variation influences overall expression levels or splicing. We demonstrate that eQTLs near genes generally act by a mechanism involving allele-specific expression, and that variation that influences the inclusion of an exon is enriched within and near the consensus splice sites. Our results illustrate the power of high-throughput sequencing for the joint analysis of variation in transcription, splicing and allele-specific expression across individuals.

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Figure 1: Annotating genes with RNA-Seq.
Figure 2: Loci affecting gene expression levels.
Figure 3: Loci affecting isoform expression.

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Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

Sequencing data have been deposited in Gene Expression Omnibus (GEO) under accession number GSE19480, and are also available at http://eqtl.uchicago.edu.

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Acknowledgements

We thank D. Gaffney, J. Bell, K. Bullaughey, Y. Guan and other members of the Pritchard, M. Przeworski and Stephens laboratory groups for helpful discussions, M. Domanus and P. Zumbo for sequencing support, and J. Zekos for computational assistance. J.F.D. and A.A.P. are supported by an NIH Training Grant to the University of Chicago. This work was supported by the HHMI and by NIH grants MH084703-01 to J.K. Pritchard and GM077959 to Y.G.

Author Contributions J.K. Pickrell performed most of the data analysis. J.C.M. contributed to the analysis of GC content and data normalizations and provided input on other aspects of data analysis. A.A.P. coordinated the cell culture and sequencing, and A.A.P. and E.N. prepared the sequencing libraries. The PCA-based normalization procedure was on the basis of results from J.-B.V., B.E.E. and M.S. J.F.D. provided software for the analysis of allele-specific expression. All authors participated in regular, detailed discussions of study design and data analysis at all stages of the study. The project was designed and supervised by Y.G. and J.K. Pritchard with regular input from M.S. The paper was written by J.K. Pickrell, Y.G. and J.K. Pritchard, with input from all authors.

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Correspondence to Joseph K. Pickrell, Yoav Gilad or Jonathan K. Pritchard.

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This file contains Supplementary Material including Supplementary Figures 1-19 with legends, Supplementary Tables 1-2, and Supplementary References. (PDF 1169 kb)

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Pickrell, J., Marioni, J., Pai, A. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010). https://doi.org/10.1038/nature08872

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