Open Access Highly Accessed Methodology article

Genetic validation of whole-transcriptome sequencing for mapping expression affected by cis-regulatory variation

Tomas Babak1, Philip Garrett-Engele1, Christopher D Armour26, Christopher K Raymond26, Mark P Keller3, Ronghua Chen1, Carol A Rohl1, Jason M Johnson1, Alan D Attie3, Hunter B Fraser24* and Eric E Schadt25*

Author Affiliations

1 Merck Research Laboratories, Research Informatics, 33 Avenue Louis Pasteur, Boston, MA, 02115, USA

2 Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., 401 Terry Ave N, Seattle, WA, 98109, USA

3 University of Wisconsin-Madison, Department of Biochemistry, 433 Babcock Drive, Madison, WI, 53706, USA

4 Currently at Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA, 94305, USA

5 Currently at Pacific Biosciences, 1505 Adams Drive, Menlo Park, CA, 94025, USA

6 Currently at NuGEN Technologies, 201 Industrial Road, San Carlos, CA 94070, USA

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BMC Genomics 2010, 11:473  doi:10.1186/1471-2164-11-473

Published: 13 August 2010

Abstract

Background

Identifying associations between genotypes and gene expression levels using microarrays has enabled systematic interrogation of regulatory variation underlying complex phenotypes. This approach has vast potential for functional characterization of disease states, but its prohibitive cost, given hundreds to thousands of individual samples from populations have to be genotyped and expression profiled, has limited its widespread application.

Results

Here we demonstrate that genomic regions with allele-specific expression (ASE) detected by sequencing cDNA are highly enriched for cis-acting expression quantitative trait loci (cis-eQTL) identified by profiling of 500 animals in parallel, with up to 90% agreement on the allele that is preferentially expressed. We also observed widespread noncoding and antisense ASE and identified several allele-specific alternative splicing variants.

Conclusion

Monitoring ASE by sequencing cDNA from as little as one sample is a practical alternative to expression genetics for mapping cis-acting variation that regulates RNA transcription and processing.