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Open Access Highly Accessed Methodology article

Allele-specific expression assays using Solexa

Bradley J Main1*, Ryan D Bickel1, Lauren M McIntyre2, Rita M Graze2, Peter P Calabrese1 and Sergey V Nuzhdin1

Author Affiliations

1 Section of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA

2 Departments of Molecular Genetics and Microbiology and Statistics, University of Florida, Gainesville, Florida 32610-1399, USA

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BMC Genomics 2009, 10:422  doi:10.1186/1471-2164-10-422

Published: 9 September 2009

Abstract

Background

Allele-specific expression (ASE) assays can be used to identify cis, trans, and cis-by-trans regulatory variation. Understanding the source of expression variation has important implications for disease susceptibility, phenotypic diversity, and adaptation. While ASE is commonly measured via relative fluorescence at a SNP, next generation sequencing provides an opportunity to measure ASE in an accurate and high-throughput manner using read counts.

Results

We introduce a Solexa-based method to perform large numbers of ASE assays using only a single lane of a Solexa flowcell. In brief, transcripts of interest, which contain a known SNP, are PCR enriched and barcoded to enable multiplexing. Then high-throughput sequencing is used to estimate allele-specific expression using sequencing counts. To validate this method, we measured the allelic bias in a dilution series and found high correlations between measured and expected values (r>0.9, p < 0.001). We applied this method to a set of 5 genes in a Drosophila simulans parental mix, F1 and introgression and found that for these genes the majority of expression divergence can be explained by cis-regulatory variation.

Conclusion

We present a new method with the capacity to measure ASE for large numbers of assays using as little as one lane of a Solexa flowcell. This will be a valuable technique for molecular and population genetic studies, as well as for verification of genome-wide data sets.