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This article is part of the supplement: Proceedings of the International Symposium on Animal Genomics for Animal Health (AGAH 2010)

Open Access Proceedings

Genome-wide identification of allele-specific expression (ASE) in response to Marek’s disease virus infection using next generation sequencing

Sean MacEachern12, William M Muir3, Seth Crosby4 and Hans H Cheng1*

Author Affiliations

1 USDA, ARS, Avian Disease and Oncology Laboratory (ADOL), East Lansing, MI 48823, USA

2 Current address: Cobb Vantress Inc., Siloam Springs, AR 72761, USA

3 Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA

4 Genome Sequencing Center, Washington University in St. Louis, St. Louis, MO 63108, USA

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BMC Proceedings 2011, 5(Suppl 4):S14  doi:10.1186/1753-6561-5-S4-S14

Published: 3 June 2011

Abstract

Background

Marek’s disease (MD), a T cell lymphoma induced by the highly oncogenic α-herpesvirus Marek’s disease virus (MDV), is the main chronic infectious disease concern threatening the poultry industry. Enhancing genetic resistance to MD in commercial poultry is an attractive method to augment MD vaccines, which is currently the control method of choice. In order to optimally implement this control strategy through marker-assisted selection (MAS) and to gain biological information, it is necessary to identify specific genes that influence MD incidence.

Methods

A genome-wide screen for allele-specific expression (ASE) in response to MDV infection was conducted. The highly inbred ADOL chicken lines 6 (MD resistant) and 7 (MD susceptible) were inter-mated in reciprocal crosses and half of the progeny challenged with MDV. Splenic RNA pools at a single time after infection for each treatment group point were generated, sequenced using a next generation sequencer, then analyzed for allele-specific expression (ASE). To validate and extend the results, Illumina GoldenGate assays for selected cSNPs were developed and used on all RNA samples from all 6 time points following MDV challenge.

Results

RNA sequencing resulted in 11-13+ million mappable reads per treatment group, 1.7+ Gb total sequence, and 22,655 high-confidence cSNPs. Analysis of these cSNPs revealed that 5360 cSNPs in 3773 genes exhibited statistically significant allelic imbalance. Of the 1536 GoldenGate assays, 1465 were successfully scored with all but 19 exhibiting evidence for allelic imbalance.

Conclusions

ASE is an efficient method to identify potentially all or most of the genes influencing this complex trait. The identified cSNPs can be further evaluated in resource populations to determine their allelic direction and size of effect on genetic resistance to MD as well as being directly implemented in genomic selection programs. The described method, although demonstrated in inbred chicken lines, is applicable to all traits in any diploid species, and should prove to be a simple method to identify the majority of genes controlling any complex trait.