|MACEACHERN, SEAN - US Department Of Agriculture (USDA)|
|MUIR, WILLIAM - Purdue University|
|CROSBY, SETH - Washington University|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2010
Publication Date: 10/18/2010
Citation: MacEachern, S., Muir, W.M., Crosby, S., Cheng, H.H. 2010. Comprehensive genome-wide screen for genes with cis-acting regulatory elements that respond to Marek's disease virus infection [abstract]. In: 5th International Workshop on the Molecular Pathogenesis of Marek's Disease Virus and 1st Symposium on Avian Herpesviruses, October 17-20, 2010, Athens, Georgia, p. 87.
Technical Abstract: The comprehensive identification of genes underlying phenotypic variation of complex traits such as disease resistance remains one of the greatest challenges in biology despite having genome sequences and more powerful tools. Most genome-wide screens lack sufficient resolving power as they typically depend on linkage. An alternate method is to screen for allele-specific expression (ASE), a simple yet powerful approach, where the expression of each gene allele is compared within an RNA sample. When the expression of the alleles is not equal, then one can unequivocally declare ASE and the presence of a polymorphic cis-acting (genetic) element for that gene as linkage disequilibrium (LD) is confined to the transcriptional unit. The only requirements for ASE to work are: (1) the assumption that variation in expression between alleles of a gene are responsible for part of the phenotypic variation, and (2) the existence of a cSNP to monitor the alleles. Response to Marek’s disease virus (MDV) infection in chickens is evaluated using next generation sequencing on a limited number of samples to query for ASE followed by Illumina GoldenGate assays to validate and expand the number of samples. Our results clearly demonstrate that ASE is an efficient method to identify potentially all or most of the genes for this complex trait. The identified cSNPs can be further evaluated in resource populations to determine their size of effect on genetic resistance to Marek’s disease as well as be directly implemented in genomic selection programs.