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United States Department of Agriculture

Agricultural Research Service

Research Project: USING THE GENOME TO UNDERSTAND IMMUNOGENETICS OF POULTRY Title: Identifying Specific Genes Controlling Complex Traits Through A Genome-Wide Screen For cis-Acting Regulatory Elements - An Example Using Marek's Disease

Authors
item Maceachern, Sean
item Muir, William -
item Crosby, Seth -
item Cheng, Hans

Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: January 9, 2010
Publication Date: January 9, 2010
Citation: MacEachern, S.A., Muir, W.M., Crosby, S., Cheng, H.H. 2010. Identifying Specific Genes Controlling Complex Traits Through A Genome-Wide Screen For cis-Acting Regulatory Elements - An Example Using Marek's Disease [abstract]. International Plant and Animal Genome XVIII Conference, January 9-13, 2010, San Diego, California. p. 169.

Interpretive Summary: The identification of specific genes underlying phenotypic variation of complex traits 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. One alternate is to screen for allele-specific expression (ASE), a simple yet powerful approach. In ASE, for all genes of interest, the relative expression levels of the two alleles are compared within an RNA sample derived from an individual. When the expression of the alleles are not equal, then one can unequivocally declare ASE and the presence of a polymorphic cis-acting element for that gene as linkage disequilibrium 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. We provide an example that demonstrates an efficient method to screen for ASE on a genome-wide scale. Specifically, using next generation sequencing, inbred chicken lines were intermated to produce F1 progeny that are heterozygous for the maximal number of genes. RNA from uninfected and virus-infected birds were sequenced on a next generation sequencer. The data was analyzed to identify SNPs and when found, queried to see if the alleles were differentially expressed. Finally, Illumina GoldenGate assays were designed to screen more samples to validate and expand the initial analyses. The most current results are presented.

Technical Abstract: The identification of specific genes underlying phenotypic variation of complex traits 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. One alternate is to screen for allele-specific expression (ASE), a simple yet powerful approach. In ASE, for all genes of interest, the relative expression levels of the two alleles are compared within an RNA sample derived from an individual. When the expression of the alleles are not equal, then one can unequivocally declare ASE and the presence of a polymorphic cis-acting element for that gene as linkage disequilibrium 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. We provide an example that demonstrates an efficient method to screen for ASE on a genome-wide scale. Specifically, using next generation sequencing, inbred chicken lines were intermated to produce F1 progeny that are heterozygous for the maximal number of genes. RNA from uninfected and virus-infected birds were sequenced on a next generation sequencer. The data was analyzed to identify SNPs and when found, queried to see if the alleles were differentially expressed. Finally, Illumina GoldenGate assays were designed to screen more samples to validate and expand the initial analyses. The most current results are presented.

Last Modified: 12/22/2014
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