Document Detail

Genetical genomics analysis of a yeast segregant population for transcription network inference.
MedLine Citation:
PMID:  15781693     Owner:  NLM     Status:  MEDLINE    
Genetic analysis of gene expression in a segregating population, which is expression profiled and genotyped at DNA markers throughout the genome, can reveal regulatory networks of polymorphic genes. We propose an analysis strategy with several steps: (1) genome-wide QTL analysis of all expression profiles to identify eQTL confidence regions, followed by fine mapping of identified eQTL; (2) identification of regulatory candidate genes in each eQTL region; (3) correlation analysis of the expression profiles of the candidates in any eQTL region with the gene affected by the eQTL to reduce the number of candidates; (4) drawing directional links from retained regulatory candidate genes to genes affected by the eQTL and joining links to form networks; and (5) statistical validation and refinement of the inferred network structure. Here, we apply an initial implementation of this strategy to a segregating yeast population. In 65, 7, and 28% of the identified eQTL regions, a single candidate regulatory gene, no gene, or more than one gene was retained in step 3, respectively. Overall, 768 putative regulatory links were retained, 331 of which are the strongest candidate links, as they were retained in the expression correlation analysis and were located within or near an eQTL subregion identified by a multimarker analysis separating multiple linked QTL. One or several biological processes were statistically significantly overrepresented in independent network structures or in highly interconnected subnetworks. Most of the transcription factors found in the inferred network had a putative regulatory link to only one other gene or exhibited cis-regulation.
Nan Bing; Ina Hoeschele
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2005-03-21
Journal Detail:
Title:  Genetics     Volume:  170     ISSN:  0016-6731     ISO Abbreviation:  Genetics     Publication Date:  2005 Jun 
Date Detail:
Created Date:  2005-06-27     Completed Date:  2005-11-22     Revised Date:  2013-06-09    
Medline Journal Info:
Nlm Unique ID:  0374636     Medline TA:  Genetics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  533-42     Citation Subset:  IM    
Virginia Bioinformatics Institute and Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, 24061-0477, USA.
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MeSH Terms
Gene Expression Profiling
Gene Expression Regulation, Fungal
Genetic Markers
Genome, Fungal*
Models, Biological
Models, Genetic
Quantitative Trait Loci
Saccharomyces cerevisiae / genetics
Saccharomyces cerevisiae Proteins / genetics
Transcription Factors
Transcription, Genetic*
Reg. No./Substance:
0/Genetic Markers; 0/Saccharomyces cerevisiae Proteins; 0/Transcription Factors; 9007-49-2/DNA

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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