Prior knowledge guided eQTL mapping for identifying candidate genes

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DOIResolve DOI: http://doi.org/10.1186/s12859-016-1387-9
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TypeArticle
Journal titleBMC Bioinformatics
ISSN1471-2105
Volume17
Issue1
SubjecteQTL mapping; prior knowledge; candidate genes; Lasso
AbstractExpression quantitative trait loci (eQTL) mapping is often used to identify genetic loci and candidate genes correlated with traits. Although usually a group of genes affect complex traits, genes in most eQTL mapping methods are considered as independent. Recently, some eQTL mapping methods have accounted for correlated genes, used biological prior knowledge and applied these in model species such as yeast or mouse. However, biological prior knowledge might be very limited for most species. We proposed a data-driven prior knowledge guided eQTL mapping for identifying candidate genes. At first, quantitative trait loci (QTL) analysis was used to identify single nucleotide polymorphisms (SNP) markers that are associated with traits. Then co-expressed gene modules were generated and gene modules significantly associated with traits were selected. Prior knowledge from QTL mapping was used for eQTL mapping on the selected modules. We tested and compared prior knowledge guided eQTL mapping to the eQTL mapping with no prior knowledge in a simulation study and two barley stem rust resistance case studies. The results in simulation study and real barley case studies show that models using prior knowledge outperform models without prior knowledge. In the first case study, three gene modules were selected and one of the gene modules was enriched with defense response Gene Ontology (GO) terms. Also, one probe in the gene module is mapped to Rpg1, previously identified as resistance gene to stem rust. In the second case study, four gene modules are identified, one gene module is significantly enriched with defense response to fungus and bacterium. Prior knowledge guided eQTL mapping is an effective method for identifying candidate genes. The case studies in stem rust show that this approach is robust, and outperforms methods with no prior knowledge in identifying candidate genes.
Publication date
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23001312
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Record identifierb64f6b56-c9de-4d35-a5a6-b372bb0401ed
Record created2017-01-17
Record modified2017-01-17
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