title: Prior knowledge guided eQTL mapping for identifying candidate genes.
abstract: BACKGROUND
  Expression 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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: National Research Council Canada, 1200 Montreal Rd., Ottawa, K1A 0R6, Canada. Yunli.Wang@nrc-cnrc.gc.ca.
authors.forename: Yunli
authors.identifier: http://orcid.org/0000-0002-2320-954X
authors.initials: Y
authors.lastname: Wang
authors.affiliation: National Research Council Canada, 46 Dineen Dr., Fredericton, E3B 9W4, Canada.
authors.forename: Rene
authors.initials: R
authors.lastname: Richard
authors.affiliation: National Research Council Canada, 1200 Montreal Rd., Ottawa, K1A 0R6, Canada.
authors.forename: Youlian
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000818:Animals; D002874:Chromosome Mapping / Q000379:methods*; D053263:Gene Regulatory Networks; D001467:Hordeum / Q000235:genetics*; D051379:Mice / Q000235:genetics*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D012441:Saccharomyces cerevisiae / Q000235:genetics*
keywords: Candidate genes; Lasso; Prior knowledge; eQTL mapping