title: Mapping epistatic quantitative trait loci.
abstract: BACKGROUND
  How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multiple QTL are in linkage and have interactions, searching for QTL can become a very delicate issue. A commonly used strategy that performs a two-dimensional genome scan to search for a pair of QTL with epistasis can suffer from low statistical power and also may lead to false identification due to complex linkage disequilibrium and interaction patterns.
  
  
  RESULTS
  To tackle the problem of complex interaction of multiple QTL with linkage, we developed a three-stage search strategy. In the first stage, main effect QTL are searched and mapped. In the second stage, epistatic QTL that interact significantly with other identified QTL are searched. In the third stage, new epistatic QTL are searched in pairs. This strategy is based on the consideration that most genetic variance is due to the main effects of QTL. Thus by first mapping those main-effect QTL, the statistical power for the second and third stages of analysis for mapping epistatic QTL can be maximized. The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage. The model search criterion is empirically and dynamically evaluated by using a score-statistic based resampling procedure. We demonstrate through simulations that the method has good power and low false positive in the identification of QTL and epistasis.
  
  
  CONCLUSIONS
  This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern. The method has been implemented in the user-friendly computer software Windows QTL Cartographer. This will greatly facilitate the application of the method for QTL mapping data analysis.
authors.affiliation: Department of Mathematics, University of Alabama, Tuscaloosa AL, USA. claurie@bama.ua.edu.|Department of Biostatistics, University of Washington, Seattle WA, USA. claurie@bama.ua.edu.
authors.forename: Cecelia
authors.initials: C
authors.lastname: Laurie
authors.affiliation: Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh NC, 27695-7566, USA. swang@statgen.ncsu.edu.
authors.forename: Shengchu
authors.initials: S
authors.lastname: Wang
authors.affiliation: Instituto Agronômico de Campinas, Centro de Grãos e Fibras, Campinas SP, Brazil. lacgarcia@gmail.com.|APTA Regional, Pólo Centro Sul, Piracicaba SP, Brazil. lacgarcia@gmail.com.
authors.forename: Luciana Aparecida
authors.initials: LA
authors.lastname: Carlini-Garcia
authors.affiliation: Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh NC, 27695-7566, USA. zeng@stat.ncsu.edu.|Department of Biological Sciences, North Carolina State University, Raleigh NC, USA. zeng@stat.ncsu.edu.
authors.forename: Zhao-Bang
authors.initials: ZB
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D004843:Epistasis, Genetic; D008126:Lod Score; D008957:Models, Genetic; D040641:Quantitative Trait Loci