title: Genetic mapping of complex traits by minimizing integrated square errors.
abstract: BACKGROUND
  Genetic mapping has been used as a tool to study the genetic architecture of complex traits by localizing their underlying quantitative trait loci (QTLs). Statistical methods for genetic mapping rely on a key assumption, that is, traits obey a parametric distribution. However, in practice real data may not perfectly follow the specified distribution.
  
  
  RESULTS
  Here, we derive a robust statistical approach for QTL mapping that accommodates a certain degree of misspecification of the true model by incorporating integrated square errors into the genetic mapping framework. A hypothesis testing is formulated by defining a new test statistics--energy difference.
  
  
  CONCLUSIONS
  Simulation studies were performed to investigate the statistical properties of this approach and compare these properties with those from traditional maximum likelihood and non-parametric QTL mapping approaches. Lastly, analyses of real examples were conducted to demonstrate the usefulness and utilization of the new approach in a practical genetic setting.
authors.affiliation: Department of Applied Mathematics and Statistics, the State University of New York at Stony Brook, Stony Brook, NY 11790, USA.
authors.forename: Song
authors.initials: S
authors.lastname: Wu
authors.forename: Guifang
authors.initials: G
authors.lastname: Fu
authors.forename: Yunmei
authors.initials: Y
authors.lastname: Chen
authors.forename: Zhong
authors.initials: Z
authors.lastname: Wang
authors.forename: Rongling
authors.initials: R
authors.lastname: Wu
mesh_terms: D002874:Chromosome Mapping / Q000379:methods*; D008957:Models, Genetic; D040641:Quantitative Trait Loci; D013223:Statistics as Topic