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Open Access Research article

Genetic mapping of complex traits by minimizing integrated square errors

Song Wu12, Guifang Fu3, Yunmei Chen4, Zhong Wang23 and Rongling Wu23*

Author Affiliations

1 Department of Applied Mathematics and Statistics, the State University of New York at Stony Brook, Stony Brook, NY 11790, USA

2 Center for Computational Biology, Beijing Forestry University, Beijing 100083, China

3 Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA

4 Department of Mathematics, University of Florida, Gainesville, FL 32611, USA

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BMC Genetics 2012, 13:20  doi:10.1186/1471-2156-13-20

Published: 23 March 2012



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.


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.


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.