Genetic mapping of complex traits by minimizing integrated square errors
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
BMC Genetics 2012, 13:20 doi:10.1186/1471-2156-13-20Published: 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.