Robust QTL analysis by minimum β-divergence method

Authors:
Md. Nurul Haque Mollah;Shinto Eguchi
Affiliations:
The Institute of Statistical Mathematics, 4-6-7, Minami-Azabu, Minato-Ku, Tokyo 106-8569, Japan.;The Institute of Statistical Mathematics, 4-6-7, Minami-Azabu, Minato-Ku, Tokyo 106-8569, Japan
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

Citing 2
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Robust blind source separation by beta divergence

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Robust Composite Interval Mapping for QTL Analysis by Minimum beta-Divergence Method

BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine

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Abstract

Robustness has received too little attention in Quantitative Trait Loci (QTL) analysis in experimental crosses. This paper discusses a robust QTL mapping algorithm based on Composite Interval Mapping (CIM) model by minimising β-divergence using the EM like algorithm. We investigate the robustness performance of the proposed method in a comparison of Interval Mapping (IM) and CIM algorithms using both synthetic and real datasets. Experimental results show that the proposed method significantly improves the performance over the traditional IM and CIM methods for QTL analysis in presence of outliers; otherwise, it keeps equal performance.