Robust QTL analysis by minimum beta-divergence method.

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 beta-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.