Locating multiple interacting quantitative trait loci using rank-based model selection

In previous work, a modified version of the Bayesian Information Criterion (mBIC) has been proposed to locate multiple interacting quantitative trait loci (QTL). Simulation studies and real data analysis demonstrate good properties of the mBIC in situations where the error distribution is approximately normal. However, as with other standard techniques of QTL mapping, the performance of the mBIC strongly deteriorate when the trait distribution is heavy tailed or when the data contain a significant proportion of outliers. In the present paper, we propose a suitable robust version of the mBIC which is based on ranks. We investigate the properties of the resulting method based on theoretical calculations, computer simulations and a real data analysis. Our simulation results show that for the sample sizes typically used in QTL mapping, the methods based on ranks are almost as efficient as standard techniques when the data are normal and are much better when the data come from some heavy tailed distribution or include a proportion of outliers.

Key Words: BIC, QTL mapping, model selection, ranks

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				S. Isobe, A. Nakaya, and S. Tabata Genotype Matrix Mapping: Searching for Quantitative Trait Loci Interactions in Genetic Variation in Complex Traits DNA Res, November 13, 2007; (2007) dsm020v1. [Abstract] [Full Text] [PDF]

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