Learning classifiers from discretized expression quantitative trait loci

Expression quantitative trait loci are used as a tool to iden- tify genetic causes of natural variation in gene expression. Only in a few cases the expression of a gene is controlled by a variant on a single marker. There is a plethora of dierent complexity levels of interaction ef- fects within markers, within genes and between marker and genes. This complexity challenges biostatisticians and bioinformatitians every day and makes ndings dicult to appear. As a way to simplify analysis and better control confounders, we tried a new approach for associa- tion analysis between genotypes and expression data. We pursued to understand whether discretization of expression data can be useful in genome-transcriptome association analyses. By discretizing the depen- dent variable, algorithms for learning classiers from data as well as performing block selection were used to help understanding the relation- ship between the expression of a gene and genetic markers. We present the results of a rst set of studies in which we used this approach to de- tect new possible causes of expression variation of DRB5, a gene playing an important role within the immune system. A supplementary website including a link to the software with the method implemented can be found at http://bios.ugr.es/classDRB5.

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