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The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping method based on independent component analysis. The proposed method outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype. It is applied to a clinical data set for the Schizophrenia disease with 368 individuals and 42 SNPs. It is also applied to a simulation study to investigate in more depth its performance. The obtained results demonstrate the novel characteristics of the proposed method compared to other genetic mapping methods. Finally, we study the robustness of the proposed method with missing genotype values and limited sample sizes.
Independent component analysis (ICA), principal component analysis (PCA), single nucleotide polymorphisms (SNPs), linkage disequilibrium, complex diseases, association mapping

M. Sarkis, Z. Dawy, J. Hagenauer and J. C. Mueller, "Fine-Scale Genetic Mapping Using Independent Component Analysis," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 5, no. , pp. 448-460, 2007.
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