Principal variable approach to multipurpose SNP selection in genetic association studies

Despite the various merits of joint analysis of the multiple markers, a single marker analysis is still popularly adopted in many Genome-Wide Association Studies GWAS. Since GWAS data tend to have many near-duplicated SNPs in the linkage equilibrium, it is a challenge to eliminate the redundant SNPs and determine the subset of the informative SNPs to be included in the joint analysis. In this study, we propose an unsupervised SNP selection algorithm based on the principal variable approach called the multipurpose SNP selection MP-SNP method. MP-SNP method takes subset of the original variables to keep the structure and information of the original variables, and the resulting SNP subset could be used for further analysis in various ways. Based on our simulation and real data analysis, we conclude that the MP-SNP method shows good performance in selecting the informative SNPs and also provides well-explained cluster structures.

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