Comparison of Aggregators for Multi-Objective SNP Selection

Abstract

SNPs (Single Nucleotide Polymorphisms) are genomic variants that associate with many genetic characteristics. These variants can also be utilized to track the on-going mutation in population genetics. The goal of this study was to select the most relevant SNP subsets for discriminating ethnic groups. Each SNP was evaluated by its: i) Mutual information, ii) Relief-F score, iii) Loadings of the first principal component, iv) Loadings of the second principal component. Combining these four feature ranking criteria in different ways, three different aggregation methods (Pareto Optimal, Condorcet, MC4) were compared with respect to their SNP selection accuracies. Results showed that SNP subsets chosen with Pareto Optimal yielded better classification accuracy.