Feature Selection Methods for SNP Analysis

Single Nucleotide Polymorphisms (SNPs) are considered as the most significant biomarkers with a wide range of applications, especially in the field of human genetics. The high cost of genotyping a large number of SNPs is one of the major challenge faced in SNP analysis. Evaluating the SNP data/high dimensional data and identify the most important or significant SNPs/features from the high-dimensional data, called feature selection (FS) and is one of the most focused areas of research in bioinformatics. Feature selection is an important process in many of the bioinformatics applications. This selection process can reduce the dimensionality of the dataset by removing irrelevant, redundant features and select the most informative features. In this article, we present a review of feature selection methods for SNP analysis.