factors can vary in different populations so the HapMap project used a variety of sequencing techniques to discover and catalog SNPs from different sets of populations. Initially the project sequenced individuals from Yoruba population of African origin (YRI), residents of Utah with western European ancestry (CEU), unrelated individuals from Tokyo, Japan (JPT) and unrelated Han Chinese individuals from Beijing, China (CHB). Recently their datasets have been expanded to include other populations (11 groups) Selection and evaluation Steps for tag SNP selection Selection of maximum informative tag SNPs is an NP complete problem. However, algorithms can be devised to provide approximate solution within a margin of error. The criteria that are needed to define each tag SNP selection algorithm is the following: Define area to search - the algorithm will attempt to locate tag SNPs in neighborhood N(t) of a target SNP t Define a metric to assess the quality of tagging - the metric needs to measure how well a target SNP t can be predicted using a set of its neighbors N(t) i.e. how well a tag SNP as a representative of the SNPs in a neighborhood N(t) can predict a target SNP t. It can be defined as a probability that the target SNP t has different values for any pair of haplotypes i and j where the value of the SNP s is also different for the same haplotypes. The informativeness of the metric can be represented in terms of a graph theory, where every SNP s is represented as a graph Gs whose nodes are haplotypes. Gs has an edge between the nodes (i,j) if and only if the values of s are different for the haplotypes Hi, Hj. Derive the algorithm to find representative SNPs - the goal of the algorithm is to find the minimal subset of tag SNPs selected with maximum informativeness between each tag SNP with every other target SNP Validate the algorithm Feature selection Methods for selecting features fall into two categories: filter methods and wrapper methods. Filter algorithms are general preprocessing algorithms that do not assume the use of a specific classification method. Wrapper algorithms, in contrast, “wrap” the feature selection around a specific classifier and select a subset of features based on the classifier's accuracy using cross-validation. The feature selection method suitable for selecting tag SNPs must have the following characteristics: scale well for large number of SNPs; not