Efficient algorithms for genome-wide tagSNP selection across populations via the linkage disequilibrium criterion.

In this paper, we study the tagSNP selection problem on multiple populations using the pairwise r(2) linkage disequilibrium criterion. We propose a novel combinatorial optimization model for the tagSNP selection problem, called the minimum common tagSNP selection (MCTS) problem, and present efficient solutions for MCTS. Our approach consists of three main steps including (i) partitioning the SNP markers into small disjoint components, (ii) applying some data reduction rules to simplify the problem, and (iii) applying either a fast greedy algorithm or a Lagrangian relaxation algorithm to solve the remaining (general) MCTS. These algorithms also provide lower bounds on tagging (i.e. the minimum number of tagSNPs needed). The lower bounds allow us to evaluate how far our solution is from the optimum. To the best of our knowledge, it is the first time tagging lower bounds are discussed in the literature. We assess the performance of our algorithms on real HapMap data for genome-wide tagging. The experiments demonstrate that our algorithms run 3 to 4 orders of magnitude faster than the existing single-population tagging programs like FESTA, LD-Select and the multiple-population tagging method MultiPop-TagSelect. Our method also greatly reduces the required tagSNPs compared to LD-Select on a single population and MultiPop-TagSelect on multiple populations. Moreover, the numbers of tagSNPs selected by our algorithms are almost optimal since they are very close to the corresponding lower bounds obtained by our method.