Haplotype inference based on sparse dictionary selection

The knowledge of the haplotypes of an individual makes it possible to predict diseases and help designing drugs. However, due to the cost of experimentally determining haplotypes, genotypes are usually measured instead. The haplotypes can still be inferred if the genotypes of a group of unrelated individuals are measured. We propose a mathematical framework and an efficient formulation based on the maximum parsimony principle that translates this principle to a sparse dictionary selection problem. We test the proposed solution with synthetic and real data sets and compare the performance with other methods.