In this paper we investigate the question of transfer learning in evolutionary optimization using estimation of distribution algorithms. We propose a framework for transfer learning between related optimization problems by means of structural transfer. Different methods for incrementing or replacing the (possibly unavailable) structural information of the target optimization problem are presented. As a test case we solve the multi-marker tagging single-nucleotide polymorphism (SNP) selection problem, a real world problem from genetics. The introduced variants of structural transfer are validated in the computation of tagging SNPs on a database of 1167 individuals from 58 human populations worldwide. Our experimental results show significant improvements over EDAs that do not incorporate information from related problems.