Metaheuristics for the Haplotype Inference Problem: a preliminary analysis

Haplotype inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype inference are either simple greedy heuristic or exact methods (Integer Linear/Quadratic Programming, SAT encoding) that, at present, are adequate only for moderate size instances. We believe that metaheuristic and hybrid approaches could provide a better scalability. Moreover, metaheuristics can be very easily combined with problem specific heuristics and they can also be integrated with tree-based search tecnhiques, thus providing a promising framework for hybrid systems in which a good trade-off between effectiveness and efficiency can be reached. In this paper we illustrate a feasibility study of the approach and discuss some relevant design issues, such as modelling and design of approximate solvers which combine constructive heuristics, local search-based improvement strategies and learning mechanisms. Besides the relevance of the Haplotype inference problem itself, this preliminary analysis is also an interesting case study because the formulation of the problem poses some challenges in modelling and hybrid metaheuristic solver design that can be generalized to other problems.