Identifying maternal and paternal inheritance is essential to be
able to find the set of genes responsible for a particular disease.
However, due to technological limitations, we have access to genotype
data (genetic makeup of an individual), and determining haplotypes
(genetic makeup of the parents) experimentally is a costly and time
consuming procedure. With these biological motivations, we study a
computational problem, called Haplotype Inference by Pure Parsimony
(HIPP), that asks for the minimal number of haplotypes that form a
given set of genotypes. HIPP has been studied using integer
linear
programming, branch and bound algorithms, SAT-based algorithms, or
pseudo-boolean optimization methods. We introduce a new approach to
solving HIPP, using Answer Set Programming (ASP). According to our
experiments with a large number of problem instances (some
automatically generated and some real), our ASP-based approach solves
the most number of problems compared with other approaches. Due to the
expressivity of the knowledge representation language of ASP, our
approach allows us to solve variations of HIPP, e.g., with additional
domain specific information, such as patterns/parts of haplotypes
observed for some gene family, or with some missing genotype
information. In this sense, the ASP-based approach is more general than
the existing approaches to haplotype inference.