The hardness of approximate optima in lattices, codes, and systems of linear equations
Journal of Computer and System Sciences - Special issue: papers from the 32nd and 34th annual symposia on foundations of computer science, Oct. 2–4, 1991 and Nov. 3–5, 1993
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
The Haplotyping problem: an overview of computational models and solutions
Journal of Computer Science and Technology
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
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Haplotype Inference (HI) is a computational challenge of crucial importance in a range of genetic studies, such as functional genomics, pharmacogenetics and population genetics. Pedigrees have been shown a valuable data that allows us to infer haplotypes from genotypes more accurately than population data, since Mendelian inheritance restricts the set of possible solutions. In order to overcome the limitations of classic statistical haplotyping methods, a combinatorial formulation of the HI problem on pedigrees has been proposed in the literature, called MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION (MRHC) problem, that allows a single type of genetic variation events, namely recombinations. In this work, we define a new problem, called MINIMUM-CHANGE HAPLOTYPE CONFIGURATION (MCHC), that extends the MRHC formulation by allowing also a second type of natural variation events: mutations. We propose an efficient and accurate heuristic algorithm for MCHC based on an L-reduction to a well-known coding problem. Our heuristic can also be used to solve the original MRHC problem and it can take advantage of additional knowledge about the input genotypes, such as the presence of recombination hotspots and different rates of recombinations and mutations. Finally, we present an extensive experimental evaluation and comparison of our heuristic algorithm with several other state-of-the-art methods for HI on pedigrees under several simulated scenarios.