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Mendelian error detection in complex pedigrees using weighted constraint satisfaction techniques

Abstract : With the arrival of high throughput genotyping techniques, the detection of likely genotyping errors is becoming an increasingly important problem. In this paper we are interested in errors that violate Mendelian laws. The problem of deciding if a Mendelian error exists in a pedigree is NP-complete [1]. Existing tools dedicated to this problem may offer different level of services: detect simple inconsistencies using local reasoning, prove inconsistency, detect the source of error, propose an optimal correction for the error. All assume that there is at most one error. In this paper we show that the problem of error detection, of determining the minimum number of errors needed to explain the data (with a possible error detection) and error correction can all be modeled using soft constraint networks. Therefore, these problems provide attractive benchmarks for weighted constraint network (WCN) solvers. Because of their sheer size, these problems drove us into the development of a new WCN solver toulbar2 which solves very large pedigree problems with thousands of animals, including many loops and several errors.
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  • HAL Id : hal-02814634, version 1
  • PRODINRA : 186569

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Marti Sanchez, Simon de Givry, Thomas Schiex. Mendelian error detection in complex pedigrees using weighted constraint satisfaction techniques. Artificial Intelligence Research and Development, 163, IOS Press, pp.22, 2007, Frontiers in Artificial Intelligence and Applications, 978-1-60750-285-2 978-1-58603-798-7. ⟨hal-02814634⟩

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