k-Recombination Haplotype Inference in Pedigrees

Chin, Francis Y. L.; Zhang, Qiangfeng; Shen, Hong

doi:10.1007/11428848_125

Cited by 4 publications

(2 citation statements)

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“…Recently, Chin et al [6,28] developed a new problem called k-recombinant haplotype inference by adding a constraint to the number of recombinant events in a parentoffspring inheritance, which could get a more reasonable solution. This problem was also proved to be NP-hard.…”

Section: Haplotype Inference In Pedigreementioning

confidence: 99%

An overview of the haplotype problems and algorithms

Zhao

Zhang

et al. 2007

Front. Comput. Sc. China

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A single nucleotide polymorphism (SNP), as the most common form of genetic variation, has been widely studied to help analyze the possible association between diseases and genomes. To gain more information, SNPs on a single chromosome are usually studied together, which constitute a haplotype. Gaining haplotypes from biological experiments is usually very costly and timeconsuming, which causes people to develop efficient methods to determine haplotypes from the computational angle. Many problems and algorithms about haplotypes have been proposed to reduce the cost of studies of disease association. In general, four categories of problems are widely researched: the haplotype assembly problem, the haplotype inference problem, the haplotype block partition problem, and the haplotype tagging SNP selection problem. The former two problems have been well reviewed by many researchers, whereas the latter two have not been comprehensively surveyed to our knowledge. In this paper, we try to make a detailed introduction to the four problems, especially the latter two.

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Section: Haplotype Inference In Pedigreementioning

confidence: 99%

An overview of the haplotype problems and algorithms

Zhao

Zhang

et al. 2007

Front. Comput. Sc. China

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“…Similarly, one could obtain a trivial algorithm for k-RHC on tree pedigrees with time complexity O((mn) k+1 ) by using the linear time algorithms in [5,20,21] for tree ZRHC. We note in passing that the dynamic programming algorithm in [6] solves MRHC on tree pedigrees in O(nm 3k+1 2 m ) time when each parent-child pair is allowed to have at most k recombinants. This algorithm is inefficient when the number of loci exceeds 30 even if k is very small.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Haplotype Inference on Pedigrees with a Small Number of Recombinants

2011

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Combinatorial (or rule-based) methods for inferring haplotypes from genotypes on a pedigree have been studied extensively in the recent literature. These methods generally try to reconstruct the haplotypes of each individual so that the total number of recombinants is minimized in the pedigree. The problem is NP-hard, although it is known that the number of recombinants in a practical dataset is usually very small. In this paper, we consider the question of how to efficiently infer haplotypes on a large pedigree when the number of recombinants is bounded by a small constant, i.e. the so called k-recombinant haplotype configuration (k-RHC) problem. We introduce a simple probabilistic model for k-RHC where the prior haplotype probability of a founder and the haplotype transmission probability from a parent to a child are all assumed to follow the uniform distribution and k random recombination events are assumed to have taken place uniformly and independently in the pedigree. We present an O(mn log k+1 n) time algorithm for k-RHC on tree pedigrees without mating loops, where m is the number of loci and n is the size of the input pedigree, and prove that when 90 log n < m < n 3 , the algorithm can correctly find a feasible haplotype configuration that obeys the Mendelian law of inheritance and requires no more than k recombinants with probability 1 − O(k 2 log 2 n mn + 1 n 2 ). The algorithm is efficient when k is of a moderate value and could thus be used to infer haplotypes from genotypes on large tree pedigrees efficiently in practice. We have implemented the algorithm as a C++ program named TREE-k-RHC. The implementation incorporates several ideas for dealing with missing data and data with a large number of recombinants effectively. Our experimental results on both simulated and real datasets show that TREE-k-RHC can reconstruct haplotypes with a high accuracy and is much faster than the best combinatorial method in the literature.

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