Haplotyping in Pedigrees via a Genetic Algorithm

Tapadar, Pradip; Ghosh, Saurabh; Majumder, Partha P.

doi:10.1159/000022890

Cited by 51 publications

(40 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When these methods are faced with data with mutations, they typically treat the loci involving mutations as genotyping errors and delete such loci. Very few haplotype inference methods in the literature deal with pedigree data that contain mutations (one such method is a genetic algorithm in [18]). …”

Section: Introductionmentioning

confidence: 99%

Efficient Inference of Haplotypes from Genotypes on a Pedigree with Mutations and Missing Alleles (Extented Abstract)

Wang

Jiang

2009

Combinatorial Pattern Matching

View full text Add to dashboard Cite

Abstract. Driven by the international HapMap project, the haplotype inference problem has become an important topic in the computational biology community. In this paper, we study how to efficiently infer haplotypes from genotypes of related individuals as given by a pedigree. Our assumption is that the input pedigree data may contain de novo mutations and missing alleles but is free of genotyping errors and recombinants, which is usually true for tightly linked markers. We formulate the problem as a combinatorial optimization problem, called the minimum mutation haplotype configuration (MMHC) problem, where we seek haplotypes consistent with the given genotypes that incur no recombinants and require the minimum number of mutations. This extends the well studied zero-recombinant haplotype configuration (ZRHC) problem. Although ZRHC is polynomial-time solvable, MMHC is NP-hard. We construct an integer linear program (ILP) for MMHC using the system of linear equations over the field F (2) that has been developed recently to solve ZRHC. Since the number of constraints in the ILP is large (exponentially large in the general case), we present an incremental approach for solving the ILP where we gradually add the constraints to a standard ILP solver until a feasible haplotype configuration is found. Our preliminary experiments on simulated data demonstrate that the method is very efficient on large pedigrees and can infer haplotypes very accurately as well as recover most of the mutations and missing alleles correctly.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Inference of Haplotypes from Genotypes on a Pedigree with Mutations and Missing Alleles (Extented Abstract)

Wang

Jiang

2009

Combinatorial Pattern Matching

View full text Add to dashboard Cite

show abstract

“…Various algorithms have been presented for the MRHI problem [8] [7][12] [15]. In some cases, however, the MRHI model might yield unrealistic results in which a few parent-offspring pairs have many recombinations while others have no or few recombinations.…”

Section: Introductionmentioning

confidence: 99%

Minimum Parent-Offspring Recombination Haplotype Inference in Pedigrees

Zhang

Chin

Shen

2005

Transactions on Computational Systems Biology II

View full text Add to dashboard Cite

Abstract. The problem of haplotype inference under the Mendelian law of inheritance on pedigree genotype data is studied. The minimum recombination principle states that genetic recombinations are rare and haplotypes with fewer recombinations are more likely to exist. Given genotype data on a pedigree, the problem of Minimum Recombination Haplotype Inference (MRHI) is to find a set of haplotype configurations consistent with the genotype data having the minimum number of recombinations. In this paper, we focus on a variation of the MRHI problem that gives more realistic solutions, namely the k-MRHI problem, which has the additional constraint that the number of recombinations in each parent-offspring pair is at most k. Although the k-MRHI problem is NPhard even for k = 1, the k-MRHI problem with k > 1 can be solved efficiently by dynamic programming in O(nm 3k+1 0 2 m 0 ) time by adopting an approach similar to the one used by Doi, Li and Jiang [4] on pedigrees with n nodes and at most m0 heterozygous loci in each node. In particular, the 1-MRHI problem can be solved in O(nm 4 0 2 m 0 ) time. We propose an O(n 2 m0) algorithm to find a node as the root of the pedigree tree so as to further reduce the time complexity to O(m0min(tR)), where tR is the number of feasible haplotype configuration combinations in all trios in the pedigree tree when R is the root. If the pedigree has few generations, the 1-MRHI problem can be solved in O(min{nmwhere µR is the number of heterozygous loci in the root, and l is the maximum path length from the root to the leaves in the pedigree tree. Experiments on both real and simulated data confirm the out-performance of our algorithm when compared with other popular algorithms. In most real cases, our algorithm gives the same haplotyping results but runs much faster. In some real cases, other algorithms give an answer which has the least number of recombinations, while our algorithm gives a more credible solution and runs faster.

show abstract

“…The Minimum-Recombination Haplotype Inference (MRHI) problem, which is NP-hard [5], is to find a haplotype configuration with minimum number of recombinations given pedigree genotype data [8] [9][12] [15]. At times, however, the MRHI model might yield unrealistic results in which recombinations are concentrated in a few parent-offspring pairs.…”

Section: Introductionmentioning

confidence: 99%

k-Recombination Haplotype Inference in Pedigrees

Chin

Zhang

Shen

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.Haplotyping under the Mendelian law of inheritance on pedigree genotype data is studied. Because genetic recombinations are rare, research has focused on Minimum Recombination Haplotype Inference (MRHI), i.e. finding the haplotype configuration consistent with the genotype data having the minimum number of recombinations. We focus here on the more realistic k-MRHI, which has the additional constraint that the number of recombinations on each parent-offspring pair is at most k.Although k-MRHI is NP-hard even for k = 1, we give an algorithm to solve k-MRHI efficiently by dynamic programming in O(nm03k+12m0) time on pedigrees with n nodes and at most m0 heterozygous loci in each node. Experiments on real and simulated data show that, in most cases, our algorithm gives the same haplotyping results but runs much faster than other popular algorithms.

show abstract

Haplotyping in Pedigrees via a Genetic Algorithm

Cited by 51 publications

References 3 publications

Efficient Inference of Haplotypes from Genotypes on a Pedigree with Mutations and Missing Alleles (Extented Abstract)

Efficient Inference of Haplotypes from Genotypes on a Pedigree with Mutations and Missing Alleles (Extented Abstract)

Minimum Parent-Offspring Recombination Haplotype Inference in Pedigrees

k-Recombination Haplotype Inference in Pedigrees

Contact Info

Product

Resources

About