An Overview of Combinatorial Methods for Haplotype Inference

Gusfield, Dan

doi:10.1007/978-3-540-24719-7_2

Cited by 36 publications

(24 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, computational inference of haplotypes from genotype data, known as the genotype phasing problem, has received much attention in the past few years, see, e.g., [2]- [5] for recent surveys.…”

Section: Introductionmentioning

confidence: 99%

Highly Scalable Genotype Phasing by Entropy Minimization

Gusev

Măndoiu

Paşaniuc

2008

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Abstract-A Single Nucleotide Polymorphism (SNP) is a position in the genome at which two or more of the possible four nucleotides occur in a large percentage of the population. SNPs account for most of the genetic variability between individuals, and mapping SNPs in the human population has become the next high-priority in genomics after the completion of the Human Genome project. In diploid organisms such as humans, there are two non-identical copies of each autosomal chromosome. A description of the SNPs in a chromosome is called a haplotype. At present, it is prohibitively expensive to directly determine the haplotypes of an individual, but it is possible to obtain rather easily the conflated SNP information in the so called genotype. Computational methods for genotype phasing, i.e., inferring haplotypes from genotype data, have received much attention in recent years as haplotype information leads to increased statistical power of disease association tests. However, many of the existing algorithms have impractical running time for phasing large genotype datasets such as those generated by the international HapMap project. In this paper we propose a highly scalable algorithm based on entropy minimization. Our algorithm is capable of phasing both unrelated and related genotypes coming from complex pedigrees. Experimental results on both real and simulated datasets show that our algorithm achieves a phasing accuracy worse but close to that of best existing methods while being several orders of magnitude faster. The open source code implementation of the algorithm and a web interface are publicly available at http: //dna.engr.uconn.edu/˜software/ent/.

show abstract

Section: Introductionmentioning

confidence: 99%

Highly Scalable Genotype Phasing by Entropy Minimization

Gusev

Măndoiu

Paşaniuc

2008

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

“…There are several approaches to this problem: combinatorial, statistical, etc. Reviews about the haplotype inference problem can be found in [1], [2], [3]. This paper focuses on one of the most popular approaches to the haplotype inference problem which is known as haplotype inference by pure parsimony (HIPP) [4].…”

Section: Introductionmentioning

confidence: 99%

A Preprocessing Procedure for Haplotype Inference by Pure Parsimony

Irurozki

Calvo

Lozano

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Haplotype data is especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and expensive. Computational methods have proved to be an effective way of inferring haplotype data from genotype data. One of these methods, the haplotype inference by pure parsimony approach (HIPP), casts the problem as an optimization problem and as such has been proved to be NP-hard. We have designed and developed a new preprocessing procedure for this problem. Our proposed algorithm works with groups of haplotypes rather than individual haplotypes. It iterates searching and deleting haplotypes that are not helpful in order to find the optimal solution. This preprocess can be coupled with any of the current solvers for the HIPP that need to preprocess the genotype data. In order to test it, we have used two state-of-the-art solvers, RTIP and GAHAP, and simulated and real HapMap data. Due to the computational time and memory reduction caused by our preprocess, problem instances that were previously unaffordable can be now efficiently solved.

show abstract

“…Many developments have been made since then. There have been a few review papers on haplotype inference in recent years, [5][6][7] but all of them focus mainly on combinatorial formulations and solutionsa; two of them, Gusfield 6 and Halldórsson et al, 7 deal only with unrelated population data. This paper will review both statistical and combinatorial algorithms for three different types of data: pedigree data, population data, and pooled samples.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Haplotyping Algorithms for Tightly Linked Markers

Jiang

2008

J. Bioinform. Comput. Biol.

View full text Add to dashboard Cite

Two grand challenges in the postgenomic era are to develop a detailed understanding of heritable variation in the human genome, and to develop robust strategies for identifying the genetic contribution to diseases and drug responses. Haplotypes of single nucleotide polymorphisms (SNPs) have been suggested as an effective representation of human variation, and various haplotype-based association mapping methods for complex traits have been proposed in the literature. However, humans are diploid and, in practice, genotype data instead of haplotype data are collected directly. Therefore, efficient and accurate computational methods for haplotype reconstruction are needed and have recently been investigated intensively, especially for tightly linked markers such as SNPs. This paper reviews statistical and combinatorial haplotyping algorithms using pedigree data, unrelated individuals, or pooled samples.

show abstract

An Overview of Combinatorial Methods for Haplotype Inference

Cited by 36 publications

References 25 publications

Highly Scalable Genotype Phasing by Entropy Minimization

Highly Scalable Genotype Phasing by Entropy Minimization

A Preprocessing Procedure for Haplotype Inference by Pure Parsimony

A Survey on Haplotyping Algorithms for Tightly Linked Markers

Contact Info

Product

Resources

About