Maximum likelihood resolution of multi-block genotypes

Gad Kimmel; Ron Shamir

doi:10.1145/974614.974616

Maximum likelihood resolution of multi-block genotypes

Gad Kimmel^*, Ron Shamir

^*Corresponding author for this work

School of Computer Science

Tel Aviv University

Research output: Contribution to conference › Paper › peer-review

25 Scopus citations

Abstract

We present a new algorithm for the problems of genotype phasing and block partitioning. Our algorithm is based on a new stochastic model, and on the novel concept of probabilistic common haplotypes. We formulate the goals of genotype resolving and block partitioning as a maximum likelihood problem, and solve it by an EM algorithm. When applied to real biological SNP data, our algorithm outperforms two state of the art phasing algorithms. Our algorithm is also considerably more sensitive and accurate than a previous method in predicting and identifying disease association.

Original language	English
Pages	2-9
Number of pages	8
DOIs	https://doi.org/10.1145/974614.974616
State	Published - 2004
Event	RECOMB 2004 - Proceedings of the Eight Annual International Conference on Research in Computational Molecular Biology - San Diego, CA., United States Duration: 27 Mar 2004 → 31 Mar 2004

Conference

Conference	RECOMB 2004 - Proceedings of the Eight Annual International Conference on Research in Computational Molecular Biology
Country/Territory	United States
City	San Diego, CA.
Period	27/03/04 → 31/03/04

Keywords

Algorithm
Disease association
Genotype
Genotype phasing
Haplotype
Haplotype block
Haplotype resolution
Maximum likelihood
SNP

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10.1145/974614.974616

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AB - We present a new algorithm for the problems of genotype phasing and block partitioning. Our algorithm is based on a new stochastic model, and on the novel concept of probabilistic common haplotypes. We formulate the goals of genotype resolving and block partitioning as a maximum likelihood problem, and solve it by an EM algorithm. When applied to real biological SNP data, our algorithm outperforms two state of the art phasing algorithms. Our algorithm is also considerably more sensitive and accurate than a previous method in predicting and identifying disease association.

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KW - Haplotype

KW - Haplotype block

KW - Haplotype resolution

KW - Maximum likelihood

KW - SNP

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Maximum likelihood resolution of multi-block genotypes

Abstract

Conference

Keywords

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