Algorithms for association study design using a generalized model of haplotype conservation

Schwartz, R.

doi:10.1109/csb.2004.1332421

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Unlike Becker et al (2006), we employ a hidden Markov model (HMM) to represent frequencies of all possible haplotypes over the set of typed loci. Similar HMMs have been successfully used in recent works (Kimmel and Shamir, 2005;Rastas et al, 2007;Scheet and Stephens, 2006;Schwartz, 2004) for genotype phasing and disease association. Two limitations of previous uses of HMMs in this context have been the relatively slow training based on genotype data and the inability to exploit available pedigree information.…”

Section: Kennedy Et Almentioning

confidence: 99%

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Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

Kennedy

Măndoiu²,

Paşaniuc³

2008

Journal of Computational Biology

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The presence of genotyping errors can invalidate statistical tests for linkage and disease association, particularly for methods based on haplotype analysis. Becker et al. have recently proposed a simple likelihood ratio approach for detecting errors in trio genotype data. Under this approach, a SNP genotype is flagged as a potential error if the likelihood associated with the original trio genotype data increases by a multiplicative factor exceeding a user selected threshold when the SNP genotype under test is deleted. In this article we give improved error detection methods using the likelihood ratio test approach in conjunction with likelihood functions that can be efficiently computed based on a Hidden Markov Model of haplotype diversity in the population under study. Experimental results on both simulated and real datasets show that proposed methods have highly scalable running time and achieve significantly improved detection accuracy compared to previous methods.

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Section: Kennedy Et Almentioning

confidence: 99%

“…The HMM used to represent haplotype frequencies has a similar structure to HMMs recently used in Kimmel and Shamir (2005), Rastas et al (2007), Scheet and Stephens (2006), and Schwartz (2004). This structure ( Fig.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

Kennedy

Măndoiu²,

Paşaniuc³

2008

Journal of Computational Biology

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“…Formally, for every SNP locus i ∈ {1, … , n }, we let be a random variable representing the allele observed at this locus on the maternal (paternal) chromosome of the individual under study, and be a random variable denoting the founder haplotype from which H i (respectively ) originates. As in previous works [ 15 , 17 , 28 - 30 ], we assume that F i form the states of a first order HMM with emissions H i , and estimate probabilities P ( f 1 ), P ( f i +1 | f i ), and P ( h i | f i )) using the classical Baum-Welch algorithm [ 31 ] based on haplotypes inferred from a panel representing the population of origin of the individual’s mother. Probabilities , and are estimated in the same way based on haplotypes inferred from a panel representing the population of origin of the individual’s father.…”

Section: Methodsmentioning

confidence: 74%

Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads

et al. 2011

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BackgroundRecent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping.ResultsIn this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/.ConclusionsIntegration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies.

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Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

Kennedy

Măndoiu

Paşaniuc

Lecture Notes in Computer Science

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Algorithms for association study design using a generalized model of haplotype conservation

Cited by 4 publications

References 15 publications

Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads

Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

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