New methods for imputation of missing genotype using linkage disequilibrium and haplotype information

Jung, Ho-Youl; Park, Yun-Ju; Kim, Young‐Jin; Park, Jungsun; Kimm, Kuchan; Koh, Insong

doi:10.1016/j.ins.2006.07.017

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…This method first uses joint mutual information to measure dependence between SNP sites, then find the two SNP sites having strong dependence with missing SNP site, and finally impute the missing SNP site by haplotype-based imputation method [7] based on EM method [12] and haplotype information.…”

Section: An Extension Methods To Haplotype-based Imputationmentioning

confidence: 99%

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Model, properties and imputation method of missing SNP genotype data utilizing mutual information

Wang

Wei-ming

Wang

et al. 2009

Journal of Computational and Applied Mathematics

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a b s t r a c tMutual information can be used as a measure for the association of a genetic marker or a combination of markers with the phenotype. In this paper, we study the imputation of missing genotype data. We first utilize joint mutual information to compute the dependence between SNP sites, then construct a mathematical model in order to find the two SNP sites having maximal dependence with missing SNP sites, and further study the properties of this model. Finally, an extension method to haplotype-based imputation is proposed to impute the missing values in genotype data. To verify our method, extensive experiments have been performed, and numerical results show that our method is superior to haplotype-based imputation methods. At the same time, numerical results also prove joint mutual information can better measure the dependence between SNP sites. According to experimental results, we also conclude that the dependence between the adjacent SNP sites is not necessarily strongest.

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Section: An Extension Methods To Haplotype-based Imputationmentioning

confidence: 99%

“…Later, taking the dependence between the adjoint SNP sites into account, some methods have been proposed based on linkage disequilibrium (LD) and haplotype information. By far, the methods based on LD and haplotype are most efficient for imputing missing SNP data [7].…”

Section: Introductionmentioning

confidence: 99%

Model, properties and imputation method of missing SNP genotype data utilizing mutual information

Wang

Wei-ming

Wang

et al. 2009

Journal of Computational and Applied Mathematics

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“…The resultant block structure was determined according to the algorithm of Gabriel et al [8], where a block is created if it is 95% of informative pair-wise SNP comparisons show a strong linkage disequilibrium with D 0 (a normalized measure of allelic association) equal to or greater than 0.8. Linkage disequilibrium [15] is the nonrandom association of alleles or genes at two or more specific location of a chromosome (loci). Linkage disequilibrium describes a situation in which some combinations of alleles or genetic markers occur more or less frequently in a population than would be expected from a random formation of haplotypes from alleles based on their frequencies.…”

Section: Haplotype Analysismentioning

confidence: 99%

A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients

Calcagno¹,

Staiano²,

Fortunato³

et al. 2010

Information Sciences

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“…However, it is common for data to be missing on the various genotyping platforms. Even for array technology, the rate of missing data can be as high as 0.53% [4]. This is approximately 5300 loci for every million SNPs designed on the arrays.…”

Section: Introductionmentioning

confidence: 99%

“…Imputation can be as simple as selecting at random a genotype that already exists in the data or by using a major allele. However, such naive methods normally result in high error rates [4]. Certain other methods are based on haplotypes, which are sets of SNPs that are associated on one chromosome pair.…”

Section: Introductionmentioning

confidence: 99%

Simpute: An Efficient Solution for Dense Genotypic Data

Lin

Chang²,

Tang³

et al. 2013

BioMed Research International

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Single nucleotide polymorphism (SNP) data derived from array-based technology or massive parallel sequencing are often flawed with missing data. Missing SNPs can bias the results of association analyses. To maximize information usage, imputation is often adopted to compensate for the missing data by filling in the most probable values. To better understand the available tools for this purpose, we compare the imputation performances among BEAGLE, IMPUTE, BIMBAM, SNPMStat, MACH, and PLINK with data generated by randomly masking the genotype data from the International HapMap Phase III project. In addition, we propose a new algorithm called simple imputation (Simpute) that benefits from the high resolution of the SNPs in the array platform. Simpute does not require any reference data. The best feature of Simpute is its computational efficiency with complexity of order (mw + n), where n is the number of missing SNPs, w is the number of the positions of the missing SNPs, and m is the number of people considered. Simpute is suitable for regular screening of the large-scale SNP genotyping particularly when the sample size is large, and efficiency is a major concern in the analysis.

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New methods for imputation of missing genotype using linkage disequilibrium and haplotype information

Cited by 8 publications

References 16 publications

Model, properties and imputation method of missing SNP genotype data utilizing mutual information

Model, properties and imputation method of missing SNP genotype data utilizing mutual information

A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients

Simpute: An Efficient Solution for Dense Genotypic Data

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