Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms.

By Zhaohui S. Qin, Tianhua Niu, Jun S. Liu. American Journal of Human Genetics, 2002

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View Paper: https://www.semanticscholar.org/paper/5ea26c2582004b1fb0606af36e6b090f25b0bd14

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Citations in the corpus

Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. (2005, unknown): “This framework was subsequently adopted by others, including Qin et al. (2002), Stephens and Donnelly (2003), and Lin et al. (2004), for different methods for inferring haplotypes within each segment and for combining segments.”
HIBAG – HLA Genotype Imputation with Attribute Bagging (2013, unknown): “…available for individuals for each bootstrap sample B k from T are used to form haplotypes and their estimated frequencies (Figure 1) using the EM algorithm assuming HWE 22 as extended to multiple loci: 23,24 multi-locus genotype frequencies are assumed to be the products of haplotype frequencies.”
MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes (2010, background): “…and Scheet, 2005b; Stephens et al., 2001] and fastPHASE [Scheet and Stephens, 2006], two state of the art haplotyping algorithms [Marchini et al., 2006], and for BEAGLE [Browning, 2006] and PL-EM [Qin et al., 2002], two alternative haplotyping algorithms that are very computationally efficient.”
A comparison of bayesian methods for haplotype reconstruction from population genotype data. (2003, methodology): “Qin et al. (2002) apply similar ideas to make the EM algorithm computationally tractable for large data sets.”
STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement (2009, unknown): “It is common in such studies to use statistical methods to estimate haplotypes (Stephens et al. 2001; Qin et al. 2002; Scheet and Stephens 2006; Browning 2008), and their accuracy and efficiency have been discussed (Huang et al.”
Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9 (2005, methodology): “We estimated the haplotypes using the partition-ligation-expectation-maximization algorith”
Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. (2007, result): “8 In particular, we excluded Gerbil 13 (version 1.0) and PL-EM 9 (version 1.5), which had low accuracy relative to that of HaploRec.”
Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies (2006, methodology): “When the number of SNPs is large, the partition–ligation method of Niu et al. (2002) and Qin et al. (2002) and other modifications potentially can be adapted to reduce the computational burden.”
Robust inference with knockoffs (2018, methodology): “Returning to our genetic example, it has been shown that the joint distribution of SNPs may be accurately modeled by hidden Markov models (see [14, 16, 12, 10] for some early formulations), and there certainly is an abundance of genotype data to estimate the various model parameters; compare for ins”
Haplotype phasing: Existing methods and new developments (2011, methodology): “The partition–ligation extension of the EM algorith”
A comparison of phasing algorithms for trios and unrelated individuals. (2006, methodology): “Essentially, tripleM is a direct extension of the PL-EM algorithm for haplotype reconstruction, seen in the work of Qin et al. (2002) , and this approach has been used to construct haplotype phase for general pedigrees in the work of Zhang et al. (2005) .”