Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation.

By Matthew Stephens, Paul Scheet. American Journal of Human Genetics, 2005

1,424 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2d151b8799222495c807ad81cdea8f12e35e4518

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Citations in the corpus

Genome-wide association study of prostate cancer identifies a second risk locus at 8q24 (2007, methodology): “In order to identify the haplotypes harboring the deleterious allele of the centromeric region, we phased genotypes from 20 SNPs to determine the most likely pair of haplotypes present in each individua”
Haplotype phasing: Existing methods and new developments (2011, neutral): “This approach forms the basis of many population-based statistical phasing methods, including PHAS”
MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes (2010, methodology): “…examples, see Abecasis et al., 2002; Kruglyak et al., 1996; Lander and Green, 1987] and shares several features with other HMMs used to describe sampled haplotypes as a mosaic of a set of reference haplotypes [Daly et al., 2001; Li and Stephens, 2003; Mott et al., 2000; Stephens and Scheet, 2005a].”
Isolation and introgression in the Intermountain West: contrasting gene genealogies reveal the complex biogeographic history of the American pika (Ochotona princeps) (2010, methodology): “…kit (Invitrogen, Carlsbad, CA, USA) (sequencing four clones per individual), or inferred allelic sequences using the program phase 2.1 (Stephens et al., 2001; Stephens & Scheet, 2005). phase applies a Bayesian approach to infer haplotypes from multi-allelic loci while accounting for recombination.”
Genotype Imputation (2009, methodology): “Tools in the first category can be further subdivided into those that compare the potential haplotypes for each individual with all other observed haplotypes (e.g., IMPUTE and MACH) and those that compare potential haplotypes for each individual to a representative set of haplotypes (e.g., fastPHASE”
Fast and accurate long-range phasing in a UK Biobank cohort (2015, positive): “Most existing methods for statistical phasing apply hidden Markov models (HMM) to iteratively refine haplotype frequency models and improve phase calls [6–12].”
Do fluctuating temperature environments elevate coral thermal tolerance? (2011, methodology): “The allele makeup of diploid sequences was established with Phase 2.1 (Stephens and Scheet 2005), and allele designations were used in FST calculations in Arlequin 3.5 (Excoffier et al. 2005).”
Reference-based phasing using the Haplotype Reference Consortium panel (2016, methodology): “Having prepared a HapHedge of conditioning haplotypes, Eagle2 performs phasing using a statistical model similar to the Li-Stephens haplotype copying model [5, 20] used by previous HMM-based methods.”
A comparison of phasing algorithms for trios and unrelated individuals. (2006, methodology): “With the explicit incorporation of recombination in the most recent version of the algorithm (Stephens and Scheet 2005), this clustering of haplotypes may change as one moves along a chromosome.”
Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. (2007, methodology): “Existing approaches to this problem include explicitly modeling the recombination in the coalescent model, 15 using haplotype blocks, 11,13 taking a sliding-window approach with window size varying with LD structure, 16 employing an HMM, 7 and using frequent haplotype fragments.”
A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. (2006, methodology): “As in a previous study (Stephens and Scheet 2005), our analysis of data from the African American and European-descent samples combined seemed to give a small decrease in error rate compared with analysis of the samples separately, which illustrates the relative robustness of the methods to…”
A Map of Recent Positive Selection in the Human Genome (2006, methodology): “Our analysis was based on haplotypes estimated by the HapMap Consortium using the program Phase 2 [18,23].”
Genome-Wide Inference of Ancestral Recombination Graphs (2013, background): “, [44–48]), and it generally offers good performance at minimal computational cost.”