Reference-based phasing using the Haplotype Reference Consortium panel

By Po-Ru Loh, Petr Danecek, Pier Francesco Palamara, Christian Fuchsberger, Yakir A. Reshef, Hilary K Finucane, S. Schoenherr, Lukas Forer, Shane A. McCarthy, Gonçalo R. Abecasis and 2 more. 2016

1,643 citations recorded by the source corpus.

Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing in a genotyped cohort, an approach that can yield high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium; HRC) using a new data structure based on the positional Burrows-Wheeler transform. We demonstrate that Eagle2 attains a ∼20× speedup and ∼10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2× the accuracy of 1000 Genomes–based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.

Subfields: Genetics

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Citations in the corpus

Fast two-stage phasing of large-scale sequence data. (2021, methodology): “Further improvements in computation time came from incorporating methodological ideas from genotype imputation and from data compression, such as the use of a small, custom reference panel for each individual,(24,25) and from the use of the positional Burrows-Wheeler transform(26) for efficiently id”
Accurate, scalable and integrative haplotype estimation (2019, methodology): “In SHAPEIT2 and 3, we start from a random haplotype resolution for all individuals and then perform 35 MCMC iterations to converge towards a reasonable haplotype resolution.”
Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics (2016, background): “3 [43]) and imputation against the HRC r1.”
Allelic Landscape of Human Blood Cell Trait Variation and Links (2016, background): “Larger panels include rare alleles from more variants and better capture the between-variant correlation structure of study populations (1000 Genomes Project Consortium et al., 2015; Iotchkova et al., 2016b; Loh et al., 2016; UK10K Consortium et al., 2015).”
WhatsHap: fast and accurate read-based phasing (2016, neutral): “The accuracy of such methods crucially depends on the size of the panel and recent progress has been made to scale them up to panels of more than 100 000 haplotypes [10, 11].”
Large-scale whole-genome sequencing of three diverse Asian populations in Singapore (2018, neutral): “Lastly, we applied a population-based phasing algorithm to phase genotypes to haplotypes for all samples including close relatives.(33) The final dataset consisted of haplotypes”
Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “We used the Eagle2 software [Loh et al., 2016] for pre-phasing and Minimac3 [Das et al.”