For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
42
views
14
references
 Top references
cited by
534
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      498
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      Reference-based phasing using the Haplotype Reference Consortium panel

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing within a genotyped cohort, an approach that can attain 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 ≈20x speedup and ≈10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2x 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.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: found
          • Article: not found

          A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

          Paul Scheet, Matthew Stephens (2006)
          We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both "block-like" patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide-polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site.
          Article 0 comments Cited 755 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

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

            Matthew Stephens, Paul Scheet (2005)
            Although many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.
            Article 0 comments Cited 426 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              WASP: allele-specific software for robust molecular quantitative trait locus discovery

              Bryce van de Geijn, Graham McVicker, Yoav Gilad … (2015)
              Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL mapping approaches.
              Article 0 comments Cited 240 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9216904
                Journal ID (pubmed-jr-id): 2419
                Journal ID (nlm-ta): Nat Genet
                Journal ID (iso-abbrev): Nat. Genet.
                Title: Nature genetics
                ISSN (Print): 1061-4036
                ISSN (Electronic): 1546-1718
                Publication date Nihms-submitted: 27 September 2016
                Publication date (Electronic): 3 October 2016
                Publication date (Print): November 2016
                Publication date PMC-release: 03 April 2017
                Volume: 48
                Issue: 11
                Pages: 1443-1448
                Affiliations
                [1 ] Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
                [2 ] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
                [3 ] The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK.
                [4 ] Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), affiliated to the University of Lübeck, Via Galvani 31, Bolzano 39100, Italy.
                [5 ] Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
                [6 ] Department of Computer Science, Harvard University, Cambridge, Massachusetts, USA.
                [7 ] Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
                [8 ] Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck 6020, Austria.
                [9 ] Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
                Author notes
                Correspondence should be addressed to P.-R.L. ( loh@ 123456hsph.harvard.edu ) or A.L.P. ( aprice@ 123456hsph.harvard.edu ).
                Article
                Manuscript ID: NIHMS813751
                DOI: 10.1038/ng.3679
                PMC ID: 5096458
                PubMed ID: 27694958
                SO-VID: b93db26e-5218-49d9-a677-8faffc2105f9
                License:

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Categories
                Subject: Article

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

                Comments

                Comment on this article

                scite_

                Similar content498

                See all similar

                Cited by685

                See all cited by

                Most referenced authors810

                See all reference authors