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Reference-based phasing using the Haplotype Reference Consortium panel

Nature Genetics volume 48, pages 1443–1448 (2016)Cite this article

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Abstract

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.

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Figure 1: Schematic of the Eagle2 core phasing algorithm.
Figure 2: Running time and accuracy of reference-based phasing in UK Biobank benchmarks.
Figure 3: Accuracy of reference-based phasing in GERA benchmarks.
Figure 4: Accuracy of reference-based phasing using the 1000 Genomes and HRC panels.
Figure 5: Running time and accuracy of cohort-based phasing in the UK Biobank cohort.

References

  1. Tewhey, R., Bansal, V., Torkamani, A., Topol, E.J. & Schork, N.J. The importance of phase information for human genomics. Nat. Rev. Genet. 12, 215–223 (2011).

    Article  CAS  Google Scholar 

  2. Browning, S.R. & Browning, B.L. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12, 703–714 (2011).

    Article  CAS  Google Scholar 

  3. Stephens, M., Smith, N.J. & Donnelly, P. A new statistical method for haplotype reconstruction from population data. Am. J. Hum. Genet. 68, 978–989 (2001).

    Article  CAS  Google Scholar 

  4. Halperin, E. & Eskin, E. Haplotype reconstruction from genotype data using Imperfect Phylogeny. Bioinformatics 20, 1842–1849 (2004).

    Article  CAS  Google Scholar 

  5. Stephens, M. & Scheet, P. Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am. J. Hum. Genet. 76, 449–462 (2005).

    Article  CAS  Google Scholar 

  6. Scheet, P. & Stephens, M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629–644 (2006).

    Article  CAS  Google Scholar 

  7. Browning, S.R. & Browning, B.L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  Google Scholar 

  8. Kong, A. et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat. Genet. 40, 1068–1075 (2008).

    Article  CAS  Google Scholar 

  9. Browning, B.L. & Browning, S.R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84, 210–223 (2009).

    Article  CAS  Google Scholar 

  10. Delaneau, O., Marchini, J. & Zagury, J.-F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).

    Article  Google Scholar 

  11. Williams, A.L., Patterson, N., Glessner, J., Hakonarson, H. & Reich, D. Phasing of many thousands of genotyped samples. Am. J. Hum. Genet. 91, 238–251 (2012).

    Article  CAS  Google Scholar 

  12. Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    Article  CAS  Google Scholar 

  13. Loh, P.-R., Palamara, P.F. & Price, A.L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    Article  CAS  Google Scholar 

  14. O'Connell, J. et al. Haplotype estimation for biobank-scale data sets. Nat. Genet. 48, 817–820 (2016).

    Article  CAS  Google Scholar 

  15. Snyder, M.W., Adey, A., Kitzman, J.O. & Shendure, J. Haplotype-resolved genome sequencing: experimental methods and applications. Nat. Rev. Genet. 16, 344–358 (2015).

    Article  CAS  Google Scholar 

  16. van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J.K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).

    Article  CAS  Google Scholar 

  17. Kumasaka, N., Knights, A.J. & Gaffney, D.J. Fine-mapping cellular QTLs with RASQUAL and ATAC–seq. Nat. Genet. 48, 206–213 (2016).

    Article  CAS  Google Scholar 

  18. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. http://dx.doi.org/10.1038/ng.3656 (published online 29 August, 2016).

  19. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. http://dx.doi.org/10.1038/ng.3643 (published online 22 August 2016).

  20. Durbin, R. Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT). Bioinformatics 30, 1266–1272 (2014).

    Article  CAS  Google Scholar 

  21. Li, N. & Stephens, M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165, 2213–2233 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  Google Scholar 

  23. Kvale, M.N. et al. Genotyping informatics and quality control for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200, 1051–1060 (2015).

    Article  Google Scholar 

  24. Banda, Y. et al. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200, 1285–1295 (2015).

    Article  Google Scholar 

  25. Browning, B.L. & Browning, S.R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).

    Article  CAS  Google Scholar 

  26. 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  27. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    Article  CAS  Google Scholar 

  28. He, D., Han, B. & Eskin, E. Hap-seq: an optimal algorithm for haplotype phasing with imputation using sequencing data. J. Comput. Biol. 20, 80–92 (2013).

    Article  CAS  Google Scholar 

  29. Delaneau, O., Howie, B., Cox, A.J., Zagury, J.-F. & Marchini, J. Haplotype estimation using sequencing reads. Am. J. Hum. Genet. 93, 687–696 (2013).

    Article  CAS  Google Scholar 

  30. Sharp, K., Kretzschmar, W., Delaneau, O. & Marchini, J. Phasing for medical sequencing using rare variants and large haplotype reference panels. Bioinformatics 32, 1974–1980 (2016).

    Article  CAS  Google Scholar 

  31. Chang, C.C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to S. Linderman, N. Patterson, L. O'Connor, A. Gusev, and B. van de Geijn for helpful discussions. This research was conducted using the UK Biobank Resource. P.L., P.P., and A.L.P. were supported by US National Institutes of Health grants R01 HG006399 and R01 MH101244 and fellowship F32 HG007805. P.D., S.M., R.D., and the Sanger Institute HRC server were supported by Wellcome Trust grant WT098051. C.F., G.R.A., and the Michigan Imputation Server were supported by the Austrian Science Fund (FWF) grant J-3401 and US National Institutes of Health grants HG007022 and HL117626. H.K.F. was supported by the Fannie and John Hertz Foundation. Computational analyses were performed on the Orchestra High Performance Compute Cluster at Harvard Medical School, which is partially supported by grant NCRR 1S10RR028832-01, and on the Lisa Genetic Cluster Computer hosted by SURFsara and financially supported by the Netherlands Scientific Organization (NWO 480-05-003, principal investigator D. Posthuma) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam.

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Authors and Affiliations

  1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Po-Ru Loh, Pier Francesco Palamara, Hilary K Finucane & Alkes L Price

  2. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Po-Ru Loh, Pier Francesco Palamara & Alkes L Price

  3. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

    Petr Danecek, Shane McCarthy & Richard Durbin

  4. Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), affiliated with the University of Lübeck, Bolzano, Italy

    Christian Fuchsberger

  5. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Christian Fuchsberger & Goncalo R Abecasis

  6. Department of Computer Science, Harvard University, Cambridge, Massachusetts, USA

    Yakir A Reshef

  7. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Hilary K Finucane

  8. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria

    Sebastian Schoenherr & Lukas Forer

  9. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Alkes L Price

Authors
  1. Po-Ru Loh
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  9. Shane McCarthy
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Contributions

P.-R.L. and A.L.P. designed the study. P.-R.L., P.F.P., Y.A.R., and H.K.F. developed the algorithm. P.-R.L. wrote the software. P.-R.L. and P.D. performed experiments. P.D. and S.M. incorporated the software into the Sanger Imputation Service. C.F., S.S., and L.F. incorporated the software into the Michigan Imputation Server. All authors analyzed data and wrote the paper.

Corresponding authors

Correspondence to Po-Ru Loh or Alkes L Price.

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The authors declare no competing financial interests.

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Supplementary Figures 1 and 2, Supplementary Tables 1–13 and Supplementary Note. (PDF 1968 kb)

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Loh, PR., Danecek, P., Palamara, P. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet 48, 1443–1448 (2016). https://doi.org/10.1038/ng.3679

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