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Fast and accurate long-range phasing in a UK Biobank cohort

Nature Genetics volume 48, pages 811–816 (2016)Cite this article

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Abstract

Recent work has leveraged the extensive genotyping of the Icelandic population to perform long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in target samples typed on genotyping arrays. Here we develop a fast and accurate LRP method, Eagle, that extends this paradigm to populations with much smaller proportions of genotyped samples by harnessing long (>4-cM) identical-by-descent (IBD) tracts shared among distantly related individuals. We applied Eagle to N ≈ 150,000 samples (0.2% of the British population) from the UK Biobank, and we determined that it is 1–2 orders of magnitude faster than existing methods while achieving similar or better phasing accuracy (switch error rate ≈ 0.3%, corresponding to perfect phase in a majority of 10-Mb segments). We also observed that, when used within an imputation pipeline, Eagle prephasing improved downstream imputation accuracy in comparison to prephasing in batches using existing methods, as necessary to achieve comparable computational cost.

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Figure 1: Eagle algorithm and example phase calls after each step.
Figure 2: Computational cost and accuracy of phasing methods.

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Acknowledgements

We are grateful to G. Bhatia, S. Gusev, M. Lipson, B. Pasaniuc, N. Patterson, and N. Zaitlen for helpful discussions. This research was conducted using the UK Biobank Resource and was supported by US National Institutes of Health grants R01 HG006399 and R01 MH101244 and US National Institutes of Health fellowship F32 HG007805. 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 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 & 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. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Alkes L Price

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  1. Po-Ru Loh
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Contributions

P.-R.L. and P.F.P. designed the algorithm. P.-R.L. implemented the algorithm and performed experiments. P.-R.L. and A.L.P. analyzed data and wrote the manuscript.

Corresponding authors

Correspondence to Po-Ru Loh or Alkes L Price.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparison of in-sample imputation and standard GWAS imputation.

Standard GWAS imputation differs from in-sample imputation in three ways. First, GWAS imputation usually involves imputing sequence data from a reference panel into a (genotyped but not sequenced) target sample, which typically requires phasing the sequenced reference (possibly using read information36), phasing the target sample (possibly using the phased reference), and imputing reference data into the target sample; in contrast, in-sample imputation involves only one sample, serving as both target and reference, that is simultaneously phased and imputed. Second, GWAS imputation pipelines produce probabilistic allele ‘dosage’ estimates, whereas phasing methods produce hard calls at missing genotypes (thus achieving suboptimal imputation R2). Third, typical GWAS impute sequenced SNPs into target samples that are fully typed at a set of ascertained array SNPs, whereas phasing methods impute missing data at ascertained SNPs. (The latter task may be slightly harder than the former, as genotyping arrays are sometimes optimized to minimize redundancy among ascertained SNPs; thus, the LD between a typical ascertained SNP and its closest ascertained proxy may be lower than the LD between a typical sequenced SNP and its closest ascertained proxy. On the other hand, the fact that rare variants on genotyping arrays are typically enriched in densely typed fine-mapping regions may make in-sample imputation easier.) For all of these reasons, different algorithms are typically used for phasing versus GWAS imputation (e.g., SHAPEIT10,12 versus IMPUTE2,55 and MaCH4 versus minimac5,56).

Supplementary Figure 2 In-sample imputation accuracy of Eagle and SHAPEIT2.

We randomly masked 2% of the genotypes in all N = 150,000 UK Biobank samples and phased the first 40 cM of chromosome 10 using Eagle (on the full cohort) and SHAPEIT2 (on all samples at once with either K = 100 (default) or 200 states as well as in N = 50,000 and 15,000 batches), imputing all masked genotypes in the process. (a) Accuracy of the imputed genotypes on the subset of 120,000 UK samples curated by UK Biobank for GWAS (~80% of all samples), stratified by MAF in those samples. (b) Accuracy of the imputed genotypes on subsets of samples defined by self-reported ancestry, stratified by MAF in those samples. The five largest ancestry groups in the data set were British (137,178 samples), Irish (3,977), “any other white background” (4,760), Indian (1,324), and Caribbean (1,028). The British and Irish results were nearly identical (Supplementary Table 11), so we did not plot Irish results to improve readability. For the ancestry groups with <5,000 samples, we plotted results only for MAF bins corresponding to an expected minor allele count of ≥2 among masked samples. Error bars, s.e.m. Numerical data are provided in Supplementary Tables 9 and 11.

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Supplementary Note, Supplementary Figures 1 and 2, and Supplementary Tables 1–15. (PDF 1702 kb)

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Loh, PR., Palamara, P. & Price, A. Fast and accurate long-range phasing in a UK Biobank cohort. Nat Genet 48, 811–816 (2016). https://doi.org/10.1038/ng.3571

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