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Haplotype estimation for biobank-scale data sets


The UK Biobank (UKB) has recently released genotypes on 152,328 individuals together with extensive phenotypic and lifestyle information. We present a new phasing method, SHAPEIT3, that can handle such biobank-scale data sets and results in switch error rates as low as 0.3%. The method exhibits O(NlogN) scaling with sample size N, enabling fast and accurate phasing of even larger cohorts.

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Figure 1: Performance on the UK BiLEVE chromosome 20 data set.


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UK BiLEVE was funded by a Medical Research Council (MRC) strategic award to M.T., I.H., D. Strachan and L.W. (MC_PC_12010). We would like to thank the UK BiLEVE study investigators, array design team, analysis group and steering group. This research has been conducted using the UK Biobank Resource. This article presents independent research funded partially by the National Institute for Health Research (NIHR). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the UK Department of Health. J.M. acknowledges support from the ERC (grant 617306).

Author information




J.O'C. and J.M. designed the experiments. J.O'C. derived the clustering algorithm and implemented it. O.D. refactored the HMM code. J.O'C. and K.S. worked on the IBD HMM and analyzed the data sets. J.O'C., K.S. and J.M. carried out the experiments. N.S., L.W., I.H. and M.T. provided the UK BiLEVE data set. J.M. and O.D. supervised the research. J.O'C., K.S., J.-F.Z., O.D. and J.M. wrote the manuscript.

Corresponding author

Correspondence to Jonathan Marchini.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 SHAPEIT3 performance by varying the cluster size parameter.

SHAPEIT3 running time (left) and SER (right) against increasing values of the cluster size parameter (M). SHAPEIT2 performance is denoted by the horizontal red line (no M parameter). These results are for the UK BiLEVE test set (N = 49,074) for the region at chr. 20: 10–20 Mb (2,518 SNPs). Computation was performed on an Intel Xeon E5-2690 CPU using four threads. Increasing M improves accuracy at the cost of increased computation time.

Supplementary Figure 2 Number of perfect matches as iteration and sample size vary.

The average number of 2-Mb windows (of 31 windows) per individual where a Hamming distance of zero was found with a conditioning haplotype, plotted against the iteration number of the SHAPEIT3 algorithm. Lines are colored by sample size. The number of perfect match windows increases rather dramatically with sample size. When a perfect match is found, the SHAPEIT3 algorithm skips HMM calculations in that window. This suggests that, as sample sizes increase, less time needs to be spent on inference per individual, owing to longer haplotype matches. The dip at iteration 16 occurs after the pruning step of the algorithm, where the HMM step needs to be recalculated regardless of matching haplotypes because of the graph structure having changed; we resample all haplotypes after this step.

Supplementary Figure 3 Estimated haplotypes in trio children phased using SHAPEIT3 on the full UKB data set of 152,112 samples.

Each line represents the phase of a single individual. Changes in color between dark and light blue represent switch errors. Switch errors were determined by comparing the estimated phase to that inferred when using the trio parents.

Supplementary Figure 4 Estimated haplotypes in trio children phased using SHAPEIT3 together with an unrelated subset of 10,000 UKB samples.

Each line represents the phase of a single individual. Changes in color between dark and light blue represent switch errors. Switch errors were determined by comparing the estimated phase to that inferred when using the trio parents.

Supplementary Figure 5 Estimated haplotypes in trio children phased using SHAPEIT3 together with an unrelated subset of 1,000 UKB samples.

Each line represents the phase of a single individual. Changes in color between dark and light blue represent switch errors. Switch errors were determined by comparing the estimated phase to that inferred when using the trio parents.

Supplementary Figure 6 Example of IBD sharing of siblings inferred by the HMM before and after filtering.

Black circles denote the IBS state at each marker across chromosome 20. The colored bars represent the IBD state inferred by our HMM before (top) and after (bottom) filtering out stretches of IBD less than 10 cM in length. The filtered state sequence provides a more conservative estimate of IBD1 sharing and is closer to the IBD patterns we would expect to see in a sibling pair.

Supplementary Figure 7 Comparison of switch error rates for HAPI-UR 3× and SHAPEIT3.

Methods were run on N = 49,074 UK BiLEVE samples in total. Accuracy was compared for each of the 384 phased individuals. Left, prior to filtering out short IBD sequences; right, after filtering out short IBD sequences. Dashed red lines indicate an error rate of 5%. After filtering, the rates are slightly lower for both methods and have lower variance. This suggests that filtering did indeed remove some stretches that induced incorrect phasing in the validation haplotypes.

Supplementary Figure 8 Effect of IBD filtering on switch error rate.

Variation in the SER achieved by SHAPEIT3 applied to all N = 49,074 UK BiLEVE samples. Switch error is measured on phased haplotypes derived form the 384 likely sibling pairs. Left, prior to filtering out short IBD sequences; right, after filtering out short IBD sequences. Horizontal and vertical dashed red lines represent the median error rate and median yield, respectively. In both cases, there is a small preponderance of individuals with SERs above the median at lower yields relative to the number above the median at higher yields (top left quadrant versus top right quadrant). This decreases after filtering from 110:72 to 96:86.

Supplementary information

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Supplementary Figures 1–8, Supplementary Tables 1–4 and Supplementary Note. (PDF 2340 kb)

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

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