Accurate, scalable and integrative haplotype estimation

Olivier Delaneau (), Jean-François Zagury, Matthew R. Robinson, Jonathan L. Marchini and Emmanouil T. Dermitzakis
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Olivier Delaneau: University of Lausanne, Génopode
Jean-François Zagury: HESAM Université
Matthew R. Robinson: University of Lausanne, Génopode
Jonathan L. Marchini: University of Oxford
Emmanouil T. Dermitzakis: University of Geneva Medical School

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.

Date: 2019
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DOI: 10.1038/s41467-019-13225-y

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