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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.

Original publication

DOI

10.1038/s41467-019-13225-y

Type

Journal article

Journal

Nature communications

Publication Date

28/11/2019

Volume

10

Addresses

Department of Computational Biology, University of Lausanne, Génopode, 1015, Lausanne, Switzerland. olivier.delaneau@unil.ch.

Keywords

Humans, Data Interpretation, Statistical, Sample Size, Sequence Analysis, DNA, Genotype, Haplotypes, Polymorphism, Single Nucleotide, Software, Biological Specimen Banks, High-Throughput Nucleotide Sequencing, Datasets as Topic