Accurate, scalable and integrative haplotype estimation

• Published in: Nature communications Vol. 10; no. 1; pp. 5436 - 10
• Main Authors: Delaneau, Olivier, Zagury, Jean-François, Robinson, Matthew R., Marchini, Jonathan L., Dermitzakis, Emmanouil T.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.11.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/114/794; 631/208/514/1948; 631/208/728; Biological Specimen Banks; Data collection; Data Interpretation, Statistical; Datasets; Datasets as Topic; Genetics; Genomes; Genotype; Genotypes; Haplotypes; High-Throughput Nucleotide Sequencing; Human genetics; Humanities and Social Sciences; Humans; Life Sciences; multidisciplinary; Polymorphism, Single Nucleotide; Quantitative Methods; Sample Size; Science; Science (multidisciplinary); Sequence Analysis, DNA; Software
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-13225-y

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. Haplotype information inferred by phasing is useful in genetic and genomic analysis. Here, the authors develop SHAPEIT4, a phasing method that exhibits sub-linear running time, provides accurate haplotypes and enables integration of external phasing information.