| Title: | Accurate, scalable and integrative haplotype estimation |
| Journal: | Nature Communications |
| Published: | 28 Nov 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31780650/ |
| DOI: | https://doi.org/10.1038/s41467-019-13225-y |
| URL: | https://www.nature.com/articles/s41467-019-13225-y.pdf |
| Citations: | 431 (218 in last 2 years) as of 8 Aug 2024 |
Publication 3976
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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.</p>
5 Authors
- Olivier Delaneau
- Jean-François Zagury
- Matthew R. Robinson
- Jonathan L. Marchini
- Emmanouil T. Dermitzakis
Enabling scientific discoveries that improve human health