Accurate, scalable and integrative haplotype estimation

By Olivier Delaneau, Jean-François Zagury, M. Robinson, Jonathan Marchini, Emmanouil T. Dermitzakis. Nature Communications, 2019

557 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9e6ccc85fbe28fc89e210ac89fd71f792ffaa478

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Fast two-stage phasing of large-scale sequence data. (2021, methodology): “26,27 This initialization algorithm phases one marker at a time in chromosome order.”