Integrating read-based and population-based phasing for dense and accurate haplotyping of individual genomes

• Published in: Bioinformatics Vol. 35; no. 14; pp. i242 - i248
• Main Author: Bansal, Vikas
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15-07-2019
• Subjects: Ismb/Eccb 2019 Conference Proceedings
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btz329

Abstract
Motivation
Reconstruction of haplotypes for human genomes is an important problem in medical and population genetics. Hi-C sequencing generates read pairs with long-range haplotype information that can be computationally assembled to generate chromosome-spanning haplotypes. However, the haplotypes have limited completeness and low accuracy. Haplotype information from population reference panels can potentially be used to improve the completeness and accuracy of Hi-C haplotyping.
Results
In this paper, we describe a likelihood based method to integrate short-range haplotype information from a population reference panel of haplotypes with the long-range haplotype information present in sequence reads from methods such as Hi-C to assemble dense and highly accurate haplotypes for individual genomes. Our method leverages a statistical phasing method and a maximum spanning tree algorithm to determine the optimal second-order approximation of the population-based haplotype likelihood for an individual genome. The population-based likelihood is encoded using pseudo-reads which are then used as input along with sequence reads for haplotype assembly using an existing tool, HapCUT2. Using whole-genome Hi-C data for two human genomes (NA19240 and NA12878), we demonstrate that this integrated phasing method enables the phasing of 97–98% of variants, reduces the switch error rates by 3–6-fold, and outperforms an existing method for combining phase information from sequence reads with population-based phasing. On Strand-seq data for NA12878, our method improves the haplotype completeness from 71.4 to 94.6% and reduces the switch error rate 2-fold, demonstrating its utility for phasing using multiple sequencing technologies.
Availability and implementation
Code and datasets are available at https://github.com/vibansal/IntegratedPhasing.