Year: 2022
Type: article
Abstract: An important challenge in genome assembly is haplotype phasing, that is, to reconstruct the different haplotype sequences of an individual genome. Phasing becomes considerably more difficult with increasing ploidy, which makes polyploid phasing a notoriously hard computational problem. We present a novel genetic phasing method for plant breeding with the aim to phase two deep-sequenced parental samples with the help of a large number of progeny samples sequenced at low depth. The key ideas underlying our approach are to (i) integrate the individually weak Mendelian progeny signals with a Bayesian log-likelihood model, (ii) cluster alleles according to their likelihood of co-occurrence, and (iii) assign them to haplotypes via an interval scheduling approach. We show on two deep-sequenced parental and 193 low-depth progeny potato samples that our approach computes high-quality sparse phasings and that it scales to whole genomes.
Source: iScience
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,000
Awards: WhatsHap: Software für die Genomforschung und klinische Diagnostik auf Haplotypebene, Vom Genotyp zum Haplotyp: Readbasierte integrierte Haplotypisierung ganzer Chromosomen, G17649921, G2642650165, G2958780423, EXC 2048: CEPLAS Exzellenzcluster für Pflanzenwissenschaften, G7735813670, G9000025545
OpenAlex ID: W4281483539
