TriPoly: haplotype estimation for polyploids using sequencing data of related individuals

Ehsan Motazedi, Dick de Ridder, Richard Finkers, Samantha Baldwin, Susan Thomson, Katrina Monaghan, Chris Maliepaard*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

Abstract

Motivation: Knowledge of haplotypes, i.e. phased and ordered marker alleles on a chromosome, is essential to answer many questions in genetics and genomics. By generating short pieces of DNA sequence, high-throughput modern sequencing technologies make estimation of haplotypes possible for single individuals. In polyploids, however, haplotype estimation methods usually require deep coverage to achieve sufficient accuracy. This often renders sequencing-based approaches too costly to be applied to large populations needed in studies of Quantitative Trait Loci. Results: We propose a novel haplotype estimation method for polyploids, TriPoly, that combines sequencing data with Mendelian inheritance rules to infer haplotypes in parent-offspring trios. Using realistic simulations of both short and long-read sequencing data for banana (Musa acuminata) and potato (Solanum tuberosum) trios, we show that TriPoly yields more accurate progeny haplotypes at low coverages compared to existing methods that work on single individuals. We also apply TriPoly to phase Single Nucleotide Polymorphisms on chromosome 5 for a family of tetraploid potato with 2 parents and 37 offspring sequenced with an RNA capture approach. We show that TriPoly haplotype estimates differ from those of the other methods mainly in regions with imperfect sequencing or mapping difficulties, as it does not rely solely on sequence reads and aims to avoid phasings that are not likely to have been passed from the parents to the offspring. Availability and implementation: TriPoly has been implemented in Python 3.5.2 (also compatible with Python 2.7.3 and higher) and can be freely downloaded at https://github.com/EhsanMotazedi/TriPoly. Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)3864-3872
JournalBioinformatics
Volume34
Issue number22
DOIs
Publication statusPublished - 15 Nov 2018

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