Identifying barley pan-genome sequence anchors using genetic mapping and machine learning

Journal article

Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji. (2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning. Theoretical and Applied Genetics. 133(9), pp. 2535-2544. https://doi.org/10.1007/s00122-020-03615-y
AuthorsGao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji
Abstract

We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, building more comprehensive pan-genomes is of great importance in genetic research and breeding. Obtaining a thousand-genotype scale pan-genome using deep-sequencing data is currently impractical for species like barley which has a huge and highly repetitive genome. To this end, we attempted to identify barley pan-genome sequence anchors from a large quantity of genotype-by-sequencing (GBS) datasets by combining genetic mapping and machine learning algorithms. Based on the GBS sequences from 11,166 domesticated and 1140 wild barley genotypes, we identified 1.844 million pan-genome sequence anchors. Of them, 532,253 were identified as presence/absence variation (PAV) tags. Through aligning these PAV tags to the genome of hulless barley genotype Zangqing320, our analysis resulted in a validation of 83.6% of them from the domesticated genotypes and 88.6% from the wild barley genotypes. Association analyses against flowering time, plant height and kernel size showed that the relative importance of the PAV and non-PAV tags varied for different traits. The pan-genome sequence anchors based on GBS tags can facilitate the construction of a comprehensive pan-genome and greatly assist various genetic studies including identification of structural variation, genetic mapping and breeding in barley.

KeywordsAlgorithms; Chromosome Mapping; Plant Genome; Genotype; Hordeum genetics; Linkage Disequilibrium; Machine Learning
Year01 Jan 2020
JournalTheoretical and Applied Genetics
Journal citation133 (9), pp. 2535-2544
PublisherSpringer
ISSN0040-5752
Digital Object Identifier (DOI)https://doi.org/10.1007/s00122-020-03615-y
PubMed ID32448920
Web address (URL)https://link.springer.com/article/10.1007/s00122-020-03615-y
Open accessPublished as non-open access
Research or scholarlyResearch
Page range2535-2544
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All rights reserved
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Controlled
Output statusPublished
Publication dates
Print24 May 2020
Publication process dates
Accepted16 May 2020
Deposited11 Jan 2023
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© Springer-Verlag GmbH Germany, part of Springer Nature 2020.

Place of publicationGermany
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