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


Gao, S and Wu, J and Stiller, J and Zheng, Z and Zhou, M and Wang, YG and Liu, C, Identifying barley pan-genome sequence anchors using genetic mapping and machine learning, Theoretical and Applied Genetics, 133, (9) pp. 2535-2544. ISSN 0040-5752 (2020) [Refereed Article]

Copyright Statement

Springer-Verlag GmbH Germany, part of Springer Nature 2020

DOI: doi:10.1007/s00122-020-03615-y


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.

Key message: We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning.

Item Details

Item Type:Refereed Article
Keywords:barley, pan-genome, machine learning
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Crop and pasture production
Research Field:Agrochemicals and biocides (incl. application)
Objective Division:Plant Production and Plant Primary Products
Objective Group:Grains and seeds
Objective Field:Barley
UTAS Author:Gao, S (Mr Shang Gao)
UTAS Author:Zhou, M (Professor Meixue Zhou)
ID Code:145367
Year Published:2020
Web of Science® Times Cited:4
Deposited By:TIA - Research Institute
Deposited On:2021-07-19
Last Modified:2021-09-16

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