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Identifying barley pan-genome sequence anchors using genetic mapping and machine learning
Citation
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
Abstract
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 |
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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 |
Downloads: | 0 |
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