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Identifying barley pan-genome sequence anchors using genetic mapping and machine learning
Gao, Shang Wu, Ryan Stiller, Jiri Zheng, Zhi Zhou, Meixue Wang, You-Gan Liu, Chunji
Gao, Shang
Wu, Ryan
Stiller, Jiri
Zheng, Zhi
Zhou, Meixue
Wang, You-Gan
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.
Keywords
Algorithms, Chromosome Mapping, Plant Genome, Genotype, Hordeum genetics, Linkage Disequilibrium, Machine Learning
Date
2020
Type
Journal article
Journal
Book
Volume
133
Issue
9
Page Range
2535-2544
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled
Notes
© Springer-Verlag GmbH Germany, part of Springer Nature 2020.