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

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Title: Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.
Authors: Gao, Shang1,2 (AUTHOR), Wu, Jinran3 (AUTHOR), Stiller, Jiri1 (AUTHOR), Zheng, Zhi1 (AUTHOR), Zhou, Meixue2 (AUTHOR), Wang, You-Gan3 (AUTHOR) you-gan.wang@qut.edu.au, Liu, Chunji1 (AUTHOR) Chunji.liu@csiro.au
Source: Theoretical & Applied Genetics. Sep2020, Vol. 133 Issue 9, p2535-2544. 10p.
Subjects: Barley, Machine learning, Hordeum, Flowering time, Anchors, Genotypes
Abstract: Key message: 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. [ABSTRACT FROM AUTHOR]
Copyright of Theoretical & Applied Genetics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.
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  Data: <searchLink fieldCode="JN" term="%22Theoretical+%26+Applied+Genetics%22">Theoretical & Applied Genetics</searchLink>. Sep2020, Vol. 133 Issue 9, p2535-2544. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Barley%22">Barley</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Hordeum%22">Hordeum</searchLink><br /><searchLink fieldCode="DE" term="%22Flowering+time%22">Flowering time</searchLink><br /><searchLink fieldCode="DE" term="%22Anchors%22">Anchors</searchLink><br /><searchLink fieldCode="DE" term="%22Genotypes%22">Genotypes</searchLink>
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  Data: Key message: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Theoretical & Applied Genetics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00122-020-03615-y
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        Text: English
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        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Hordeum
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      – SubjectFull: Anchors
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      – SubjectFull: Genotypes
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              Text: Sep2020
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