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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 00405752 |
| DOI: | 10.1007/s00122-020-03615-y |
