How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data
The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F<sub>2</sub> population were genotyped with the DArTseq (sequen...
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MDPI AG
2020-10-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/21/20/7501 |
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| author | Magdalena Góralska Jan Bińkowski Natalia Lenarczyk Anna Bienias Agnieszka Grądzielewska Ilona Czyczyło-Mysza Kamila Kapłoniak Stefan Stojałowski Beata Myśków |
| author_facet | Magdalena Góralska Jan Bińkowski Natalia Lenarczyk Anna Bienias Agnieszka Grądzielewska Ilona Czyczyło-Mysza Kamila Kapłoniak Stefan Stojałowski Beata Myśków |
| author_sort | Magdalena Góralska |
| collection | DOAJ |
| description | The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F<sub>2</sub> population were genotyped with the DArTseq (sequencing-based diversity array technology). A maximum likelihood (MLH) algorithm (JoinMap 5.0) and three ML algorithms: logistic regression (LR), random forest and extreme gradient boosted trees (XGBoost), were used to select markers closely linked to the gene encoding wax layer. The allele conditioning the nonglaucous appearance of plants, derived from the cultivar Karlikovaja Zelenostebelnaja, was mapped at the chromosome 2R, which is the first report on this localization. The DNA sequence of DArT-Silico 3585843, closely linked to wax segregation detected by using ML methods, was indicated as one of the candidates controlling the studied trait. The putative gene encodes the ABCG11 transporter. |
| first_indexed | 2024-03-10T15:42:07Z |
| format | Article |
| id | doaj.art-2608021079fd447f969ca4b8f991a2aa |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T15:42:07Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-2608021079fd447f969ca4b8f991a2aa2023-11-20T16:42:19ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672020-10-012120750110.3390/ijms21207501How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS DataMagdalena Góralska0Jan Bińkowski1Natalia Lenarczyk2Anna Bienias3Agnieszka Grądzielewska4Ilona Czyczyło-Mysza5Kamila Kapłoniak6Stefan Stojałowski7Beata Myśków8Department of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71–434 Szczecin, PolandDepartment of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71–434 Szczecin, PolandDepartment of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71–434 Szczecin, PolandDepartment of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71–434 Szczecin, PolandInstitute of Plant Genetics, Breeding and Biotechnology, University of Life Sciences in Lublin, ul. Akademicka, 20–950 Lublin, PolandPolish Academy of Sciences, The Franciszek Górski Institute of Plant Physiology, Niezapominajek 21, 30–239 Kraków, PolandPolish Academy of Sciences, The Franciszek Górski Institute of Plant Physiology, Niezapominajek 21, 30–239 Kraków, PolandDepartment of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71–434 Szczecin, PolandDepartment of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71–434 Szczecin, PolandThe standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F<sub>2</sub> population were genotyped with the DArTseq (sequencing-based diversity array technology). A maximum likelihood (MLH) algorithm (JoinMap 5.0) and three ML algorithms: logistic regression (LR), random forest and extreme gradient boosted trees (XGBoost), were used to select markers closely linked to the gene encoding wax layer. The allele conditioning the nonglaucous appearance of plants, derived from the cultivar Karlikovaja Zelenostebelnaja, was mapped at the chromosome 2R, which is the first report on this localization. The DNA sequence of DArT-Silico 3585843, closely linked to wax segregation detected by using ML methods, was indicated as one of the candidates controlling the studied trait. The putative gene encodes the ABCG11 transporter.https://www.mdpi.com/1422-0067/21/20/7501ATP-binding cassette (ABC) transportersfatty acid desaturase (FAD), genetic mapglaucousnesslarge-scale sequence-based markers<i>Secale cereale</i> L. |
| spellingShingle | Magdalena Góralska Jan Bińkowski Natalia Lenarczyk Anna Bienias Agnieszka Grądzielewska Ilona Czyczyło-Mysza Kamila Kapłoniak Stefan Stojałowski Beata Myśków How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data International Journal of Molecular Sciences ATP-binding cassette (ABC) transporters fatty acid desaturase (FAD), genetic map glaucousness large-scale sequence-based markers <i>Secale cereale</i> L. |
| title | How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data |
| title_full | How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data |
| title_fullStr | How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data |
| title_full_unstemmed | How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data |
| title_short | How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data |
| title_sort | how machine learning methods helped find putative rye wax genes among gbs data |
| topic | ATP-binding cassette (ABC) transporters fatty acid desaturase (FAD), genetic map glaucousness large-scale sequence-based markers <i>Secale cereale</i> L. |
| url | https://www.mdpi.com/1422-0067/21/20/7501 |
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