UTILIZING DISCRETE HIDDEN MARKOV MODELS TO ANALYZE TETRAPLOID PLANT BREEDING
DOI:
https://doi.org/10.25077/jmua.13.4.244-256.2024Keywords:
hidden Markov model, tetraploidAbstract
In plant heredity, the phenotype is the result of observation that can be directly observed, while the genotype is the underlying hidden factor that underlies the expression of the phenotype. The genotype is an important aspect that needs to be understood to explain the pattern of trait inheritance and predict trait inheritance in subsequent generations. The discrete hidden Markov model is a model generated by pair of an unobserved Markov chain and an observation process. This model can be applied to tetraploid plant crosses by modeling genotypes as hidden state and phenotypes as the obeservation process. The probability of dominant phenotype in monohybrid, dihybrid and trihybrid crosses occurring over ten generations during that period is as follows 61,305%, 37,583%, and 23,041%. Furthermore, as more traits are crossed, the probability of dominant phenotype appearing within ten generations decreases. When the dominant phenotype occurs over ten generations, the same genotype can be obtained in monohybrid, dihybrid, and trihybrid crosses, which is heterozygous in the first and second generations, while from the third to the tenth generation it is homozygous dominant.Downloads
References
Reece, J.B., Urry, L.A., Cain, M.L., Wasserman, S.A., Minorsky, P.V., Jackson, R.B., 2011, Campbell Biologi, 9th Edition, Pearson Education Inc, USA
Hayati, N., Setiawaty, B., Purnaba, I.G.P., 2023, The Application of Discrete Hidden Markov Model on Crosses of Diploid Plant, Barekeng: Journal of Mathematics and Its Applications, 17 : 1449 – 1462
Harris, P.D., Narducci, A., Gebhardt, C., Cordes, T., Weiss, S., Lerner, E., 2022, Multi-parameter Photon-by-Photon Hidden Markov Modeling, Nature Communications, 13 : 1 – 12
Hsiao, J.H., An, J., Hui, V.K.S., Zheng, Y., Chan, A.B., 2022, Understanding The Role of Eye Movement Consistency in Face Recognition and Autism Through Integrating Deep Neural Networks and Hidden Markov Models, njp Science of Learning, 7 : 1 – 13
Mousavi, N., Monemian , M., Daneshmand, P.G., Mirmohammadsadeghi, M., Zekri, M., Rabbani, H., 2023, Cyst Identification in Retinal Optical Coherence Tomography Images using Hidden Markov Model, Scientific Reports, 13 : 1 – 15
Mohammadi, F., Visagan, S., Gross, S.M., Karginov, L., Jagarde, J., Heiser, L.M., Meyer, A.S., 2022, A Lineage Tree-based Hidden Markov Model Quantifies Cellular Heterogeneity and Plasticity, Communications Biology, 5 : 1 – 14
Ludwig, R., Pouymayou, B., Balermpas, P., Unkelbach, J., 2021, A Hidden Markov Model for Lymphatic Tumor Progression in The Head and Neck, Scientific Reports, 11 : 1 – 17
Thorvaldsen, S., 2022, A Tutorial on Markov Models Based on Mendel’s Classical Experiments, Journal of Bioinformatics and Computational Biology, 3 : 1441 – 1460
Sattler, M.C., Carvalho, C.R., Clarindo, W.R., 2015, The Polyploidy and Its Key Role in Plant Breeding, Planta, 243 : 281 – 296
Rabiner, L.R., 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of The IEEE, Scottsdale, 77 : 257 – 285
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
All articles published in Jurnal Matematika UNAND (JMUA) are open access and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). This license allows published content to be freely accessed, shared, redistributed, and adapted for any purpose, including commercial use, provided that the applicable license conditions are followed.
Copyright Holder
The copyright of articles published in Jurnal Matematika UNAND is held by the Departemen Matematika dan Sains Data, Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA), Universitas Andalas (UNAND), in accordance with the journal's stated copyright policy.
Author Rights
Under the CC BY-SA 4.0 license, authors may share, distribute, and reuse their published articles, subject to the applicable license conditions. These rights include:
-
Self-Archiving: Authors may deposit their published articles in institutional repositories, personal websites, or academic platforms.
-
Reuse: Authors may reuse their published work in subsequent publications, research, teaching materials, or other lawful purposes, provided that appropriate attribution is maintained.
-
Distribution: Authors may share and distribute their published articles without requiring additional permission under the terms of the license.
Creative Commons License (CC BY-SA 4.0)
Under the CC BY-SA 4.0 license, users are free to:
-
Share: Copy and redistribute the material in any medium or format.
-
Adapt: Remix, transform, and build upon the material for any purpose, including commercial purposes.
These permissions are subject to the following conditions:
-
Attribution: Users must give appropriate credit to the original author(s), retain relevant copyright notices, provide a link to the license, and indicate if changes were made. Attribution must not imply endorsement by the author(s), copyright holder, or journal.
-
ShareAlike: If users remix, transform, or build upon the material, they must distribute their contributions under the same CC BY-SA 4.0 license or a compatible license.
-
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from exercising the rights granted by the license.
For more information about the CC BY-SA 4.0 license, please visit:
https://creativecommons.org/licenses/by-sa/4.0/
Third-Party Content
Third-party materials, including figures, tables, photographs, and illustrations, are covered by the article's CC BY-SA 4.0 license only when their inclusion under that license is authorized.
Materials excluded from the license must be clearly identified with appropriate copyright notices and attribution. Authors are responsible for obtaining any necessary permissions, while users must comply with the applicable licensing terms or permissions when reusing excluded materials.
Copyright and Licensing Information
Copyright and licensing information should be clearly displayed on each published article's landing page and included in the full-text versions (HTML and PDF), where applicable, to ensure transparency regarding copyright ownership and permitted reuse.



