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Integrated model for genomic prediction under additive and non-additive genetic architecture
Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749549/ https://www.ncbi.nlm.nih.gov/pubmed/36531414 http://dx.doi.org/10.3389/fpls.2022.1027558 |
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author | Budhlakoti, Neeraj Mishra, Dwijesh Chandra Majumdar, Sayanti Guha Kumar, Anuj Srivastava, Sudhir Rai, S. N. Rai, Anil |
author_facet | Budhlakoti, Neeraj Mishra, Dwijesh Chandra Majumdar, Sayanti Guha Kumar, Anuj Srivastava, Sudhir Rai, S. N. Rai, Anil |
author_sort | Budhlakoti, Neeraj |
collection | PubMed |
description | Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM’s performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance. |
format | Online Article Text |
id | pubmed-9749549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97495492022-12-15 Integrated model for genomic prediction under additive and non-additive genetic architecture Budhlakoti, Neeraj Mishra, Dwijesh Chandra Majumdar, Sayanti Guha Kumar, Anuj Srivastava, Sudhir Rai, S. N. Rai, Anil Front Plant Sci Plant Science Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM’s performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9749549/ /pubmed/36531414 http://dx.doi.org/10.3389/fpls.2022.1027558 Text en Copyright © 2022 Budhlakoti, Mishra, Majumdar, Kumar, Srivastava, Rai and Rai https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Budhlakoti, Neeraj Mishra, Dwijesh Chandra Majumdar, Sayanti Guha Kumar, Anuj Srivastava, Sudhir Rai, S. N. Rai, Anil Integrated model for genomic prediction under additive and non-additive genetic architecture |
title | Integrated model for genomic prediction under additive and non-additive genetic architecture |
title_full | Integrated model for genomic prediction under additive and non-additive genetic architecture |
title_fullStr | Integrated model for genomic prediction under additive and non-additive genetic architecture |
title_full_unstemmed | Integrated model for genomic prediction under additive and non-additive genetic architecture |
title_short | Integrated model for genomic prediction under additive and non-additive genetic architecture |
title_sort | integrated model for genomic prediction under additive and non-additive genetic architecture |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749549/ https://www.ncbi.nlm.nih.gov/pubmed/36531414 http://dx.doi.org/10.3389/fpls.2022.1027558 |
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