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....
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1027558/full |
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| author | Neeraj Budhlakoti Dwijesh Chandra Mishra Sayanti Guha Majumdar Anuj Kumar Sudhir Srivastava S. N. Rai Anil Rai |
| author_facet | Neeraj Budhlakoti Dwijesh Chandra Mishra Sayanti Guha Majumdar Anuj Kumar Sudhir Srivastava S. N. Rai Anil Rai |
| author_sort | Neeraj Budhlakoti |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T15:42:41Z |
| format | Article |
| id | doaj.art-832238d555be459eba07a6cf146a4035 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-11T15:42:41Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-832238d555be459eba07a6cf146a40352022-12-22T04:15:43ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-11-011310.3389/fpls.2022.10275581027558Integrated model for genomic prediction under additive and non-additive genetic architectureNeeraj Budhlakoti0Dwijesh Chandra Mishra1Sayanti Guha Majumdar2Anuj Kumar3Sudhir Srivastava4S. N. Rai5Anil Rai6Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaDivision of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaDivision of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaDepartment of Microbiology and Immunology, Dalhousie University, Halifax, NS, CanadaDivision of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaBioinformatics and Biostatistics Department, University of Louisville, Louisville, KY, United StatesDivision of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaUsing 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.https://www.frontiersin.org/articles/10.3389/fpls.2022.1027558/fullGBLUPGEBVsk-RCVnonparametricparametricSVM |
| spellingShingle | Neeraj Budhlakoti Dwijesh Chandra Mishra Sayanti Guha Majumdar Anuj Kumar Sudhir Srivastava S. N. Rai Anil Rai Integrated model for genomic prediction under additive and non-additive genetic architecture Frontiers in Plant Science GBLUP GEBVs k-RCV nonparametric parametric SVM |
| 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 | GBLUP GEBVs k-RCV nonparametric parametric SVM |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1027558/full |
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