Cargando…

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....

Descripción completa

Detalles Bibliográficos
Autores principales: Budhlakoti, Neeraj, Mishra, Dwijesh Chandra, Majumdar, Sayanti Guha, Kumar, Anuj, Srivastava, Sudhir, Rai, S. N., Rai, Anil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784850057387835392
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
work_keys_str_mv AT budhlakotineeraj integratedmodelforgenomicpredictionunderadditiveandnonadditivegeneticarchitecture
AT mishradwijeshchandra integratedmodelforgenomicpredictionunderadditiveandnonadditivegeneticarchitecture
AT majumdarsayantiguha integratedmodelforgenomicpredictionunderadditiveandnonadditivegeneticarchitecture
AT kumaranuj integratedmodelforgenomicpredictionunderadditiveandnonadditivegeneticarchitecture
AT srivastavasudhir integratedmodelforgenomicpredictionunderadditiveandnonadditivegeneticarchitecture
AT raisn integratedmodelforgenomicpredictionunderadditiveandnonadditivegeneticarchitecture
AT raianil integratedmodelforgenomicpredictionunderadditiveandnonadditivegeneticarchitecture