Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Animal Sciences (Online) |
Texto Completo: | https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/32023 |
Resumo: | Accuracy of genomic prediction was compared using three parametric and semi parametric methods, including BayesA, Bayesian LASSO and Reproducing kernel Hilbert spaces regression under various levels of heritability (0.15, 0.3 and 0.45), different number of markers (500, 750 and 1000) and generation intervals of validating set. A historical population of 1000 individuals with equal sex ratio was simulated for 100 generations at constant size. It followed by 100 extra generations of gradually reducing size down to 500 individuals in generation 200. Individuals of generation 200 were mated randomly for 10 more generations applying litter size of 5 to expand the historical generation. Finally, 50 males and 500 females chosen from generation 210 were randomly mated to generate 10 more generations of recent population. Individuals born in generation 211 considered as the training set while the validation set was composed of individuals either from generations 213, 215 or 217. The genome comprised one chromosome of 100 cM length carrying 50 QTLs. There was no significant difference between accuracy of investigated methods (p > 0.05) but among three methods, the highest mean accuracy (0.659) was observed for BayesA. By increasing the heritability, the average genomic accuracy increased from 0.53 to 0.75 (p < 0.05). The number of SNPs affected the accuracy and accuracies increased as number of SNPs increased; therefore, the highest accuracy was for the case number of SNPs=1000. With getting away from validating set, the accuracies decreased and the most severe decay observed in the case of low heritability. Decreasing the accuracy across generations affected by marker density but was independent from investigated methods. |
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Acta Scientiarum. Animal Sciences (Online) |
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Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methodsaccuracygenomicsemi parametric methodsgenetic architectureAccuracy of genomic prediction was compared using three parametric and semi parametric methods, including BayesA, Bayesian LASSO and Reproducing kernel Hilbert spaces regression under various levels of heritability (0.15, 0.3 and 0.45), different number of markers (500, 750 and 1000) and generation intervals of validating set. A historical population of 1000 individuals with equal sex ratio was simulated for 100 generations at constant size. It followed by 100 extra generations of gradually reducing size down to 500 individuals in generation 200. Individuals of generation 200 were mated randomly for 10 more generations applying litter size of 5 to expand the historical generation. Finally, 50 males and 500 females chosen from generation 210 were randomly mated to generate 10 more generations of recent population. Individuals born in generation 211 considered as the training set while the validation set was composed of individuals either from generations 213, 215 or 217. The genome comprised one chromosome of 100 cM length carrying 50 QTLs. There was no significant difference between accuracy of investigated methods (p > 0.05) but among three methods, the highest mean accuracy (0.659) was observed for BayesA. By increasing the heritability, the average genomic accuracy increased from 0.53 to 0.75 (p < 0.05). The number of SNPs affected the accuracy and accuracies increased as number of SNPs increased; therefore, the highest accuracy was for the case number of SNPs=1000. With getting away from validating set, the accuracies decreased and the most severe decay observed in the case of low heritability. Decreasing the accuracy across generations affected by marker density but was independent from investigated methods. Editora da Universidade Estadual de Maringá2016-11-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/3202310.4025/actascianimsci.v38i4.32023Acta Scientiarum. Animal Sciences; Vol 38 No 4 (2016); 447-453Acta Scientiarum. Animal Sciences; v. 38 n. 4 (2016); 447-4531807-86721806-2636reponame:Acta Scientiarum. Animal Sciences (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/32023/pdfCopyright (c) 2016 Acta Scientiarum. Animal Sciencesinfo:eu-repo/semantics/openAccessAtefi, AbbasShadparvar, Abdol AhadGhavi Hossein-Zadeh, Navid2022-02-20T21:50:06Zoai:periodicos.uem.br/ojs:article/32023Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSciPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSci/oaiactaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com1807-86721806-2636opendoar:2022-02-20T21:50:06Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods |
title |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods |
spellingShingle |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods Atefi, Abbas accuracy genomic semi parametric methods genetic architecture |
title_short |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods |
title_full |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods |
title_fullStr |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods |
title_full_unstemmed |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods |
title_sort |
Comparison of whole genome prediction accuracy across generations using parametric and semi parametric methods |
author |
Atefi, Abbas |
author_facet |
Atefi, Abbas Shadparvar, Abdol Ahad Ghavi Hossein-Zadeh, Navid |
author_role |
author |
author2 |
Shadparvar, Abdol Ahad Ghavi Hossein-Zadeh, Navid |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Atefi, Abbas Shadparvar, Abdol Ahad Ghavi Hossein-Zadeh, Navid |
dc.subject.por.fl_str_mv |
accuracy genomic semi parametric methods genetic architecture |
topic |
accuracy genomic semi parametric methods genetic architecture |
description |
Accuracy of genomic prediction was compared using three parametric and semi parametric methods, including BayesA, Bayesian LASSO and Reproducing kernel Hilbert spaces regression under various levels of heritability (0.15, 0.3 and 0.45), different number of markers (500, 750 and 1000) and generation intervals of validating set. A historical population of 1000 individuals with equal sex ratio was simulated for 100 generations at constant size. It followed by 100 extra generations of gradually reducing size down to 500 individuals in generation 200. Individuals of generation 200 were mated randomly for 10 more generations applying litter size of 5 to expand the historical generation. Finally, 50 males and 500 females chosen from generation 210 were randomly mated to generate 10 more generations of recent population. Individuals born in generation 211 considered as the training set while the validation set was composed of individuals either from generations 213, 215 or 217. The genome comprised one chromosome of 100 cM length carrying 50 QTLs. There was no significant difference between accuracy of investigated methods (p > 0.05) but among three methods, the highest mean accuracy (0.659) was observed for BayesA. By increasing the heritability, the average genomic accuracy increased from 0.53 to 0.75 (p < 0.05). The number of SNPs affected the accuracy and accuracies increased as number of SNPs increased; therefore, the highest accuracy was for the case number of SNPs=1000. With getting away from validating set, the accuracies decreased and the most severe decay observed in the case of low heritability. Decreasing the accuracy across generations affected by marker density but was independent from investigated methods. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-11-07 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/32023 10.4025/actascianimsci.v38i4.32023 |
url |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/32023 |
identifier_str_mv |
10.4025/actascianimsci.v38i4.32023 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/32023/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 Acta Scientiarum. Animal Sciences info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 Acta Scientiarum. Animal Sciences |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Animal Sciences; Vol 38 No 4 (2016); 447-453 Acta Scientiarum. Animal Sciences; v. 38 n. 4 (2016); 447-453 1807-8672 1806-2636 reponame:Acta Scientiarum. Animal Sciences (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Animal Sciences (Online) |
collection |
Acta Scientiarum. Animal Sciences (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com |
_version_ |
1799315361755037696 |