Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | https://www.revistas.usp.br/sa/article/view/130922 |
Resumo: | Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy. |
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Scientia Agrícola (Online) |
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Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populationsgenome-wide selectiondominance value predictionprediction accuracyQuantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2017-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/13092210.1590/1678-992x-2015-0479Scientia Agricola; v. 74 n. 1 (2017); 41-50Scientia Agricola; Vol. 74 No. 1 (2017); 41-50Scientia Agricola; Vol. 74 Núm. 1 (2017); 41-501678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/130922/127381Copyright (c) 2017 Scientia Agricolainfo:eu-repo/semantics/openAccessViana, José Marcelo SorianoPiepho, Hans-PeterSilva, Fabyano Fonseca e2017-06-12T11:44:51Zoai:revistas.usp.br:article/130922Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2017-06-12T11:44:51Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations |
title |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations |
spellingShingle |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations Viana, José Marcelo Soriano genome-wide selection dominance value prediction prediction accuracy |
title_short |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations |
title_full |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations |
title_fullStr |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations |
title_full_unstemmed |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations |
title_sort |
Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations |
author |
Viana, José Marcelo Soriano |
author_facet |
Viana, José Marcelo Soriano Piepho, Hans-Peter Silva, Fabyano Fonseca e |
author_role |
author |
author2 |
Piepho, Hans-Peter Silva, Fabyano Fonseca e |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Viana, José Marcelo Soriano Piepho, Hans-Peter Silva, Fabyano Fonseca e |
dc.subject.por.fl_str_mv |
genome-wide selection dominance value prediction prediction accuracy |
topic |
genome-wide selection dominance value prediction prediction accuracy |
description |
Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-02-01 |
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://www.revistas.usp.br/sa/article/view/130922 10.1590/1678-992x-2015-0479 |
url |
https://www.revistas.usp.br/sa/article/view/130922 |
identifier_str_mv |
10.1590/1678-992x-2015-0479 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.revistas.usp.br/sa/article/view/130922/127381 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2017 Scientia Agricola info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2017 Scientia Agricola |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz |
publisher.none.fl_str_mv |
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz |
dc.source.none.fl_str_mv |
Scientia Agricola; v. 74 n. 1 (2017); 41-50 Scientia Agricola; Vol. 74 No. 1 (2017); 41-50 Scientia Agricola; Vol. 74 Núm. 1 (2017); 41-50 1678-992X 0103-9016 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1787713260545376256 |