Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models

Detalhes bibliográficos
Autor(a) principal: Peixoto, L.A.
Data de Publicação: 2016
Outros Autores: Bhering, L.L., Cruz, C.D.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: http://dx.doi.org/10.4238/gmr15048874
http://www.locus.ufv.br/handle/123456789/12215
Resumo: Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F 2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F 2 populations. F 2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F 2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.
id UFV_6f275114dc6c6324ae8742d1c3f6c087
oai_identifier_str oai:locus.ufv.br:123456789/12215
network_acronym_str UFV
network_name_str LOCUS Repositório Institucional da UFV
repository_id_str 2145
spelling Peixoto, L.A.Bhering, L.L.Cruz, C.D.2017-10-20T09:18:32Z2017-10-20T09:18:32Z2016-11-2116765680http://dx.doi.org/10.4238/gmr15048874http://www.locus.ufv.br/handle/123456789/12215Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F 2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F 2 populations. F 2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F 2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.engGenetics and Molecular Research15 (4), gmr15048874, november 2016Genomic predictionHeritabilityPrediction abilityBreedingQuantitative geneticsDetermination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection modelsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALgmr-15-04-gmr.15048874.pdfgmr-15-04-gmr.15048874.pdftexto completoapplication/pdf2140067https://locus.ufv.br//bitstream/123456789/12215/1/gmr-15-04-gmr.15048874.pdf4ffcf2a7dd49d61ab423402cccb13592MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/12215/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILgmr-15-04-gmr.15048874.pdf.jpggmr-15-04-gmr.15048874.pdf.jpgIM Thumbnailimage/jpeg4911https://locus.ufv.br//bitstream/123456789/12215/3/gmr-15-04-gmr.15048874.pdf.jpg2b90522df22744cfcf469c64bdec90dbMD53123456789/122152017-10-20 22:00:33.653oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452017-10-21T01:00:33LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
title Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
spellingShingle Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
Peixoto, L.A.
Genomic prediction
Heritability
Prediction ability
Breeding
Quantitative genetics
title_short Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
title_full Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
title_fullStr Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
title_full_unstemmed Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
title_sort Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models
author Peixoto, L.A.
author_facet Peixoto, L.A.
Bhering, L.L.
Cruz, C.D.
author_role author
author2 Bhering, L.L.
Cruz, C.D.
author2_role author
author
dc.contributor.author.fl_str_mv Peixoto, L.A.
Bhering, L.L.
Cruz, C.D.
dc.subject.pt-BR.fl_str_mv Genomic prediction
Heritability
Prediction ability
Breeding
Quantitative genetics
topic Genomic prediction
Heritability
Prediction ability
Breeding
Quantitative genetics
description Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F 2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F 2 populations. F 2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F 2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.
publishDate 2016
dc.date.issued.fl_str_mv 2016-11-21
dc.date.accessioned.fl_str_mv 2017-10-20T09:18:32Z
dc.date.available.fl_str_mv 2017-10-20T09:18:32Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.4238/gmr15048874
http://www.locus.ufv.br/handle/123456789/12215
dc.identifier.issn.none.fl_str_mv 16765680
identifier_str_mv 16765680
url http://dx.doi.org/10.4238/gmr15048874
http://www.locus.ufv.br/handle/123456789/12215
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt-BR.fl_str_mv 15 (4), gmr15048874, november 2016
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Genetics and Molecular Research
publisher.none.fl_str_mv Genetics and Molecular Research
dc.source.none.fl_str_mv reponame:LOCUS Repositório Institucional da UFV
instname:Universidade Federal de Viçosa (UFV)
instacron:UFV
instname_str Universidade Federal de Viçosa (UFV)
instacron_str UFV
institution UFV
reponame_str LOCUS Repositório Institucional da UFV
collection LOCUS Repositório Institucional da UFV
bitstream.url.fl_str_mv https://locus.ufv.br//bitstream/123456789/12215/1/gmr-15-04-gmr.15048874.pdf
https://locus.ufv.br//bitstream/123456789/12215/2/license.txt
https://locus.ufv.br//bitstream/123456789/12215/3/gmr-15-04-gmr.15048874.pdf.jpg
bitstream.checksum.fl_str_mv 4ffcf2a7dd49d61ab423402cccb13592
8a4605be74aa9ea9d79846c1fba20a33
2b90522df22744cfcf469c64bdec90db
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)
repository.mail.fl_str_mv fabiojreis@ufv.br
_version_ 1801213109951528960