A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding

Detalhes bibliográficos
Autor(a) principal: RAHIMI,MEHDI
Data de Publicação: 2022
Outros Autores: HERNANDEZ,MATEO V.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000100804
Resumo: Abstract Phenotypic-genotypic covariance and correlation have been useful in crop and animal breeding programs. In the study of diversity of natural populations and different cultivars of plants that are examined based on statistical design, estimation of genotypic-phenotypic covariance through expected value of statistical designs mean square is hard and time-consuming when the number of studied traits is high. Moreover, the lack of a program in this field and manual calculations make the estimation more complicated. Therefore, in this study, one program was developed in SAS that can be used to calculate the genotypic-phenotypic covariance matrix through the first part of the program based on the expected value of applied statistical designs mean square. Then, based on the covariance matrix computed from the previous design model, their correlation matrix was calculated using the second part of the program based on the interactive matrix language (IML) of SAS. The phenotypic-genotypic covariance matrices of the 12 studied traits of rice are calculated based on this code. This program could compute phenotypic-genotypic covariance and correlation matrices based on the expected value of any statistical designs.
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spelling A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breedingComputer programcovariance matrixestimationgenetic correlationplant breedingstatistical designAbstract Phenotypic-genotypic covariance and correlation have been useful in crop and animal breeding programs. In the study of diversity of natural populations and different cultivars of plants that are examined based on statistical design, estimation of genotypic-phenotypic covariance through expected value of statistical designs mean square is hard and time-consuming when the number of studied traits is high. Moreover, the lack of a program in this field and manual calculations make the estimation more complicated. Therefore, in this study, one program was developed in SAS that can be used to calculate the genotypic-phenotypic covariance matrix through the first part of the program based on the expected value of applied statistical designs mean square. Then, based on the covariance matrix computed from the previous design model, their correlation matrix was calculated using the second part of the program based on the interactive matrix language (IML) of SAS. The phenotypic-genotypic covariance matrices of the 12 studied traits of rice are calculated based on this code. This program could compute phenotypic-genotypic covariance and correlation matrices based on the expected value of any statistical designs.Academia Brasileira de Ciências2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000100804Anais da Academia Brasileira de Ciências v.94 n.1 2022reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202220200001info:eu-repo/semantics/openAccessRAHIMI,MEHDIHERNANDEZ,MATEO V.eng2022-04-13T00:00:00Zoai:scielo:S0001-37652022000100804Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2022-04-13T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
title A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
spellingShingle A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
RAHIMI,MEHDI
Computer program
covariance matrix
estimation
genetic correlation
plant breeding
statistical design
title_short A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
title_full A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
title_fullStr A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
title_full_unstemmed A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
title_sort A SAS code to estimate phenotypic-genotypic covariance and correlation matrices based on expected value of statistical designs to use in plant breeding
author RAHIMI,MEHDI
author_facet RAHIMI,MEHDI
HERNANDEZ,MATEO V.
author_role author
author2 HERNANDEZ,MATEO V.
author2_role author
dc.contributor.author.fl_str_mv RAHIMI,MEHDI
HERNANDEZ,MATEO V.
dc.subject.por.fl_str_mv Computer program
covariance matrix
estimation
genetic correlation
plant breeding
statistical design
topic Computer program
covariance matrix
estimation
genetic correlation
plant breeding
statistical design
description Abstract Phenotypic-genotypic covariance and correlation have been useful in crop and animal breeding programs. In the study of diversity of natural populations and different cultivars of plants that are examined based on statistical design, estimation of genotypic-phenotypic covariance through expected value of statistical designs mean square is hard and time-consuming when the number of studied traits is high. Moreover, the lack of a program in this field and manual calculations make the estimation more complicated. Therefore, in this study, one program was developed in SAS that can be used to calculate the genotypic-phenotypic covariance matrix through the first part of the program based on the expected value of applied statistical designs mean square. Then, based on the covariance matrix computed from the previous design model, their correlation matrix was calculated using the second part of the program based on the interactive matrix language (IML) of SAS. The phenotypic-genotypic covariance matrices of the 12 studied traits of rice are calculated based on this code. This program could compute phenotypic-genotypic covariance and correlation matrices based on the expected value of any statistical designs.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000100804
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000100804
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765202220200001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.94 n.1 2022
reponame:Anais da Academia Brasileira de Ciências (Online)
instname:Academia Brasileira de Ciências (ABC)
instacron:ABC
instname_str Academia Brasileira de Ciências (ABC)
instacron_str ABC
institution ABC
reponame_str Anais da Academia Brasileira de Ciências (Online)
collection Anais da Academia Brasileira de Ciências (Online)
repository.name.fl_str_mv Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)
repository.mail.fl_str_mv ||aabc@abc.org.br
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