Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models

Abstract The genetic variability present in the bean (Phaseolus vulgaris L.) germplasm that is currently used as an agricultural crop has been shown to be stable in production and is acceptable for human sustenance. Accordingly, to maintain as much of the available variability as possible, this stud...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Carias,Cíntia Machado de Oliveira Moulin, Guilhen,José Henrique Soler, Marçal,Tiago de Souza, Ferreira,Adésio, SilvaFerreira,Marcia Flores da
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal 2018
Materias:	Breeding cluster analysis genetic variability Phaseolus vulgaris L REM/BLUP
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202018000300251
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:scielo:S0718-16202018000300251
record_format	dspace
spelling	oai:scielo:S0718-162020180003002512019-02-04Genetic divergence toward the selection of promising bean progenitors via mixed multivariate modelsCarias,Cíntia Machado de Oliveira MoulinGuilhen,José Henrique SolerMarçal,Tiago de SouzaFerreira,AdésioSilvaFerreira,Marcia Flores da Breeding cluster analysis genetic variability Phaseolus vulgaris L REM/BLUP Abstract The genetic variability present in the bean (Phaseolus vulgaris L.) germplasm that is currently used as an agricultural crop has been shown to be stable in production and is acceptable for human sustenance. Accordingly, to maintain as much of the available variability as possible, this study aimed to examine the genetic divergence in the bean using multivariate analysis to identify the sources of genetic variability and enable breeders to recognize genetic combinations that have a greater chances of success before crossings are performed. This study was conducted in a randomized block design with three replications in the agricultural year 2015. The agronomic traits evaluated were the stem diameter (DIAM) in millimeters, plant height (PH) in centimeters, number of seedsper plant (NS), protein percentage (PROT), height of the first pod (HFP) in centimeters, pod number (PN), grain mass per plant (GM) in g plant−1, grain yield (GY) in kg ha−1, and straw yield (SY) in kg ha−1. To enable selection of the most divergent genotypes, twenty different genotypes were analyzed via clustering according to the average linkage criterion (UPGMA) using a matrix of the mean standardized Euclidean distances and principal component analysis based on the values predicted via a multivariate mixed model. The results obtained in this study revealed a high degree of genetic divergence and allowed the progenies to be allocated into different groups, as well as recommended crossings for future bean breeding programs.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería ForestalCiencia e investigación agraria v.45 n.3 20182018-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202018000300251en10.7764/rcia.v45i3.1902
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Breeding cluster analysis genetic variability Phaseolus vulgaris L REM/BLUP
spellingShingle	Breeding cluster analysis genetic variability Phaseolus vulgaris L REM/BLUP Carias,Cíntia Machado de Oliveira Moulin Guilhen,José Henrique Soler Marçal,Tiago de Souza Ferreira,Adésio SilvaFerreira,Marcia Flores da Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models
description	Abstract The genetic variability present in the bean (Phaseolus vulgaris L.) germplasm that is currently used as an agricultural crop has been shown to be stable in production and is acceptable for human sustenance. Accordingly, to maintain as much of the available variability as possible, this study aimed to examine the genetic divergence in the bean using multivariate analysis to identify the sources of genetic variability and enable breeders to recognize genetic combinations that have a greater chances of success before crossings are performed. This study was conducted in a randomized block design with three replications in the agricultural year 2015. The agronomic traits evaluated were the stem diameter (DIAM) in millimeters, plant height (PH) in centimeters, number of seedsper plant (NS), protein percentage (PROT), height of the first pod (HFP) in centimeters, pod number (PN), grain mass per plant (GM) in g plant−1, grain yield (GY) in kg ha−1, and straw yield (SY) in kg ha−1. To enable selection of the most divergent genotypes, twenty different genotypes were analyzed via clustering according to the average linkage criterion (UPGMA) using a matrix of the mean standardized Euclidean distances and principal component analysis based on the values predicted via a multivariate mixed model. The results obtained in this study revealed a high degree of genetic divergence and allowed the progenies to be allocated into different groups, as well as recommended crossings for future bean breeding programs.
author	Carias,Cíntia Machado de Oliveira Moulin Guilhen,José Henrique Soler Marçal,Tiago de Souza Ferreira,Adésio SilvaFerreira,Marcia Flores da
author_facet	Carias,Cíntia Machado de Oliveira Moulin Guilhen,José Henrique Soler Marçal,Tiago de Souza Ferreira,Adésio SilvaFerreira,Marcia Flores da
author_sort	Carias,Cíntia Machado de Oliveira Moulin
title	Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models
title_short	Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models
title_full	Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models
title_fullStr	Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models
title_full_unstemmed	Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models
title_sort	genetic divergence toward the selection of promising bean progenitors via mixed multivariate models
publisher	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202018000300251
work_keys_str_mv	AT cariascintiamachadodeoliveiramoulin geneticdivergencetowardtheselectionofpromisingbeanprogenitorsviamixedmultivariatemodels AT guilhenjosehenriquesoler geneticdivergencetowardtheselectionofpromisingbeanprogenitorsviamixedmultivariatemodels AT marcaltiagodesouza geneticdivergencetowardtheselectionofpromisingbeanprogenitorsviamixedmultivariatemodels AT ferreiraadesio geneticdivergencetowardtheselectionofpromisingbeanprogenitorsviamixedmultivariatemodels AT silvaferreiramarciafloresda geneticdivergencetowardtheselectionofpromisingbeanprogenitorsviamixedmultivariatemodels
_version_	1714202184497758208

Genetic divergence toward the selection of promising bean progenitors via mixed multivariate models

Ejemplares similares