Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.

Advanced variance-covariance structures are commonly used in genetic evaluation of crops to account for micro-site variability and achieve higher accuracy of predictions to increase selection efficiency. Various genetic variance-covariance structures were explored to predict best linear unbiased gen...

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Autor principal:	Zapata-Valenzuela,Jaime
Lenguaje:	English
Publicado:	Instituto de Investigaciones Agropecuarias, INIA 2012
Materias:	Linear mixed model quantitative forest genetics genetic variance
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392012000300002
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spelling	oai:scielo:S0718-583920120003000022018-10-01Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.Zapata-Valenzuela,Jaime Linear mixed model quantitative forest genetics genetic variance Advanced variance-covariance structures are commonly used in genetic evaluation of crops to account for micro-site variability and achieve higher accuracy of predictions to increase selection efficiency. Various genetic variance-covariance structures were explored to predict best linear unbiased genetic merits of 453 loblolly pine (Pinus taeda L.) cloned progeny tested at 16 different locations in the southern U.S. Statistical models were compared using model fit statistics, variance components and genetic parameters. Among the models explored, spatial autoregressive error correlation with independent residual term for the R side with a factor analytic structure for the G side of the mixed model was superior. The model produced one of the smallest fit statistics (LogL equal to -2694), a small error variance (12.72), and the highest broad-sense heritability (0.45), compared with the default homogeneous error and genetic variance-covariance structure (statistical significance at P < 0.05). We concluded that the combination of specific structure for error and genetic design was effective to remove spatial-related variance, and to increase the accuracy of predictions of clonal genetic values, which could be used as analytical tool for increasing the selection efficiencies in forest genetic trials.info:eu-repo/semantics/openAccessInstituto de Investigaciones Agropecuarias, INIAChilean journal of agricultural research v.72 n.3 20122012-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392012000300002en10.4067/S0718-58392012000300002
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Linear mixed model quantitative forest genetics genetic variance
spellingShingle	Linear mixed model quantitative forest genetics genetic variance Zapata-Valenzuela,Jaime Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.
description	Advanced variance-covariance structures are commonly used in genetic evaluation of crops to account for micro-site variability and achieve higher accuracy of predictions to increase selection efficiency. Various genetic variance-covariance structures were explored to predict best linear unbiased genetic merits of 453 loblolly pine (Pinus taeda L.) cloned progeny tested at 16 different locations in the southern U.S. Statistical models were compared using model fit statistics, variance components and genetic parameters. Among the models explored, spatial autoregressive error correlation with independent residual term for the R side with a factor analytic structure for the G side of the mixed model was superior. The model produced one of the smallest fit statistics (LogL equal to -2694), a small error variance (12.72), and the highest broad-sense heritability (0.45), compared with the default homogeneous error and genetic variance-covariance structure (statistical significance at P < 0.05). We concluded that the combination of specific structure for error and genetic design was effective to remove spatial-related variance, and to increase the accuracy of predictions of clonal genetic values, which could be used as analytical tool for increasing the selection efficiencies in forest genetic trials.
author	Zapata-Valenzuela,Jaime
author_facet	Zapata-Valenzuela,Jaime
author_sort	Zapata-Valenzuela,Jaime
title	Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.
title_short	Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.
title_full	Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.
title_fullStr	Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.
title_full_unstemmed	Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.
title_sort	use of analytic factor structure to increase heritability of clonal progeny tests of pinus taeda l.
publisher	Instituto de Investigaciones Agropecuarias, INIA
publishDate	2012
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392012000300002
work_keys_str_mv	AT zapatavalenzuelajaime useofanalyticfactorstructuretoincreaseheritabilityofclonalprogenytestsofpinustaedal
_version_	1714205312509018112

Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.

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