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Chilean Journal of Agricultural Research
Instituto de Investigaciones Agropecuarias, INIA
ISSN: 0718-5820
EISSN: 0718-5820
Vol. 72, No. 3, 2012, pp. 309-315
Bioline Code: cj12049
Full paper language: English
Document type: Research Article
Document available free of charge

Chilean Journal of Agricultural Research, Vol. 72, No. 3, 2012, pp. 309-315

 en USE OF ANALYTIC FACTOR STRUCTURE TO INCREASE HERITABILITY OF CLONAL PROGENY TESTS OF Pinus taeda check for this species in other resources L.
Zapata-Valenzuela, Jaime

Abstract

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 check for this species in other resources 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.

Keywords
Linear mixed model, quantitative forest genetics, genetic variance

 
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