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Estimating Type B Genetic Correlations with Unbalanced Data and Heterogeneous Variances for Half-Sib Experiments

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A statistical approach is proposed for estimating type B genetic correlations with unbalanced data and heterogeneous variances across environments. First, parental GCA (one-half of the parental breeding value) effects are predicted separately for each environment using best linear unbiased prediction (BLUP). Second, predicted parental GCAs are weighted by their prediction accuracies and Pearson correlation between weighted GCAs in two environments is obtained. Third, type B genetic correlation is calculated by dividing the Pearson correlation with the mean products of prediction accuracies. Advantages of this approach include: (1) fixed effects are removed using best linear unbiased estimation (BLUE) so that they less seriously confound genetic effects when data are unbalanced; (2) heterogeneous variances associated with genetic group means are adjusted for during the process of parental GCA prediction; and (3) it is applicable when experimental designs are different in two environments. Numeric comparisons of estimation methods using simulated data of known true genetic parameters indicated that the new approach produces less bias and higher precision when data are highly unbalanced or have high level heterogeneity of variances across environments. For slightly or moderately unbalanced data, the method of Yamada (1962), however, is simpler and yields satisfactory estimates using standardized data. For. Sci. 45(4):562-572.

Keywords: BLUP; Breeding values; genotype x environment interaction; indirect selection

Document Type: Journal Article

Affiliations: Associate in Forest Resources and Conservation, School of Forest Resources and Conservation, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611--Phone: (352) 846-0898;, Fax: (352) 846-1277

Publication date: 1999-11-01

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
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