Abstract
In this paper, a multivariate heterogeneous variance components model was developed which allows for determination of location specific variance components in the analysis of multiple related traits. In addition to spatial heterogeneity, genetic similarities are also considered by assigning genetic variance components. The performance of the developed model was evaluated through an extensive simulation study and comparison of models was conducted by heritability estimations. The simulation study reveals that the developed method can control the locational heterogeneity well and the heritability estimations are close to desired proportions for the developed model. A real plant breeding data set was used for illustration.
Highlights
In last decades, there has been a growing interest in multivariate analysis due to the available extensive large-scaled data
In this paper a multivariate heterogeneous variance components model is developed, which allows for determining location specific variance components in the analysis of multiple related traits
Simulation study reveals that the developed method can well control the locational heterogeneity and under the developed model the heritability estimations are close to desired proportions
Summary
There has been a growing interest in multivariate analysis due to the available extensive large-scaled data. Abstract In this paper a multivariate heterogeneous variance components model is developed, which allows for determining location specific variance components in the analysis of multiple related traits. In such study designs, specifying separate genetic variance components for each environment is a way to model the heterogeneity arising from the interaction of genetic and environmental factors.
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