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CROP BREEDING, GENETICS & CYTOLOGY

Variance Component Estimation Using the Additive, Dominance, and Additive x Additive Model When Genotypes Vary across Environments

^a Dep. of Plant and Soil Sciences,
^b Crop Science Research Laboratory, USDA-ARS,
^c Dep. of Mathematics and Statistics, Mississippi State Univ., Mississippi State, MS 39762

^* Corresponding author (jnjenkins{at}ars.usda.gov).

In addition to additive (A) and dominant (D) genetic effects, the A x A interaction (or A x A epistatic) effects that control many quantitative traits are important for genetic and breeding studies. To estimate these genetic variance components, including genotype x environment (G x E) interaction, one usually expects to have data from at least two generations (i.e., F₁ and F₂) and parents with the same entries in all environments. Practical difficulties may arise in implementing such a design. In this study, we performed Monte Carlo simulations to compare the estimated variance components between four partial and two complete genetic designs (GDs) using the mixed linear model approach. Our definition for GD is different from the traditional definitions of genetic mating designs. Simulation results showed that the estimated genetic variance components for A, A x E, A x A epistatic, and A x A x E effects were unbiased for the six designs. Among four partial designs, two provided the comparable results for D and D x E effects compared with the complete GDs, but with slightly larger mean square errors (MSEs), indicating that some partial GDs can be used when the genetic resources are limited.

Abbreviations: _µ², variance component mean • A, additive • D, dominance or dominant • E, environment • G, genotype • GD, genetic design • MINQUE, minimum norm quadratic unbiased estimation • MSE, mean square error