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[Analytic methods for seed models with genotype x environment interactions]

J Zhu1

  • 1Agronomy Department Zhejiang Agricultural University, Hangzhou.

Yi Chuan Xue Bao = Acta Genetica Sinica
|January 1, 1996
PubMed
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This study introduces genetic models to analyze crop seed traits, partitioning genetic and genotype-environment interaction effects for additive and dominance components. These models enable precise estimation and prediction of genetic effects for quantitative seed traits.

Area of Science:

  • Plant genetics
  • Quantitative genetics
  • Agricultural science

Context:

  • Analyzing quantitative seed traits in crops is crucial for breeding programs.
  • Existing genetic models may not fully capture complex genetic architectures, including maternal and cytoplasmic effects.
  • Understanding genotype x environment interactions is vital for predicting trait performance across diverse conditions.

Purpose:

  • To propose novel genetic models for analyzing generation means of seed quantitative traits in crops.
  • To partition total genetic effects (G) and genotype x environment interaction effects (GE) into detailed components.
  • To provide a framework for efficient analysis using parents, F1, and F2 generations and reciprocal crosses.

Summary:

  • The study partitions genetic effects (G) into direct (G0), cytoplasmic (C), and maternal (Gm), with G0 and Gm further divided into additive (A, Am) and dominance (D, Dm) components.

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  • Genotype x environment interaction effects (GE) are similarly partitioned into direct (G0E), cytoplasmic (CE), and maternal (GmE) components, with G0E and GmE broken down into additive-by-environment (AE, AmE) and dominance-by-environment (DE, DmE) terms.
  • The MINQUE(0/1) method is proposed for estimating variance and covariance components, and the Adjusted Unbiased Prediction (AUP) approach for predicting random genetic effects, with jackknife for variance estimation.

    • Impact:

    • Offers a comprehensive genetic modeling approach for quantitative seed traits in crops.
    • Facilitates unbiased estimation of genetic parameters and prediction of genetic effects.
    • Enhances the efficiency of crop breeding by providing better tools for trait analysis and selection.