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Updated: Jun 24, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Estimation in a multiplicative mixed model involving a genetic relationship matrix.
Alison M Kelly1, Brian R Cullis, Arthur R Gilmour
1Queensland DPI&F, Biometry, Toowoomba, Queensland, Australia. alison.kelly@dpi.qld.gov.au
Genetics, Selection, Evolution : GSE
|April 10, 2009
Summary
This study enhances genetic models for plant breeding by incorporating non-additive genetic effects in multi-environment trials (METs). It addresses challenges with complex genotype-by-environment interactions and pedigree data for improved breeding strategies.
Area of Science:
- Quantitative genetics
- Plant breeding
- Statistical genomics
Background:
- Advanced genetic models are needed for multi-environment trials (METs) in plant breeding.
- Existing models struggle with complex genotype-by-environment (GxE) interactions and pedigree data.
Purpose of the Study:
- To extend existing genetic models for MET data to include non-additive genetic effects.
- To develop robust estimation methods for complex variance structures in plant breeding.
Main Methods:
- Utilized factor analytic (FA) structures for GxE effects.
- Applied sparse matrix methodology and average information algorithms.
- Extended estimation methods to incorporate numerator relationship matrices.
Main Results:
- Successfully adapted reduced rank model estimation for MET data with pedigree information.
- Investigated the impact of non-additive variance on genetic model performance.
- Demonstrated the feasibility of fitting complex models using advanced algorithms.
Conclusions:
- The proposed extension enables more accurate genetic analysis of MET data.
- Improved modeling of GxE interactions is crucial for effective plant breeding.
- This work provides a framework for advanced genetic evaluations in plant breeding programs.
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