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Genomic structural equation modelling provides a whole-system approach for the future crop breeding
Tianhua He1, Tefera Tolera Angessa1, Camilla Beate Hill1
1Western Crop Genetics Alliance, Agricultural Sciences, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia.
Summary
This study introduces a genomic structural equation modelling approach for crop breeding. It efficiently identifies genetically linked traits, enabling better selection for multiple traits like yield and protein content.
Area of Science:
- Genetics
- Plant Breeding
- Bioinformatics
Background:
- Breeding crop cultivars for multiple optimal traits is challenging due to negative correlations (e.g., grain yield and protein content) caused by pleiotropy or genetic linkage.
- Effective selection requires practical evaluation considering the joint genetic architecture of interacting traits.
Purpose of the Study:
- To develop and test a whole-system framework using structural equation modelling (SEM) for investigating trait interactions and guiding multi-trait selection in crop breeding.
- To identify genetically correlating traits and common genetic factors underlying trait networks.
Main Methods:
- Applied genomic structural equation modelling (SEM) to analyze ten traits and genome-wide SNP data from a global barley panel.
- Investigated trait interdependencies and identified pleiotropic genetic factors within a whole-system framework.
Main Results:
- Revealed a network of interacting traits in barley, where tiller number positively influences both grain yield and protein content.
- Identified common genetic factors responsible for multiple traits within the observed interaction network.
Conclusions:
- Genomic SEM provides an efficient method for identifying genetically correlated traits and underlying pleiotropic factors.
- This whole-system approach offers an effective proxy for multi-trait selection, overcoming challenges like negative trait correlations in crop breeding.
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