Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association...

Area of Science:

• Plant genetics and breeding
• Quantitative genetics
• Bioinformatics

Background:

• Agronomic trait evaluation often involves numerous, genetically correlated traits.
• Understanding trait interrelationships is crucial for effective plant breeding.
• Conventional multi-trait genome-wide association studies (MTM-GWAS) do not fully capture phenotypic network structures.

Purpose of the Study:

• To extend MTM-GWAS by incorporating trait network structures using structural equation models (SEM-GWAS).
• To illustrate the utility of SEM-GWAS in dissecting genetic effects on interrelated traits in rice.
• To provide a framework for understanding complex trait interrelationships in plant breeding.

Main Methods:

• Developed and applied structural equation model-based genome-wide association studies (SEM-GWAS).
• Incorporated trait network structures into GWAS analysis.
• Utilized digital metrics for shoot biomass, root biomass, water use, and water use efficiency in rice.

Main Results:

• SEM-GWAS can partition single nucleotide polymorphism (SNP) effects into direct and indirect components.
• For water use, SNP effects were primarily direct.
• For water use efficiency, SNP effects were largely indirect, influenced by projected shoot area.

Conclusions:

• SEM-GWAS offers a robust framework for analyzing multivariate phenotypes and trait interrelationships.
• This approach provides novel insights into quantitative trait loci (QTL) action within phenotypic networks.
• SEM-GWAS can enhance the understanding of complex agronomic trait relationships for improved plant breeding.