Abstract
Key message
Using genomic structural equation modelling, this research demonstrates an efficient way to identify genetically correlating traits and provides an effective proxy for multi-trait selection to consider the joint genetic architecture of multiple interacting traits in crop breeding.
Abstract
Breeding crop cultivars with optimal value across multiple traits has been a challenge, as traits may negatively correlate due to pleiotropy or genetic linkage. For example, grain yield and grain protein content correlate negatively with each other in cereal crops. Future crop breeding needs to be based on practical yet accurate evaluation and effective selection of beneficial trait to retain genes with the best agronomic score for multiple traits. Here, we test the framework of whole-system-based approach using structural equation modelling (SEM) to investigate how one trait affects others to guide the optimal selection of a combination of agronomically important traits. Using ten traits and genome-wide SNP profiles from a worldwide barley panel and SEM analysis, we revealed a network of interacting traits, in which tiller number contributes positively to both grain yield and protein content; we further identified common genetic factors affecting multiple traits in the network of interaction. Our method demonstrates an efficient way to identify genetically correlating traits and underlying pleiotropic genetic factors and provides an effective proxy for multi-trait selection within a whole-system framework that considers the joint genetic architecture of multiple interacting traits in crop breeding. Our findings suggest the promise of a whole-system approach to overcome challenges such as the negative correlation of grain yield and protein content to facilitating quantitative and objective breeding decisions in future crop breeding.
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Acknowledgements
We thank Ms Lee-Anne McFawn, Ms Jenifer Bussanich and Mr David Farleigh from DPIRD (South Perth, WA) for providing technical assistance in the field trials. We thank Dr Andrew Grotzinger (University of Texas at Austin) and Dr Michel Nivard (Vrije Universiteit Amsterdam) for tailoring the scripts of the R package “GenomicSEM” to the barley genome, and for their advice in running the package. The authors declare no conflict of interest.
Funding
This work was supported by funding from the Grains Research and Development Corporation (GRDC) of Australia (DAW00240/UMU00050, UMU00049 and DAW00233), Department of Primary Industries and Regional Development (DPIRD), and Western Australian State Agricultural Biotechnology Centre (SABC).
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CL and TA conceived the project, collected the barley accessions, conducted initial phenological evaluation, and selected the accessions for this study. TH developed the models, analysed the data, and wrote the paper with input from CL. TA, XZ, HL, YW, SK, GZ, SW and CL conducted the field experiments and phenotyping. KC conducted the analysis of the agronomic data. CH, XZ, PW, GZ, and CT prepared the low-coverage WGS and DArTseq data. CL supervised the project.
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He, T., Angessa, T.T., Hill, C.B. et al. Genomic structural equation modelling provides a whole-system approach for the future crop breeding. Theor Appl Genet 134, 2875–2889 (2021). https://doi.org/10.1007/s00122-021-03865-4
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DOI: https://doi.org/10.1007/s00122-021-03865-4