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This study identifies optimal prediction models using multi-omic data to enhance hybrid rice selection by evaluating various combinations of genomic, metabolomic, and transcriptomic datasets. It finds that combining genomic and metabolomic data generally yields better predictions for key agronomic traits compared to single-omics or other combinations, with Best Linear Unbiased Prediction (BLUP) being the most effective method. The findings provide guidelines for improving trait predictability in rice breedi

Author

pottalaspandana1970

Language

EN