Predicting hybrid performance in rice using genomic best linear unbiased prediction

Area of Science:

• Agricultural Science
• Genetics
• Plant Breeding

Background:

• Genomic selection (GS) enhances marker-assisted selection (MAS) for quantitative traits by utilizing whole-genome markers.
• GS offers greater efficiency than traditional MAS for polygenic traits and enables cost-saving through early selection in purebred improvement.
• Hybrid breeding is crucial for crop productivity, and GS is expected to further boost its efficiency.

Purpose of the Study:

• To propose and apply an advanced method for predicting hybrid performance using genomic data.
• To introduce genomic hybrid breeding, leveraging genomic best linear unbiased prediction (GBLUP) for hybrid trait value prediction.
• To demonstrate the potential of marker-guided prediction to revolutionize hybrid breeding strategies.

Main Methods:

• Utilized genomic best linear unbiased prediction (GBLUP) for predicting hybrid performance.
• Applied genomic selection principles to hybrid breeding, termed genomic hybrid breeding.
• Employed a training sample of 278 rice hybrids from 210 recombinant inbred lines to predict 21,945 potential hybrids.

Main Results:

• The genomic hybrid breeding strategy successfully predicted trait values for all potential hybrids.
• The top 100 selected hybrids showed a 16% average yield increase compared to the overall average yield of all potential hybrids.
• The marker-guided prediction of hybrid yields serves as a proof of concept for a revolutionary breeding technology.

Conclusions:

• Genomic selection significantly enhances hybrid breeding efficiency by predicting performance before phenotypic measurement.
• The proposed GBLUP-based genomic hybrid breeding method offers a powerful tool for accelerating crop improvement.
• This marker-guided approach has the potential to revolutionize hybrid breeding and substantially increase crop yields.