Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping

Area of Science:

• Agricultural Science
• Genetics
• Plant Breeding

Background:

• Expanding phenotyping environments improves crop line selection accuracy in early breeding stages.
• High costs often limit the feasibility of extensive phenotyping.

Purpose of the Study:

• To evaluate a sparse phenotyping method utilizing genomic prediction for predicting missing phenotypes.
• To assess the impact of sparse phenotyping on selection response in wheat and rice.

Main Methods:

• Simulated selection response under a sparse phenotyping strategy.
• Compared sparse phenotyping against complete phenotyping across diverse environmental correlations.
• Utilized genomic prediction to infer phenotypes in un-phenotyped environments.

Main Results:

• Sparse phenotyping significantly increased selection response when environments showed specific correlation patterns (negative or low positive among tested, high positive for extension).
• No improvement in selection response was observed when all environments were positively correlated or highly positively correlated.
• Effectiveness is contingent on the correlation structure between phenotyped and un-phenotyped environments.

Conclusions:

• Genomics-based sparse phenotyping offers a cost-effective strategy to potentially improve selection response in mid-stage crop breeding.
• The success of sparse phenotyping depends critically on the environmental correlation landscape.
• This approach can optimize resource allocation in breeding programs.