Genomic Prediction and Selection for Fruit Traits in Winter Squash

Area of Science:

• Plant breeding
• Genomics
• Horticultural science

Background:

• Improving fruit quality in winter squash is a significant breeding challenge.
• Squash breeding is resource-intensive, limiting population size and genetic gain.
• Genomic selection (GS) offers potential for enhanced breeding efficiency in horticultural crops.

Purpose of the Study:

• To evaluate the predictive ability of whole-genome models for fruit quality traits in butternut squash.
• To assess the effectiveness of genomic recurrent selection for improving fruit quality.
• To demonstrate realized genetic gain from a multi-trait genomic selection model in a resource-limited crop.

Main Methods:

• Developed and applied whole-genome models to predict fruit quality traits.
• Conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population.
• Utilized a multi-trait genomic selection model to derive a fruit quality index for selection.
• Assessed realized genetic gain using remnant seed from selected populations.

Main Results:

• Whole-genome models showed low to moderate, yet sufficient, predictive abilities for fruit quality traits.
• Genomic recurrent selection led to significant improvements in the fruit quality index.
• Correlated traits also exhibited significant changes following selection.
• Empirical evidence of gain from a multi-trait genomic selection model was established.

Conclusions:

• Genomic selection is a viable strategy for enhancing fruit quality in resource-limited horticultural crops like butternut squash.
• Multi-trait genomic selection models can effectively drive genetic gain for complex traits.
• This research provides a foundation for implementing GS in similar breeding programs.