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Breeding by Design for Functional Rice with Genome Editing Technologies
Published on: January 3, 2025
Enhancing alfalfa breeding through genomic prediction with exotic germplasm resources
Shufen Chen1,2, Meng Lin1,2, Neal W Tilhou3
1Breeding Insight, Cornell University, 525 Tower Rd, Ithaca, NY, 14853, USA.
BMC Plant Biology
|July 17, 2026
Summary
Genomic prediction in alfalfa is improved by using diverse germplasm in training sets. Optimizing training set composition and size enhances predictive ability for traits like growth habit and plant height.
Area of Science:
- Plant breeding
- Genomics
- Quantitative genetics
Background:
- Alfalfa molecular breeding and genomic selection lag behind major crops.
- Leveraging exotic germplasm is crucial for discovering and introgessing native traits into new cultivars.
Purpose of the Study:
- Investigate the impact of germplasm composition and proportion on genomic prediction ability in alfalfa.
- Optimize genomic prediction schemes for alfalfa breeding.
Main Methods:
- Utilized 780 alfalfa samples from diverse geographic origins and U.S. breeding materials.
- Genotyped samples using the alfalfa 3K DArTag panel.
- Evaluated traits including growth habit, plant height, and plant vigor in multi-year field trials.
Main Results:
- Genomic prediction across the entire population surpassed predictions within individual subgroups.
- Systematically varying training set composition (10-90% from subgroups) further optimized prediction.
- Increased genetic relatedness between training and test sets significantly improved predictive performance.
Conclusions:
- Expanding training set sizes is essential for successful genomic prediction in alfalfa.
- Strategic incorporation of diverse germplasm and evaluation of new base populations maximize predictive ability.
