Accelerating perennial crop improvement via multi-omics-based predictive breeding
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View abstract on PubMed
Summary
This summary is machine-generated.Perennial crop breeding is slow, but multi-omics data can accelerate genetic improvement. Integrating diverse omics data offers a promising strategy for enhancing crop resilience and productivity in the face of environmental challenges.
Area Of Science
- Agricultural Science
- Plant Breeding
- Genomics
Background
- Perennial crops face environmental challenges threatening productivity.
- Slow breeding cycles in perennials hinder adaptation to climate change.
- Annual crops utilize predictive breeding with multi-omics for accelerated genetic gains.
Purpose Of The Study
- Assess current predictive breeding in perennials.
- Compare with multi-omics frameworks in annual crops.
- Outline considerations for implementing multi-omics in perennial breeding.
Main Methods
- Review of single-omic and emerging omics resources in perennials.
- Comparison with established multi-omics prediction frameworks in annual crops.
- Analysis of challenges and strategies for multi-omics integration.
Main Results
- Multi-omics approaches capture system features of regulatory networks.
- Established multi-omics frameworks in annual crops enhance predictive ability.
- Key considerations for perennials include end-to-end systems and modeling complex interactions.
Conclusions
- Multi-omics predictive breeding shows promise for rapid genetic improvement in perennials.
- Addressing challenges like data dimensionality and GxE interactions is crucial.
- Cross-institutional collaboration and simulation tools can optimize multi-omics integration.
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