Programmable Domestication: CRISPR , Pan‐Genomics and System Level Engineering for Next‐Generation Crops
Global agriculture is increasingly challenged by climate instability, genetic erosion, emerging pathogens and rising food demands, exposing the limitations of conventional breeding and traditional domestication strategies. Recent advances in CRISPR-based genome editing, pangenomic, synthetic biology, artificial intelligence (AI)-assisted breeding and predictive phenomics are transforming de novo domestication from a slow evolutionary process into a programmable framework for rational crop redesign. This review synthesises recent advances in programmable de novo domestication and highlights how crop wild relatives and underutilised germplasm can be harnessed to develop resilient, climate-adaptive and sustainable crop systems. The integration of multiplex genome editing, pan-genomic variation discovery, AI-driven genomic prediction and predictive breeding enables precise engineering of key domestication traits governing plant architecture, yield potential, stress resilience and nutritional quality. Furthermore, we propose a trajectory-based framework for programmable domestication comprising Adaptive Rescue, Agronomic Refinement and Novel Chassis Engineering, which illustrates distinct evolutionary pathways, engineering complexity and crop redesign objectives. We also examine the major system level challenges that constrain programmable domestication, including cryptic genetic variation, epistasis, gene regulatory network complexity, genotype phenotype predictability, biodiversity conservation and regulatory considerations. Collectively, programmable domestication represents a transformative shift from conventional crop improvement towards system-level engineering of next-generation crops, providing a strategic foundation for enhancing global food security, agricultural sustainability and environmental resilience in the face of accelerating climate change.
Authors
- Sezai Erċışlı (ORCID: https://orcid.org/0000-0001-5006-5687)
- Aqsa Ijaz
- Ayesha Naveed
- Abdul Razzaq (ORCID: https://orcid.org/0000-0002-0106-0481)
- Fei Qiao (ORCID: https://orcid.org/0000-0002-5515-1792)
- Sadam Munawar
- Zunaira Anwar
- Hina Firdous
- Muhammad Mubashar Zafar (ORCID: https://orcid.org/0000-0002-5664-138X)
- Xuefei Jiang (ORCID: https://orcid.org/0000-0002-0607-3847)
- Ayesha Siddiqua
Institutions
- Pakistan Institute of Engineering and Applied Sciences (PK)
- University of Lahore (PK)
- Nuclear Institute for Agriculture and Biology (PK)
- Agricultural Genomics Institute at Shenzhen (CN)
- Tropical Crops Genetic Resources Institute (CN)
- Chinese Academy of Agricultural Sciences (CN)
- University of Faisalabad (PK)
- Sanya University (CN)
- University of Agriculture Faisalabad (PK)
- Atatürk University (TR)
- Hainan Tropical Ocean University (CN)
- Northwest A&F University (CN)
Publication Details
- Journal
- Plant Biotechnology Journal
- Published
- 2026-08-26
- DOI
- https://doi.org/10.1111/pbi.70749
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Academy of Tropical Agricultural Sciences
- National Key Research and Development Program of China