Chapter 11. Genomic insights on global journeys of adaptive wheat genes that brought us to modern wheat
Since its first cultivation, hexaploid wheat has evolved, allowing for its widespread cultivation and contributing to global food security. The identification of adaptive genes, such as vernalization and photoperiod response genes, has played a crucial role in optimizing wheat production, being instrumental in fine-tuning flowering and reproductive cycles in response to changing climates and evolving agricultural practices. While these adaptive genes have expanded the range of variation suitable for adaptation, further research is needed to understand their mechanisms, dissect the pathways involved, and expedite their implementation in breeding programs. By analyzing data across different environments and over time, Meta-QTL analysis can help identify novel genomic regions and facilitate the discovery of new candidate genes. This chapter reports on two previously unknown Meta-QTL regions, highlighting the potential for further exploration in this field. Moving forward, it will be increasingly important to expand our understanding of how genetic regions influence not only flowering time but also other developmental traits and their responses to environmental factors. Advances in gene-based modeling hold promise for describing growth and development processes using QTL and other genomic loci analysis. Integrating these findings into process-based crop models can provide valuable insights for future research. Overall, the study of adaptive genes and their impact on wheat production represents a vital area of research that continues to contribute to global food security.
| Main Authors: | , , , , , , , |
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| Format: | Book Chapter biblioteca |
| Language: | English |
| Published: |
Springer Cham
2024
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Hexaploid Wheat, Adaptive Genes, Novel Genomic Regions, Gene-Based Modeling, Process-Based Modeling, Global Food Security, HEXAPLOIDY, WHEAT, QUANTITATIVE TRAIT LOCI, MODELLING, FOOD SECURITY, Wheat, |
| Online Access: | https://hdl.handle.net/10883/22870 |
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