Application of crop wild relatives in modern breeding: An overview of resources, experimental and computational methodologies.

Autor:	Tirnaz S; School of Biological Sciences, University of Western Australia, Perth, WA, Australia., Zandberg J; School of Biological Sciences, University of Western Australia, Perth, WA, Australia., Thomas WJW; School of Biological Sciences, University of Western Australia, Perth, WA, Australia., Marsh J; School of Biological Sciences, University of Western Australia, Perth, WA, Australia., Edwards D; School of Biological Sciences, University of Western Australia, Perth, WA, Australia., Batley J; School of Biological Sciences, University of Western Australia, Perth, WA, Australia.
Jazyk:	angličtina
Zdroj:	Frontiers in plant science [Front Plant Sci] 2022 Nov 17; Vol. 13, pp. 1008904. Date of Electronic Publication: 2022 Nov 17 (Print Publication: 2022).
DOI:	10.3389/fpls.2022.1008904
Abstrakt:	Global agricultural industries are under pressure to meet the future food demand; however, the existing crop genetic diversity might not be sufficient to meet this expectation. Advances in genome sequencing technologies and availability of reference genomes for over 300 plant species reveals the hidden genetic diversity in crop wild relatives (CWRs), which could have significant impacts in crop improvement. There are many ex-situ and in-situ resources around the world holding rare and valuable wild species, of which many carry agronomically important traits and it is crucial for users to be aware of their availability. Here we aim to explore the available ex-/in- situ resources such as genebanks, botanical gardens, national parks, conservation hotspots and inventories holding CWR accessions. In addition we highlight the advances in availability and use of CWR genomic resources, such as their contribution in pangenome construction and introducing novel genes into crops. We also discuss the potential and challenges of modern breeding experimental approaches (e.g. de novo domestication, genome editing and speed breeding) used in CWRs and the use of computational (e.g. machine learning) approaches that could speed up utilization of CWR species in breeding programs towards crop adaptability and yield improvement. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Tirnaz, Zandberg, Thomas, Marsh, Edwards and Batley.)
Databáze:	MEDLINE
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