Capturing wheat phenotypes at the genome level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Bibliographic Details

Main Authors:	Hussain, Babar, Akpınar, Bala A., Alaux, Michael, Algharib, Ahmed M., Sehgal, Deepmala, Ali, Zulfiqar, Aradottir, Gudbjorg I., Batley, Jacqueline, Bellec, Arnaud, Bentley, Alison R., Cagirici, Halise B., Cattivelli, Luigi, Choulet, Fred, Cockram, James, Desiderio, Francesca, Devaux, Pierre, Dogramaci, Munevver, Dorado, Gabriel, Dreisigacker, Susanne, Edwards, David, El Hassouni, Khaoula, Eversole, Khaoula, Fahima, Tzion, Figueroa, Melania, Gálvez, Sergio, Gill, Kulvinder S., Govta, Liubov, Gul, Alvina, Hensel, Goetz, Hernández, Pilar, Crespo-Herrera, Leonardo A., Ibrahim, Amir M.H., Kilian, Benjamin, Korzun, Viktor, Krugman, Tamar, Yinghui Li, Shuyu Liu, Mahmoud, Amer F., Morgounov, Alexey I., Muslu, Tugdem, Naseer, Faiza, Ordon, Frank, Paux, Etienne, Perovic, Dragan, Reddy, Gadi V. P., Reif, Jochen Christoph, Reynolds, Matthew P., Roychowdhury, Rajib, Rudd, Jackie C., Sen, Taner Z., Sukumaran, Sivakumar, Bahar Sogutmaz Ozdemir, Tiwari, Vijay Kumar, Ullah, Naimat, Unver, Turgay, Yazar, Selami, Appels, Rudi, Budak, Hikmet
Format:	Journal Article biblioteca
Language:	English
Published:	Frontiers Media 2022
Subjects:	abiotic stress, crispr, disease resistance, quantitative trait loci, wheat,
Online Access:	https://hdl.handle.net/10568/127283 https://hdl.handle.net/10883/22144 https://doi.org/10.3389/fpls.2022.851079
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