Dataset Information

Capturing Wheat Phenotypes at the Genome Level.

ABSTRACT: 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 Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for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.

SUBMITTER: Hussain B

PROVIDER: S-EPMC9289626 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Capturing Wheat Phenotypes at the Genome Level.

Hussain Babar B Akpınar Bala A BA Alaux Michael M Algharib Ahmed M AM Sehgal Deepmala D Ali Zulfiqar Z Aradottir Gudbjorg I GI Batley Jacqueline J Bellec Arnaud A Bentley Alison R AR Cagirici Halise B HB Cattivelli Luigi L Choulet Fred F Cockram James J Desiderio Francesca F Devaux Pierre P Dogramaci Munevver M Dorado Gabriel G Dreisigacker Susanne S Edwards David D El-Hassouni Khaoula K Eversole Kellye K Fahima Tzion T Figueroa Melania M Gálvez Sergio S Gill Kulvinder S KS Govta Liubov L Gul Alvina A Hensel Goetz G Hernandez Pilar P Crespo-Herrera Leonardo Abdiel LA Ibrahim Amir A Kilian Benjamin B Korzun Viktor V Krugman Tamar T Li Yinghui Y Liu Shuyu S Mahmoud Amer F AF Morgounov Alexey A Muslu Tugdem T Naseer Faiza F Ordon Frank F Paux Etienne E Perovic Dragan D Reddy Gadi V P GVP Reif Jochen Christoph JC Reynolds Matthew M Roychowdhury Rajib R Rudd Jackie J Sen Taner Z TZ Sukumaran Sivakumar S Ozdemir Bahar Sogutmaz BS Tiwari Vijay Kumar VK Ullah Naimat N Unver Turgay T Yazar Selami S Appels Rudi R Budak Hikmet H

Frontiers in plant science 20220704

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 (<i>Triticum aestivum</i> 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 ...[more]

PMID: 35860541

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Project description:Senescence is a highly quantitative trait, but in wheat the genetics underpinning senescence regulation remain relatively unknown. To select senescence variation and ultimately identify novel genetic regulators, accurate characterization of senescence phenotypes is essential. When investigating senescence, phenotyping efforts often focus on, or are limited to, the visual assessment of flag leaves. However, senescence is a whole-plant process, involving remobilization and translocation of resources into the developing grain. Furthermore, the temporal progression of senescence poses challenges regarding trait quantification and description, whereupon the different models and approaches applied result in varying definitions of apparently similar metrics. To gain a holistic understanding of senescence, we phenotyped flag leaf and peduncle senescence progression, alongside grain maturation. Reviewing the literature, we identified techniques commonly applied in quantification of senescence variation and developed simple methods to calculate descriptive and discriminatory metrics. To capture senescence dynamism, we developed the idea of calculating thermal time to different flag leaf senescence scores, for which between-year Spearman's rank correlations of r ≥ 0.59, P < 4.7 × 10-5 (TT70), identify as an accurate phenotyping method. Following our experience of senescence trait genetic mapping, we recognized the need for singular metrics capable of discriminating senescence variation, identifying thermal time to flag leaf senescence score of 70 (TT70) and mean peduncle senescence (MeanPed) scores as most informative. Moreover, grain maturity assessments confirmed a previous association between our staygreen traits and grain fill extension, illustrating trait functionality. Here we review different senescence phenotyping approaches and share our experiences of phenotyping two independent recombinant inbred line (RIL) populations segregating for staygreen traits. Together, we direct readers toward senescence phenotyping methods we found most effective, encouraging their use when investigating and discriminating senescence variation of differing genetic bases, and aid trait selection and weighting in breeding and research programs alike.

Project description:BackgroundGamma-irradiated mutants of Triticum aestivum L., hexaploid wheat, provide novel and agriculturally important traits and are used as breeding materials. However, the identification of causative genomic regions of mutant phenotypes is challenging because of the large and complicated genome of hexaploid wheat. Recently, the combined use of high-quality reference genome sequences of common wheat and cost-effective resequencing technologies has made it possible to evaluate genome-wide polymorphisms, even in complex genomes.ResultsTo investigate whether the genome sequencing approach can effectively detect structural variations, such as deletions, frequently caused by gamma irradiation, we selected a grain-hardness mutant from the gamma-irradiated population of Japanese elite wheat cultivar "Kitahonami." The Hardness (Ha) locus, including the puroindoline protein-encoding genes Pina-D1 and Pinb-D1 on the short arm of chromosome 5D, primarily regulates the grain hardness variation in common wheat. We performed short-read genome sequencing of wild-type and grain-hardness mutant plants, and subsequently aligned their short reads to the reference genome of the wheat cultivar "Chinese Spring." Genome-wide comparisons of depth-of-coverage between wild-type and mutant strains detected ~ 130 Mbp deletion on the short arm of chromosome 5D in the mutant genome. Molecular markers for this deletion were applied to the progeny populations generated by a cross between the wild-type and the mutant. A large deletion in the region including the Ha locus was associated with the mutant phenotype, indicating that the genome sequencing is a powerful and efficient approach for detecting a deletion marker of a gamma-irradiated mutant phenotype. In addition, we investigated a pre-harvest sprouting tolerance mutant and identified a 67.8 Mbp deletion on chromosome 3B where Viviparous-B1 and GRAS family transcription factors are located. Co-dominant markers designed to detect the deletion-polymorphism confirmed the association with low germination rate, leading to pre-harvest sprouting tolerance.ConclusionsShort read-based genome sequencing of gamma-irradiated mutants facilitates the identification of large deletions linked to mutant phenotypes when combined with segregation analyses in progeny populations. This method allows effective application of mutants with agriculturally important traits in breeding using marker-assisted selection.

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Dataset Information

Capturing Wheat Phenotypes at the Genome Level.

Publications

Capturing Wheat Phenotypes at the Genome Level.

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