Marker-assisted recurrent selection improves the crown rot resistance of bread wheat

Rahman, M. M.; Davies, Philip; Bansal, Urmil; Pasam, Raj K.; Hayden, Matthew; Trethowan, Richard

doi:10.1007/s11032-020-1105-1

Cited by 27 publications

(23 citation statements)

References 37 publications

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“…The GWAS analyses identified 17 different QTL, a large proportion of which have been reported previously (Liu and Ogbonnaya 2015 ), and many most probably the same as the 23 QTL identified by Rahman et al ( 2020 ) but with some notable differences. For example, a clear indication was observed on the long arm of chromosome 3B, a region consistently reported to harbour a QTL for FCR resistance (Rahman et al 2020 ; Ma et al 2010 ; Li et al 2010 ; Poole et al 2012 ) but absent from the study of seedling resistance by Jin et al ( 2020 ) in Chinese germplasm. This QTL has been detected in multiple genetic backgrounds and with different sources of favourable alleles including a T. spelta source called CSCR6, a relatively old breeding line (IRN497), a synthetic hexaploid (CPI133814), and a number of varieties (Ernie, Macon, Otis, and Sunco) (Liu and Ogbonnaya 2015 ), many of which are represented in the pedigree of the FCR donors used in the study reported here.…”

Section: Discussionsupporting

confidence: 63%

“…Firstly, the current study utilized a population of almost 1000 individuals, 5–10 times larger than most previous studies, and more than double the size of recently published GWAS by Jin et al ( 2020 ) and Rahman et al ( 2020 ). Additionally, many previous studies for QTL mapping of FCR resistance have relied upon biparental populations (double haploids and recombinant inbred lines), whereas the population used here involved the crossings of several susceptible and resistant founders, akin to a multi-parental population such as MAGIC (Cavanagh et al 2008 ).…”

Section: Discussionmentioning

confidence: 99%

“…phenotypic and genotypic data can be used to identify resistant lines with complementary QTL, that could then be combined efficiently through marker-assisted recurrent selection (e.g. Rahman et al 2020 )).…”

Section: Discussionmentioning

confidence: 99%

“…Jin et al ( 2020 ) using a set of 358 Chinese varieties or breeding lines that have been selected for seedling resistance to FCR, reported significant associations across eight chromosomes of bread wheat, with potentially novel QTL on 5DL for seedling resistance to FCR. Finally Rahman et al ( 2020 ) used GWAS on 475 lines to detect 23 marker trait associations and then successfully used these associations via marker assisted recurrent selection (MARS) to improve FCR in two bread wheat populations, with lines derived after two cycles of MARS showing improved levels of resistance compared to the base population.…”

Section: Introductionmentioning

confidence: 99%

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Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot

et al. 2020

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Much has been published on QTL detection for complex traits using bi-parental and multi-parental crosses (linkage analysis) or diversity panels (GWAS studies). While successful for detection, transferability of results to real applications has proven more difficult. Here, we combined a QTL detection approach using a pre-breeding populations which utilized intensive phenotypic selection for the target trait across multiple plant generations, combined with rapid generation turnover (i.e. “speed breeding”) to allow cycling of multiple plant generations each year. The reasoning is that QTL mapping information would complement the selection process by identifying the genome regions under selection within the relevant germplasm. Questions to answer were the location of the genomic regions determining response to selection and the origin of the favourable alleles within the pedigree. We used data from a pre-breeding program that aimed at pyramiding different resistance sources to Fusarium crown rot into elite (but susceptible) wheat backgrounds. The population resulted from a complex backcrossing scheme involving multiple resistance donors and multiple elite backgrounds, akin to a MAGIC population (985 genotypes in total, with founders, and two major offspring layers within the pedigree). A significant increase in the resistance level was observed (i.e. a positive response to selection) after the selection process, and 17 regions significantly associated with that response were identified using a GWAS approach. Those regions included known QTL as well as potentially novel regions contributing resistance to Fusarium crown rot. In addition, we were able to trace back the sources of the favourable alleles for each QTL. We demonstrate that QTL detection using breeding populations under selection for the target trait can identify QTL controlling the target trait and that the frequency of the favourable alleles was increased as a response to selection, thereby validating the QTL detected. This is a valuable opportunistic approach that can provide QTL information that is more easily transferred to breeding applications.

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Section: Discussionsupporting

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot

et al. 2020

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“…QTL mapping and GWAS result in the detection of QTLs that can be used either directly for marker-assisted selection (MAS) or marker-assisted backcrossing (MABC), when the effects are high enough and reproducible in unrelated populations or for marker-assisted recurrent selection (MARS) when the effects are only small. The latter has been shown to be successful for resistance to Fusarium crown rot caused by Fusarium pseudograminearum that has a quantitative, complex inheritance [ 21 ].…”

Section: Basic Techniques For Genomics-assisted Breedingmentioning

confidence: 99%

Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize

Miedaner

Boeven

Gaikpa

et al. 2020

IJMS

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Generating genomics-driven knowledge opens a way to accelerate the resistance breeding process by family or population mapping and genomic selection. Important prerequisites are large populations that are genomically analyzed by medium- to high-density marker arrays and extensive phenotyping across locations and years of the same populations. The latter is important to train a genomic model that is used to predict genomic estimated breeding values of phenotypically untested genotypes. After reviewing the specific features of quantitative resistances and the basic genomic techniques, the possibilities for genomics-assisted breeding are evaluated for six pathosystems with hemi-biotrophic fungi: Small-grain cereals/Fusarium head blight (FHB), wheat/Septoria tritici blotch (STB) and Septoria nodorum blotch (SNB), maize/Gibberella ear rot (GER) and Fusarium ear rot (FER), maize/Northern corn leaf blight (NCLB). Typically, all quantitative disease resistances are caused by hundreds of QTL scattered across the whole genome, but often available in hotspots as exemplified for NCLB resistance in maize. Because all crops are suffering from many diseases, multi-disease resistance (MDR) is an attractive aim that can be selected by specific MDR QTL. Finally, the integration of genomic data in the breeding process for introgression of genetic resources and for the improvement within elite materials is discussed.

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Abiotic Stresses

Trethowan

2022

Wheat Improvement

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Abiotic stresses, such as drought and high temperature, significantly limit wheat yield globally and the intensity and frequency of these stresses are projected to increase in most wheat growing areas. Wheat breeders have incrementally improved the tolerance of cultivars to these stresses through empirical selection in the environment, however new phenotyping and genetic technologies and strategies can significantly improve rates of genetic gain. The integration of new tools and knowledge in the plant breeding process, including better breeding targets, improved choice of genetic diversity, more efficient phenotyping methods and strategy and optimized integration of genetic technologies in the context of several commonly used wheat breeding strategies is discussed. New knowledge and tools that improve the efficiency and speed of wheat improvement can be integrated within the scaffold of most wheat breeding strategies without significant increase in cost.

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Marker-assisted recurrent selection improves the crown rot resistance of bread wheat

Cited by 27 publications

References 37 publications

Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot

Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot

Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize

Abiotic Stresses

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