Usefulness of marker‐assisted selection to improve maize for increased resistance to <i>Sesamia nonagrioides</i> attack with no detrimental effect on yield

Samayoa, Luis Fernando; Butrón, Ana; Malvar, R. A.

doi:10.1111/aab.12480

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…Indirect selection based on molecular markers (marker assisted selection; MAS) was initially based on quantitative trait loci (QTL) identified in a biparental population or as a subset of significant markers from genome wide association studies (GWAS). MAS has proven to be particularly effective for qualitative traits (Jiang, 2013;Das et al, 2017;Samayoa L et al, 2019), which are controlled by few genes with large effects, whereas, quantitative traits are controlled by many genes with small effects. For quantitative traits, indirect selection based on genomic prediction is considered a more appropriate and practical breeding tool over MAS (Jannink et al, 2010;Heslot et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass

et al. 2020

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In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (r g = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (r g = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (r p = −0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (r p = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

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Section: Introductionmentioning

confidence: 99%

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass

et al. 2020

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“…Two decades of research on maize resistance to MCB has demonstrated the high negative genetic correlation between yield potential and resistance. Such that pursuing higher resistance, measured as reduced tunnel length made by MCB in the pith, has significantly contributed to reduced kernel yield (Ordaś et al, 2010;Butroń et al, 2012;Jimeńez-Galindo et al, 2017;Samayoa et al, 2019). Although this negative relationship is not found in all genetic backgrounds (Samayoa et al, 2014;Jimeńez-Galindo et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…An alternative to PS could be Marker Assisted Selection (MAS) using markers linked to QTL with significant effects on resistance to stem tunneling by MCB but without adverse effects on yield (Ordaś et al, 2010;Samayoa et al, 2019). However, commonly employed MAS strategies are not suitable for improving complex agronomic traits, in part, due to the lack of power to capture small effect loci that are often important in the inheritance of these traits since many minor genes are involved in MCB resistance, measured as reduced pith gallery length, and yield (Butroń et al, 2009;Butroń et al, 2012).…”

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confidence: 99%

Genomic versus phenotypic selection to improve corn borer resistance and grain yield in maize

et al. 2023

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IntroductionThe study of yield and resistance/tolerance to pest are related traits fundamental for maize breeding programs. Genomic selection (GS), which uses all marker information to calculate genomic breeding values, is presented as an emerging alternative to phenotypic and marker-assisted selections for improving complex traits controlled by many genes with small effects. Therefore, although phenotypic selection (PS) has been effective for increasing resistance and yield under high infestation with maize stem borers, higher genetic gains are expected to be obtained through GS based on the complex architecture of both traits. Our objective was to test whether GS is more effective than PS for improving resistance and/or tolerance to maize stem borers and grain yield.MethodsFor this, we compared different selection programs based on phenotype and genotypic value for a single trait, resistance or yield, and for both traits together.Results and discussionWe obtained that GS achieved the highest genetic gain for yield, meanwhile phenotypic selection for yield was the program that achieved the highest reduction of tunnel length, but was ineffective for increasing yield. However, phenotypic or genomic selection for increased resistance may be more effective in improving both traits together; although the gains per cycle would be small for both traits.

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“…Therefore, MAS selection was envisioned as an alternative to phenotypic selection once genetic factors for resistance and yield could be disentangled. QTL studies for maize resistance to stem tunneling by S. nonagrioides larvae were carried out and MAS selection based on DNA markers flanking some QTLs was proven suitable to improve resistance without detrimental effects on yield (Ordas et al 2009(Ordas et al , 2010(Ordas et al , 2013Samayoa et al 2014Samayoa et al , 2015aSamayoa et al , 2015bSamayoa et al , 2019Jiménez-Galindo et al 2017. Nowadays, genomic selection is preferred over MAS selection approaches when traits are controlled by many genes with small additive effects as is the case for maize resistance and tolerance to stemborer attack.…”

Section: Varietal Resistance and Bt Technologymentioning

confidence: 99%

Recent trends in management strategies for two major maize borers: Ostrinia nubilalis and Sesamia nonagrioides

et al. 2023

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Stem borers (Lepidoptera) are common cereal pests. In many parts of the world, the species Ostrinia nubilalis and Sesamia nonagrioides stand out as important insect pests of economically important crops such as maize. Their management relied mainly on transgenic host plant resistance over the last 25 years. Technologies based on the insecticidal properties of Bacillus thuringiensis-derived proteins allowed widespread pest population suppression, especially for O. nubilalis. However, the recent discovery of Bt resistance, which has revitalized interest in both pests' biology and management, may jeopardize the effectiveness of such transgenic technologies. Historical information on O. nubilalis bionomy may need to be reassessed in light of changing climate conditions and changing agricultural practices, as well as increased production of alternate host crops across its distribution range. The current paper examines the bioecology and historical research that has been conducted to manage these two important maize-boring pests.

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Usefulness of marker‐assisted selection to improve maize for increased resistance to Sesamia nonagrioides attack with no detrimental effect on yield

Cited by 7 publications

References 19 publications

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass

Genomic versus phenotypic selection to improve corn borer resistance and grain yield in maize

Recent trends in management strategies for two major maize borers: Ostrinia nubilalis and Sesamia nonagrioides

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