QTL×QTL×QTL Interaction Effects for Total Phenolic Content of Wheat Mapping Population of CSDH Lines under Drought Stress by Weighted Multiple Linear Regression

Cyplik, Adrian; Czyczyło-Mysza, Ilona; Jankowicz-Cieślak, Joanna; Bocianowski, Jan

doi:10.3390/agriculture13040850

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…Ahmad et al [19] also reported that the correlation coefficient and direct effect of biological yield on grain yield are the highest. It was reported that the effect of biological yield on grain yield is significant even in drought-stressed wheat [31,46]. In addition, the results of the study are consistent with studies on durum wheat [47] and bread wheat [48,49].…”

Section: Discussionsupporting

confidence: 90%

Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses

et al. 2023

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Genetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some morphological traits of various wheat (Triticum spp.) species and to demonstrate the application of MARS and CHAID algorithms to wheat-derived data sets. Relationships among several morphological traits of wheat were investigated using a total of 26 different wheat genotypes. MARS and CHAID data mining methods were compared for grain yield prediction from different traits using cross-validation. In addition, an optimal CHAID tree structure with minimum RMSE was obtained and cross-validated with nine terminal nodes. Based on the smallest RMSE of the cross-validation, the eight-element MARS model was found to be the best model for grain yield prediction. The MARS algorithm proved superior to CHAID in grain yield prediction and accounted for 95.7% of the variation in grain yield among wheats. CHAID and MARS analyses on wheat grain yield were performed for the first time in this research. In this context, we showed how MARS and CHAID algorithms can help wheat breeders describe complex interaction effects more precisely. With the data mining methodology demonstrated in this study, breeders can predict which wheat traits are beneficial for increasing grain yield. The adaption of MARS and CHAID algorithms should benefit breeding research.

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Section: Discussionsupporting

confidence: 90%

Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses

et al. 2023

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“…However, the use of weighted regression shows that the difference between the phenotypic and genotypic estimation can be reduced, and the total genetic effects will be closer to the true value. Similar results were obtained by estimating QTL-QTL-QTL triple interaction effects for total phenolic content in the mapping population of wheat doubled haploid lines under drought stress conditions [30].…”

Section: Data Setsupporting

confidence: 78%

“…The above results concern QTL–QTL–QTL interaction effects obtained by the simplest possible methods, i.e., using multiple linear regression on the marker data. Cyplik et al [ 30 ] showed that modifying this regression by using empirical weights provides a better agreement between phenotypic and genotypic estimates.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

Cyplik

Bocianowski

2023

IJMS

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The goal of the breeding process is to obtain new genotypes with traits improved over the parental forms. Parameters related to the additive effect of genes as well as their interactions (such as epistasis of gene–by–gene interaction effect and additive–by–additive–by–additive of gene–by–gene–by–gene interaction effect) can influence decisions on the suitability of breeding material for this purpose. Understanding the genetic architecture of complex traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL–by–QTL interactions and QTL–by–QTL–by–QTL interactions. With regards to the comparing methods for estimating additive–by–additive–by–additive of QTL×QTL×QTL interaction effects by Monte Carlo simulation studies, there are no publications in the open literature. The parameter combinations assumed in the presented simulation studies represented 84 different experimental situations. The use of weighted regression may be the preferred method for estimating additive–by–additive–by–additive of QTL–QTL–QTL triples interaction effects, as it provides results closer to the true values of total additive–by–additive–by–additive interaction effects than using unweighted regression. This is also indicated by the obtained values of the determination coefficients of the proposed models.

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“…On both dates (the first and fourteenth day of drought), the genotype had a significant impact on the content of phenolic compounds, which indicates a different reaction of the tested OMA lines to water deficiency. In experiments on drought stress in spring triticale, an increase in the content of phenolic compounds was found, and this parameter was considered useful in selecting genotypes less susceptible to drought stress [ 46 ]. In potatoes, increased content of phenolic compounds was also observed as a result of drought, during which the analyzed secondary metabolites were responsible for the good general condition of plants [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Effect of Soil Drought Stress on Selected Biochemical Parameters and Yield of Oat × Maize Addition (OMA) Lines

Warzecha,

Bocianowski,

Warchoł

et al. 2023

IJMS

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Plant growth and the process of yield formation in crops are moderated by surrounding conditions, as well as the interaction of the genetic background of plants and the environment. In the last two decades, significant climatic changes have been observed, generating unfavorable and harmful impacts on plant development. Drought stress can be considered one of the most dangerous environmental factors affecting the life cycle of plants, reducing biomass production and, finally, the yield. Plants can respond to water deficit in a wide range, which depends on the species, genetic variability within the species, the plant’s ontogenesis stage, the intensity of the stress, and other potential stress factors. In plants, it is possible to observe hybrids between different taxa that certain traits adopted to tolerate stress conditions better than the parent plants. Oat × maize addition (OMA) plants are good examples of hybrids generated via wide crossing. They can exhibit morphological, physiological, and biochemical variations implemented by the occurrence of extra chromosomes of maize, as well as the interaction of maize and oat chromatin. The initial goal of the study was to identify OMA lines among plants produced by wide crossing with maize. The main goal was to investigate differences in OMA lines according to the Excised Leaf Water Loss (ELWL) test and to identify specific biochemical changes and agronomic traits under optimal water conditions and soil drought. Additionally, detection of any potential alterations that are stable in F2 and F3 generations. The aforementioned outcomes were the basis for the selection of OMA lines that tolerate growth in an environment with limited water availability. The molecular analysis indicated 12.5% OMA lines among all tested descendants of wide oat-maize crossing. The OMA lines significantly differ according to ELWL test results, which implies some anatomical and physiological adaptation to water loss from tissues. On the first day of drought, plants possessed 34% more soluble sugars compared to control plants. On the fourteen day of drought, the amount of soluble sugars was reduced by 41.2%. A significant increase of phenolic compounds was observed in the fourteen day of drought, an average of 6%, even up to 57% in line 9. Soil drought substantially reduced stem biomass, grains number, and mass per plant. Lower water loss revealed by results of the ELWL test correlated with the high yield of OMA lines. Phenolic compound content might be used as a biochemical indicator of plant drought tolerance since there was a significant correlation with the high yield of plants subjected to soil drought.

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QTL×QTL×QTL Interaction Effects for Total Phenolic Content of Wheat Mapping Population of CSDH Lines under Drought Stress by Weighted Multiple Linear Regression

Cited by 7 publications

References 47 publications

Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses

Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

Effect of Soil Drought Stress on Selected Biochemical Parameters and Yield of Oat × Maize Addition (OMA) Lines

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