Genomic analysis of maize lines introduced in the early stages of a breeding programme

Cantelmo, Narjara Fonseca; Pinho, Renzo Garcia Von; Balestre, Márcio

doi:10.1111/pbr.12543

Cited by 4 publications

(5 citation statements)

References 80 publications

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“…The slight reduction in generalized heritability from the general analysis to the seasonal analyses (Figure 3) occurred due to the smaller number of environments -thus, information -in the former. Nevertheless, the values for this parameter are consistent with the ones normally found in the literature for tropical maize (Dias, Gezan, Guimarães, Nazarian, et al, 2018;Cantelmo et al, 2017;Pereira et al, 2022). By concomitantly analysing the generalized heritability and experimental coefficient of variation values, we verified that there was high experimental precision, which grants credibility to the results.…”

Section: Discussionsupporting

confidence: 89%

“…In this study, we applied FAST in a late-stage dataset of tropical maize evaluated in a target population of environments in two crop seasons with distinct environmental conditions. The first season, or summer season, tends to have longer photoperiod, higher water availability and higher solar radiation, which are considered optimal conditions for maize cultivation (Cantelmo et al, 2017). This season spans from September to December and is characterized by favourable weather for sowing depending on the climate zone (F. S. Dias et al, 2019).…”

Section: Core Ideasmentioning

confidence: 99%

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Employing factor analytic tools for selecting high‐performance and stable tropical maize hybrids

et al. 2023

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Genotype‐by‐environment interaction (GEI) is a major concern in tropical maize breeding. Here, we used factor analytic mixed models (FAMM) to study GEI in a tropical maize dataset. Our goal was to select high‐performance and stable hybrids using factor analytic selection tools (FAST) derived from FAMM outputs. Because the dataset comprised two different planting seasons, we also investigated differences in the genetic gains between seasonal and general selection (i.e., for both seasons simultaneously). The trials were installed in a lattice design with 36 hybrids containing two replicates and blocks with six plants each. We evaluated 53 hybrids and seven checks in 48 environments, represented by a combination of locations, years and seasons. We fitted the FAMMs with distinct sets of factors. The best‐fitted FAMM was selected considering a parsimony‐explained variance balance. Using the best FAMM for both seasonal and general analyses, we estimated the following selection tools: overall performance (OP$\textit{OP}$); root mean square deviation (RMSD$\textit{RMSD}$), which represents general stability; and responsiveness , which represents specific stability. We selected hybrids based on an index built with OP$\textit{OP}$ and RMSD$\textit{RMSD}$. Five and three hybrids were selected considering both general and seasonal analyses for the first and second season, respectively. Two hybrids were amongst the top eight for both seasonal analyses (i.e., only first season and only second season simultaenously). Higher genetic gains may be obtained by employing FAMM and using FAST in each season rather than analysing both seasons in a joint fashion.

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Section: Discussionsupporting

confidence: 89%

Section: Core Ideasmentioning

confidence: 99%

Employing factor analytic tools for selecting high‐performance and stable tropical maize hybrids

et al. 2023

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“…They concluded that GP is a useful approach for identifying the most informative genotypes. Cantelmo et al (2017) also reported a satisfactory correlation (0.53) between the GEBVs of 402 maize hybrids predicted in the winter crop season and their mean values observed in the summer crop season for grain yield. They also reported that the coincidence index of genomic values of the winter and summer seasons was more efficient to discard the 20% lowest-yielding hybrids (coincidence of 89%) than to select the highest-yielding hybrids (coincidence of 43%).…”

Section: Genomic Prediction For Inducer Developmentmentioning

confidence: 69%

Genomic prediction of maternal haploid induction rate in maize

et al. 2020

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Genomic prediction (GP) might be an efficient way to improve haploid induction rate (HIR) and to reduce the laborious and time-consuming task of phenotypic selection for HIR in maize (Zea mays L.). In this study, we evaluated GP accuracies for HIR and other agronomic traits of importance to inducers by independent and cross-validation. We propose the use of GP for cross prediction and parental selection in the development of new inducer breeding populations. A panel of 159 inducers from Iowa State University (ISU set) was genotyped and phenotyped for HIR and several agronomic traits. The data of an independent set of 53 inducers evaluated by the University of Hohenheim (UOH set) was used for independent validation. The HIR ranged from 0.61 to 20.74% and exhibited high heritability (0.90). High cross-validation prediction accuracy was observed for HIR (r = 0.82), whereas for other traits it ranged from 0.36 (self-induction rate) to 0.74 (days to anthesis). Prediction accuracies across different sets were higher when the larger panel (ISU set) was used as a training population (r = 0.54). The average HIR of the 12,561 superior predicted progenies (μ SP ) ranged from 1.00-18.36% and was closely related to the corresponding midparent genomic estimated breeding value (GEBV). A predicted genetic variance (V G ) of reduced magnitude was observed in the twenty crosses with highest midparent GEBV or μ SP for HIR. Our results indicate that although GP is a useful tool for parental selection, decisions about which cross combinations should be pursued need to be based on optimal trade-offs between maximizing both μ SP and V G .This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Genotyping based on DArT and GBS SNPs has been extensively applied in the association mapping of different crops (e.g., maize, common bean and wheat) (Ates et al, 2018;Cantelmo et al, 2017;Gowda et al, 2015;Ovenden et al, 2017), and in the present study, we compared the results obtained using these two approaches. After appro- Although in the present study we succeeded in narrowing the region corresponding to the major QTL in bin 8.03 compared with previous studies, we still identified 45 hypothetical genes with known predicted functions within this region, which is substantially higher than the 25 hypothetical genes with known predicted functions identified in bin 8.03 by Cao et al (2017).…”

Section: Discussionmentioning

confidence: 99%

“…Genotyping based on DArT and GBS SNPs has been extensively applied in the association mapping of different crops (e.g., maize, common bean and wheat) (Ates et al, 2018; Cantelmo et al, 2017; Gowda et al, 2015; Ovenden et al, 2017), and in the present study, we compared the results obtained using these two approaches. After appropriate filtering, 7258 and 232,801 high‐quality SNPs were retained for DArTseq‐based and GBS‐based association mapping of CML populations, respectively, with the same phenotypic datasets and threshold P value of −log 10 P = 4.53.…”

Section: Discussionmentioning

confidence: 99%

Association mapping of resistance to tar spot complex in maize

et al. 2022

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Tar spot complex (TSC) is a major fungal disease of maize in Mexico and several Central and South American countries. The causal agents of the disease are Phyllachora maydis and Monographella maydis. Yield losses of up to 58% associated with this disease have been reported previously. The majority of commercial maize germplasm in the United States is considered highly susceptible to TSC. In order to accelerate the disease resistance improvement process, genes for resistance to TSC and molecular markers linked to these genes, need to be identified and characterized. Here, we used an association mapping technique to detect quantitative trait loci (QTLs) for TSC resistance using genome‐wide association study (GWAS)‐based genotyping‐by‐sequencing (GBS) and Diversity Arrays Technology Sequencing (DArTseq) single‐nucleotide polymorphisms. An association mapping panel of 228 CIMMYT maize lines was used for the GWAS. One major QTL explaining 27.66% of the phenotypic variance and four minor QTLs explaining 10.18%, 12.72%, 10.14% and 13.38% of the phenotypic variance, respectively, were identified by the GBS‐based association mapping. Two QTLs were identified by DArTseq‐based association mapping explaining 1.07% and 18.29% of the phenotypic variance, respectively. These QTLs are expected to facilitate subsequent maize improvement using marker‐assisted selection.

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Genomic analysis of maize lines introduced in the early stages of a breeding programme

Cited by 4 publications

References 80 publications

Employing factor analytic tools for selecting high‐performance and stable tropical maize hybrids

Employing factor analytic tools for selecting high‐performance and stable tropical maize hybrids

Genomic prediction of maternal haploid induction rate in maize

Association mapping of resistance to tar spot complex in maize

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