Mixed models and multivariate analysis for selection of superior maize genotypes

Oliveira, Gustavo Hugo Ferreira de; Amaral, Camila Baptista do; Silva, Flávia A. M.; Dutra, Sophia Mangussi Franchi; Marconato, Marcela Bonafin; Môro, Gustavo Vitti

doi:10.4067/s0718-58392016000400005

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Thus, the REML/Blup methodology is an important statistical tool, which can help breeders to conduct breeding programs during selection processes. Similar results demonstrating the potential for the REML/Blup technique were seen in studies on beans, by Sousa et al (2017), Gomes et al (2018), and Cruz et al (2018); and maize, by Arnhold et al (2009), Oliveira et al (2016), Entringer et al (2016), and Mendonça et al (2016), all confirming the results of this study. ) and mean grain mass, municipality of Jataí, Goiás State, Brazil, UFG, 2018.…”

Section: Predicted Genetic Values (μ + G)supporting

confidence: 91%

Selection of top cross hybrids for green maize yield via REML/Blup method

et al. 2020

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The purpose of this study was to select top cross hybrids of green maize for yield, derived from partially inbred S1 lines based on genetic values using the REML/Blup method, and to estimate important genetic parameters for green maize breeding programs. The experiment was conducted in an experimental area located between 17º53´ S and 52º43´ W, 680 m altitude. The evaluation of 75 top cross hybrids was performed in a randomized block design with four replicates. A sample of five plants/ears was used in each plot to evaluate grain mass trait (MASS). For commercial ear yield trait (CEYIELD), evaluations were carried out for the total number of plants per plot. Hybrids were selected via BLUP procedures using the Selegen-REML/Blup program. Based on the Restricted Maximum Likelihood (REML), we estimated the coefficients of genetic and residual variation and components of variance, by which a genetic variability between the top cross hybrids was observed. This shows the possibility of successful selection for the traits under evaluation. The estimated accuracy for the selection of top cross hybrids was 0.81 for commercial ear yield and 0.64 for grain mass, pointing to high and moderate precision levels for CEYIELD and MASS traits, respectively, corroborating the possibility of success in selecting top cross hybrids based on the CEYIELD trait. The predicted genetic gain from the selection was 20.12%, for CEYIELD, and 6.10%, for MASS. Therefore, the REML/Blup statistical tool was efficient in selecting top cross hybrids of green maize, providing significant genetic gains for the traits under evaluation. There was evidence that hybrids 19 and 48 were distinguished from others because of the high genetic effects obtained for the commercial ear yield and grain mass weight.

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Section: Predicted Genetic Values (μ + G)supporting

confidence: 91%

Selection of top cross hybrids for green maize yield via REML/Blup method

et al. 2020

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“…Evaluation of the results of a treatment given is closely related to the results of the characters involved. However, using more selection characters causes a more significant error rate and effectiveness of an evaluation, especially if a character has a different direction of variation (Oliveira et al, 2016;Nardino et al, 2020). Correlation analysis can be further used to obtain characters that are considered to have a solid relationship with the main character (Fadhilah et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Functional food biofortification in increasing red and black rice production through the use of nano silica organic fertilizer

Haruni,

Padjung,

Musa

et al. 2024

Chil. j. agric. res.

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Increasing paddy consumption can be done through efforts to increase yields by improving production technology, one of which is using organic nano silica fertilizer. Food biofortification efforts in paddy are also considered a solution for improving nutrition to help improve community nutrition. This research aims to determine the colour cultivar and dosage of nano silica organic fertilizer and the interaction between the two, which provide high growth and production and determine the nutritional quality of functional paddy. Functional paddy planting occurred in Manakku Village, Labakkang District, Pangkep Regency, South Sulawesi, from May to August 2023. The research was arranged in a separate plot design with the main plot varying doses (kg ha -1 ) of nano silica organic fertilizer consisting of control (d0), 500 (d1), 1000 (d2), and 1500 (d3), as well as subplots of four cultivars of red rice (Oryza sativa L.), namely Inpari 24 (v1), Pamelen (v2), Pamera (v3) and Inpago 7 (v4) as well as one cultivar of black rice Jeliteng (v5). The results were that the interaction of 1000 kg ha -1 organic nano silica fertilizer and red rice 'Inpago 7' (d2v4) provides the best yield of 5.62 t ha -1 . Characters that have a significant positive correlation with yield are plant height (0.64**), number of productive tiller (0.74**), grain total (0.54*), and weight of 1000 grains (0.88**). The quality of functional rice based on glycaemic index (GI) analysis showed that the black rice 'Jeliteng' with a dose of organic nano silica fertilizer of 1000 kg ha -1 (58.9%) had the lowest GI value.

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“…Genotypic selection is very difficult with a large number of individuals (Noerwijati et al, 2021). Clustering (multivariate analysis) is an efficient tool for the genotypic selection process (Kozak et al, 2008) that classifies the phenotypic variations with the function of homogeneity within intra-clusters and heterogeneity among the interclusters (Oliveira et al, 2016). Significant intra-cluster variability (p< 0.01) was observed with respect to fruit-quality traits in guava.…”

Section: Selection Of Excellent Introgressed Linesmentioning

confidence: 99%

Construction of a genetic linkage map and QTL mapping of fruit quality traits in guava (Psidium guajava L.)

et al. 2023

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Guava (Psidium guajava L.) is an important fruit crop of the Indian sub-continent, with potential for improvements in quality and yield. The goal of the present study was to construct a genetic linkage map in an intraspecific cross between the elite cultivar ‘Allahabad Safeda’ and the Purple Guava landrace to identify the genomic regions responsible for important fruit quality traits, viz., total soluble solids, titratable acidity, vitamin C, and sugars. This population was phenotyped in field trials (as a winter crop) for three consecutive years, and showed moderate-to-high values of heterogeneity coefficients along with higher heritability (60.0%–97.0%) and genetic-advance-over-mean values (13.23%–31.17%), suggesting minimal environmental influence on the expression of fruit-quality traits and indicating that these traits can be improved by phenotypic selection methods. Significant correlations and strong associations were also detected among fruit physico-chemical traits in segregating progeny. The constructed linkage map consisted of 195 markers distributed across 11 chromosomes, spanning a length of 1,604.47 cM (average inter-loci distance of 8.80 markers) and with 88.00% coverage of the guava genome. Fifty-eight quantitative trait loci (QTLs) were detected in three environments with best linear unbiased prediction (BLUP) values using the composite interval mapping algorithm of the BIP (biparental populations) module. The QTLs were distributed on seven different chromosomes, explaining 10.95%–17.77% of phenotypic variance, with the highest LOD score being 5.96 for qTSS.AS.pau-6.2. Thirteen QTLs detected across multiple environments with BLUPs indicate stability and utility in a future breeding program for guava. Furthermore, seven QTL clusters with stable or common individual QTLs affecting two or more different traits were located on six linkage groups (LGs), explaining the correlation among fruit-quality traits. Thus, the multiple environmental evaluations conducted here have increased our understanding of the molecular basis of phenotypic variation, providing the basis for future high-resolution fine-mapping and paving the way for marker-assisted breeding of fruit-quality traits.

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Mixed models and multivariate analysis for selection of superior maize genotypes

Cited by 10 publications

References 12 publications

Selection of top cross hybrids for green maize yield via REML/Blup method

Selection of top cross hybrids for green maize yield via REML/Blup method

Functional food biofortification in increasing red and black rice production through the use of nano silica organic fertilizer

Construction of a genetic linkage map and QTL mapping of fruit quality traits in guava (Psidium guajava L.)

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