Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes

Nascimento, Moysés; Teodoro, Paulo Eduardo; Sant’Anna, Isabela de Castro; Barroso, Laís Mayara Azevedo; Nascimento, Ana Carolina Campana; Azevedo, Camila Ferreira; Teodoro, Larissa Pereira Ribeiro; Farias, F. J. C.; Almeida, Helaine Claire Ferreira de; Carvalho, Luiz Paulo de

doi:10.3390/agronomy11112179

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Here, a linear regression analysis (Equation ()) was performed on the individual treatment response as function of an environmental index for that respective response (I, Equation ()), which was calculated as the difference between the measured traits for that particular G‐E‐M combination and the overall grand mean for that trait. The output of this analysis includes an adaptability index (i.e., the slope of the relationship), a stability index (i.e., the model fit statistics), and the mean trait value:

\begin{equation}{Y_{ij}} = \;\beta + \alpha {I_j} + {\delta _{ij}}\end{equation}

where Y ij is the observation of the i th genotype in the j th environment; β is the general mean of the i th genotype over all environments, α is the regression coefficient that measures the response of i th genotype to different environments, and δ ij is the deviation from regression of the i th genotype in the j th environment; I j is the environmental index (Nascimento et al., 2021)

\begin{equation}{I_j} = \frac{{\mathop \sum \nolimits_i {Y_{ij}}}}{g}\; - \frac{{\mathop \sum \nolimits_i \mathop \sum \nolimits_j {Y_{ij}}}}{{ga}}\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

“…where Y ij is the observation of the ith genotype in the jth environment; β is the general mean of the ith genotype over all environments, α is the regression coefficient that measures the response of ith genotype to different environments, and δ ij is the deviation from regression of the ith genotype in the jth environment; I j is the environmental index (Nascimento et al, 2021)…”

Section: Crop Sciencementioning

confidence: 99%

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Intensive management simultaneously reduces yield gaps and improves milling and baking properties of bread wheat

et al. 2023

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Genotype (G), environment (E), management (M), and their interactions determine grain yield, biochemical composition, and quality of wheat. However, we are not aware of studies evaluating whether intensive management can simultaneously reduce yield gaps and improve wheat milling and baking attributes, which were our main objectives. A factorial experiment of five genotypes × four management intensities (farmer practice, high-input, high-input minus nitrogen, and high-input minus fungicide) was conducted in six Kansas (United States) environments resulting from the combination of four locations and three seasons. Grain yield and 13 parameters characterizing the grain (test weight [TWT], thousand kernel weight [TKW], grain protein [GP]), flour (e.g., ash, protein), dough (e.g., water absorption [WA],dough stability [DS]), and baking qualities (loaf volume [LV]) were measured. For most variables, the primary contributor of the variance was E while M and G were secondary and tertiary contributors, except for LV in which G was a larger moderator. High input practices increased yield (4206 kg ha −1 ) compared to farmer practice (3347 kg ha −1 ) suggesting a yield gap of 859 kg ha −1 . Management intensities receiving foliar fungicides improved grain properties related to milling characteristics (i.e., TKW and TWT), while management intensities receiving more nitrogen improved baking characteristics (e.g., WA and DS). Across sources of variation, yield response to improved management associated positively with the responses of TKW, TWT, and in some cases, GP, and WA. Our results showed the possibility of simultaneously improving grain yield and selected milling and baking attributes of bread wheat genotypes through intensive management.Abbreviations: DDT, dough development time (min); DS, dough stability (min); FA, flour ash (%); FNV, falling number (s); FST, farinograph stability (min); FY, flour yield (%); G, genotype; GP, grain protein (%); GY, grain yield (kg ha −1 ); HWW, hard winter wheat; LV, loaf volume (mL); MTI, mixing tolerance index (BU); TKW, thousand kernel weight (g); TWT, test weight (kg hl −1 ); WA, water absorption.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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\begin{equation}{Y_{ij}} = \;\beta + \alpha {I_j} + {\delta _{ij}}\end{equation}

\begin{equation}{I_j} = \frac{{\mathop \sum \nolimits_i {Y_{ij}}}}{g}\; - \frac{{\mathop \sum \nolimits_i \mathop \sum \nolimits_j {Y_{ij}}}}{{ga}}\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Section: Crop Sciencementioning

confidence: 99%

Intensive management simultaneously reduces yield gaps and improves milling and baking properties of bread wheat

et al. 2023

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“…Phenotypic and genotypic data are susceptible to outlier samples, regardless of the cause. Outlier observations have been observed in single-site and multi-environment trials 13 , 14 , DNA-seq 15 , 16 , and RNA-seq 17 data. Therefore, the presence of such observations in the data is inevitable and violates the underlying assumptions of many statistical analyses 18 .…”

Section: Introductionmentioning

confidence: 95%

Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS

Abdi

Alipour

Bernousi

et al. 2023

Sci Rep

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Principal component analysis (PCA) is widely used in various genetics studies. In this study, the role of classical PCA (cPCA) and robust PCA (rPCA) was evaluated explicitly in genome-wide association studies (GWAS). We evaluated 294 wheat genotypes under well-watered and rain-fed, focusing on spike traits. First, we showed that some phenotypic and genotypic observations could be outliers based on cPCA and different rPCA algorithms (Proj, Grid, Hubert, and Locantore). Hubert’s method provided a better approach to identifying outliers, which helped to understand the nature of these samples. These outliers led to the deviation of the heritability of traits from the actual value. Then, we performed GWAS with 36,000 single nucleotide polymorphisms (SNPs) based on the traditional approach and two robust strategies. In the conventional approach and using the first three components of cPCA as population structure, 184 and 139 marker-trait associations (MTAs) were identified for five traits in well-watered and rain-fed environments, respectively. In the first robust strategy and when rPCA was used as population structure in GWAS, we observed that the Hubert and Grid methods identified new MTAs, especially for yield and spike weight on chromosomes 7A and 6B. In the second strategy, we followed the classical and robust principal component-based GWAS, where the first two PCs obtained from phenotypic variables were used instead of traits. In the recent strategy, despite the similarity between the methods, some new MTAs were identified that can be considered pleiotropic. Hubert's method provided a better linear combination of traits because it had the most MTAs in common with the traditional approach. Newly identified SNPs, including rs19833 (5B) and rs48316 (2B), were annotated with important genes with vital biological processes and molecular functions. The approaches presented in this study can reduce the misleading GWAS results caused by the adverse effect of outlier observations.

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“…Selection for fiber quality and yield is complicated by the contribution of genotype × environment (G × E) interactions on these traits [ 1 , 10 ]. Generally speaking, breeders select for trait stability across their candidate environments, although yearly fluctuations in weather, precipitation, disease pressure, and random field variation can confound a breeder’s ability to make direct selections on observed phenotype means and relative ranks [ 11 , 12 ]. Understanding how the genetic underpinnings of key traits in cotton vary with respect to the loci of variable effect sizes (small to high) or quantitative trait loci (QTL), and the cumulative effect of many loci (the polygenic effect) in multiple years and locations is crucial for designing more efficient selection schemes [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs

Billings

Jones

Rustgi

et al. 2022

Plants

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Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.

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Influential Points in Adaptability and Stability Methods Based on Regression Models in Cotton Genotypes

Cited by 3 publications

References 21 publications

Intensive management simultaneously reduces yield gaps and improves milling and baking properties of bread wheat

Intensive management simultaneously reduces yield gaps and improves milling and baking properties of bread wheat

Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS

Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs

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