Across year and year-by-year GGE biplot analysis to evaluate soybean performance and stability in multi-environment trials

Dalló, Samuel Cristian; Zdziarski, Andrei Daniel; Woyann, Leomar Guilherme; Milioli, Anderson Simionato; Zanella, Rodrigo; Conte, Josiane

doi:10.1007/s10681-019-2438-x

Cited by 32 publications

(23 citation statements)

References 13 publications

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“…This will be conducted in a further study. However, the findings were in agreement with previously reported studies where the year effect can substantially impact plant height, maturity, seed weight, and yield in soybean (Baig et al 2018;Dalló et al 2019;Jiang et al 2018;Kato et al 2018;Wiggins et al 2019). In this report, seed weight was negatively correlated with plant height.…”

Section: Discussionsupporting

confidence: 93%

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola

Qin²,

Shi

et al. 2019

Preprint

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Background Soybean [ Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to evaluate maturity, plant height, seed weight, and yield in a diverse soybean accession panel, to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy. Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci ( E2 , E4 , and Dt1 ) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900 , Glyma.19g200800 , Glyma.09g196700 , and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent. Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits through marker-assisted selection (MAS) and GS in soybean breeding programs. After validation, the candidate genes can be transferred to new cultivars using SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.

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Section: Discussionsupporting

confidence: 93%

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola

Qin²,

Shi

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…This will be conducted in a further study. However, the ndings were in agreement with previously reported studies where the year effect can substantially impact plant height, maturity, seed weight, and yield in soybean [22][23][24][25][26]. In this report, seed weight was negatively correlated with plant height.…”

Section: Discussionsupporting

confidence: 93%

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola¹,

Qin²,

Shi³

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundSoybean [Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy.Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). Population structure was inferred using STRUCTURE 2.3.4, GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that: a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci (E2, E4, and Dt1) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900, Glyma.19g200800, Glyma.09g196700, and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP (if no annotated genes found close the top one) for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent.Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits in soybean through marker-assisted selection (MAS) and GS in breeding programs. After validation, the candidate genes can be transferred to new cultivars using the linked SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.

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“…(2000), has been widely employed in sorghum breeding (Batista et al., 2017; Figueir, 2015; Gill et al., 2014; Rakshit et al., 2012; Rakshit, Ganapathy, et al., 2014; Rakshit et al., 2016; Teodoro et al., 2016; Rao et al., 2011). This method uses principal components applied to the effects of genotypes plus G × E interaction to delimit mega‐environments, identify testing locations, and recommend the best genotypes (Dalló et al., 2019; Singh et al., 2020 ). Shape and patterns are shown in the biplot, including the correlations between testing environment, depending on the relative magnitude of genotype and G × E interaction effects.…”

Section: Introductionmentioning

confidence: 99%

Identification of mega‐environments for grain sorghum in Brazil using GGE biplot methodology

et al. 2021

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The performance of genotypes in a wide range of environments can be affected by extensive genotype × environment (G × E) interactions, making the subdivision of the testing environments into relatively more homogeneous groups of locations (mega‐environments) a necessary strategy. The genotype main effects + genotype × environment interaction biplot method (GGE) allows identification of mega‐environments and selection of stable genotypes adapted to specific environments and mega‐environments. The objectives of this study were to identify mega‐environments regarding sorghum [Sorghum bicolor (L.) Moench] grain yield and demonstrate that the GGE biplot method can identify essential locations for conducting tests in different mega‐environments. A total of 22 competition trials of grain sorghum genotypes were conducted over three crop seasons across several production locations in Brazil. A total of 25, 22, and 30 genotypes were evaluated during the first, second, and third crop seasons, respectively. After identifying the presence of G × E interactions, the data were subjected to adaptability and stability analyses using the GGE biplot method. A phenotypic correlation network was used to express functional relationships between environments. The GGE biplot was found to be an efficient approach for identifying three mega‐environments in grain sorghum in Brazil, selecting representative and discriminative environments, and recommending more adaptive and stable grain sorghum genotypes.

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Across year and year-by-year GGE biplot analysis to evaluate soybean performance and stability in multi-environment trials

Cited by 32 publications

References 13 publications

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Identification of mega‐environments for grain sorghum in Brazil using GGE biplot methodology

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