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Studies on the effects of genotype, environment and their interaction (G x E) for phenotypic plasticity of soybean under field conditions

MANGGABARANI ANDI MADIHAH 東北大学

2022.08.12

概要

To date, each soybean cultivar is grown in a restricted cultivated area based on their phenotypes under the certain environment for giving the higher yield. Due to its phenotypic differences depending on the environment, called phenotypic plasticity, the world climate change is considered to cause severe damage in soybean cultivation bringing a reduction in the yield. In order to maintain the yield of soybean cultivation under changing environments, I studied the mechanism of phenotypic plasticity with consideration of the effects of genotype, environment, and their interactions (GxE) by detailed phenotyping of soybean cultivars with wide variety of genotypes growing under multiple environmental conditions.

Ninety-three cultivars, mainly from Japanese core collection, were grown in two different fields conditions, the experimental field at the University of Miyazaki (MF) (31.832°N, 131.409°S) and Kashimadai field at Tohoku University (TF) (38.461°N, 141.093°S) in 2018. In 2019, with the aim to increase environmental variations, I applied a multi-sowing time strategy in the two fields. Flowering time, weekly growth phenotypes, and terminal phenotypes related to vegetative growth, such as plant height, total number of branches and nodes, and related to reproductive phase, such as total number of pods and total grain weight, were observed for evaluation of phenotypic plasticity.

Regarding flowering time, early flowering tendency was observed in MF compared to TF and the effect of sowing time shift from June to July and August accelerated the flowering time, especially in the late flowering cultivars. Despite the flowering time changed due to the effects of fields and sowing times, the order of the time to flower among the tested cultivars remained consistent. This result indicates that flowering time was tightly controlled by genotypes that regulate the sensitivity against day-length. In the case of weekly growth phenotypes, temperature seemed to be the main factor causing plasticity rather than other environmental factors. Considering the terminal phenotypes related to vegetative growth, overall correlation against flowering time that shifted by the effects of field and sowing time were observed. These results indicated that flowering time, that can be shifted by sowing time, could be one of the key factors to control the phenotypic plasticity of soybean. For the terminal phenotypes related to reproductive phase, overall correlation against flowering time could be observed in MF, but not in TF. Significant reduction in both total number of pods and total grain weights were detected in the late flowering individuals of June sowing in TF, presumably caused by disruption of the sink and source balance.

To investigate the cause of phenotype plasticity observed in the field experiments, combined variance analysis was performed to estimate the contribution of genotype, environment, and their interaction for flowering time and five terminal phenotypes. The results suggested the significant effect of GxE in the phenotypic plasticity, especially in the total number of pods and total grain weight (29.1% - 31.2%). Furthermore, the results of biplot analysis based on genotype (G) plus GxE (GGE biplot) were consistent with the ANOVA. This GxE effect could be explained basically by the genotypic control on the flowering time and the plasticity in vegetative phenotypes due to environmental influence.
To investigate the genomic loci associated with the six target traits that showed phenotypic plasticity, I performed genome wide association studies (GWAS) using high density SNPs of the 93 cultivars. By comparing the identified SNPs across environmental factors, their phenotypic and genotypic associations changed depending on field and/or sowing time conditions. In GWAS for flowering time, candidate SNPs were detected in a field-dependent manner with identification of field specific candidates in multiple sowing times. In contrast, in GWAS for plant height, candidate SNPs were detected in a sowing time-dependent manner with identification of sowing time specific candidates in both MF and TF. In GWAS for total number of branches and nodes, candidate SNPs were detected in an environmental condition-dependent manner as similar Manhattan plot patterns were obtained in June 19 MF and August 19 TF. Regarding the total number of pods and total grain weight, the detection level by GWAS became lower especially in TF, indicating the complexity of reproductive phase related traits under the effects of genotype and GxE.

By taking the advantage of accumulated detailed phenotypes of a large variety of cultivars, with re-sequencing information, under multiple environments, my collaborator Prof. Nakaya, built multiple regression models targeting the flowering time and growth pattern. For the construction of the growth model, the weekly growth pattern of each individual was fitted to a logistic function that was an S-shaped curve determined by two parameters, K (maximum plant height) and r (initial growth rate). Then, regression models to predict the K and r were constructed. The constructed regression models contained the genotypes of selected genomic loci as the G factors and day average temperature in the growth phase as the E factors, and their interaction (GxE). The tuning of the models was done by dividing the data, half were randomly selected for training data for determination of the parameters, and the remaining half were used for evaluation. The final version of the regression models were constructed by using the all datasets in 2018 and 2019 for training data. Then, the prediction ability of the constructed models were evaluated using new phenotypic data observed in 2020 in MF and TF as well as in a new field. As a result, it was confirmed that constructed regression models were applicable to the new dataset including a novel field and thus it would be feasible to use the models for estimation of flowering time and growth phenotypes under the given environments.

Considering the future application of the constructed models, I performed comparative analyses between the growth parameter values, K and r, that can be estimated from the models, and yield phenotype (total grain weight). Based on the plot of r against K from field experiments with indication of total grain weight of each individual, high yield individuals were clustered in the circular area centered on r = ~ 0.09 and K = ~800, indicating that yield potential of soybean cultivar could be elucidated by setting the r and K values to these target values.

Overall, by performing detailed phenotyping of a wide variety of soybean cultivars under multiple environments, the plasticity among the observed phenotypes in soybean was clearly affected by genotype (G), environment (E), and their interaction (GxE). Regarding the environmental factors, it can be considered that day-length and temperature are the main contributors for phenotypic plasticity in soybean cultivars. Sowing time shifts will be a useful strategy to control the day-length effect as I confirmed the flowering time shift by sowing time was associated with physiological state. Regarding the temperature, the constructed regression models would be valuable tools to provide reliable estimation of the effects. As the target values of r and K for elucidating the yield potential were assigned, the regression models could be applied for prediction of suitable sowing time and/or suitable new cultivars under the changed environments. Therefore, the data set obtained in this study will contribute to sustainable soybean cultivation in the global warming era.

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