Dataset Information

Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

ABSTRACT:

Key message

We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.

SUBMITTER: Toda Y

PROVIDER: S-EPMC10924738 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

Toda Yusuke Y Sasaki Goshi G Ohmori Yoshihiro Y Yamasaki Yuji Y Takahashi Hirokazu H Takanashi Hideki H Tsuda Mai M Kajiya-Kanegae Hiromi H Tsujimoto Hisashi H Kaga Akito A Hirai Masami M Nakazono Mikio M Fujiwara Toru T Iwata Hiroyoshi H

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 20240309 4

<h4>Key message</h4>We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E an ...[more]

PMID: 38460027

Similar Datasets

Project description:Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers and environmental covariates. However, comprehensive multi-trait reaction norm models accounting for marker × environmental covariates interactions are lacking. In this article, we propose to extend a reaction norm model incorporating genotype-by-environment interactions through (co)variance structures of markers and environmental covariates to a multi-trait reaction norm case. To do that, we propose a novel methodology for characterizing the environment at different growth stages based on growth degree-days (GDD). The proposed models were evaluated by variance components estimation and predictive performance for winter wheat grain yield and protein content in a set of 2,015 F6-lines. Cross-validation analyses were performed using leave-one-year-location-out (CV1) and leave-one-breeding-cycle-out (CV2) strategies. The modeling of genomic [SNPs] × environmental covariates interactions significantly improved predictive ability and reduced the variance inflation of predicted genetic values for grain yield and protein content in both cross-validation schemes. Trait-assisted genomic prediction was carried out for multi-trait models, and it significantly enhanced predictive ability and reduced variance inflation in all scenarios. The genotype by environment interaction modeling via genomic [SNPs] × environmental covariates interactions, combined with trait-assisted genomic prediction, boosted the benefits in predictive performance. The proposed multi-trait reaction norm methodology is a comprehensive approach that allows capitalizing on the benefits of multi-trait models accounting for between-trait correlations and reaction norm models exploiting high-dimensional genomic and environmental information.

Project description:BackgroundThe role of soil microorganisms in plant growth, nutrient utilization, drought tolerance as well as biocontrol activity cannot be over-emphasized, especially in this era when food crisis is a global challenge. This research was therefore designed to gain genomic insights into plant growth promoting (PGP) Rhizobium species capable of enhancing soybean (Glycine max L.) seeds germination under drought condition.ResultsRhizobium sp. strain R1, Rhizobium tropici strain R2, Rhizobium cellulosilyticum strain R3, Rhizobium taibaishanense strain R4 and Ensifer meliloti strain R5 were found to possess the entire PGP traits tested. Specifically, these rhizobial strains were able to solubilize phosphate, produce exopolysaccharide (EPS), 1-aminocyclopropane-1-carboxylate (ACC), siderophore and indole-acetic-acid (IAA). These strains also survived and grew at a temperature of 45 °C and in an acidic condition with a pH 4. Consequently, all the Rhizobium strains enhanced the germination of soybean seeds (PAN 1532 R) under drought condition imposed by 4% poly-ethylene glycol (PEG); nevertheless, Rhizobium sp. strain R1 and R. cellulosilyticum strain R3 inoculations were able to improve seeds germination more than R2, R4 and R5 strains. Thus, genomic insights into Rhizobium sp. strain R1 and R. cellulosilyticum strain R3 revealed the presence of some genes with their respective proteins involved in symbiotic establishment, nitrogen fixation, drought tolerance and plant growth promotion. In particular, exoX, htrA, Nif, nodA, eptA, IAA and siderophore-producing genes were found in the two rhizobial strains.ConclusionsTherefore, the availability of the whole genome sequences of R1 and R3 strains may further be exploited to comprehend the interaction of drought tolerant rhizobia with soybean and other legumes and the PGP ability of these rhizobial strains can also be harnessed for biotechnological application in the field especially in semiarid and arid regions of the globe.

Project description:Recently, the application of endophytes in the alleviation of different types of stresses has received considerable attention, but their role in drought stress alleviation and growth promotion in soybean is not well-stated. In this study, twenty bacterial endophytes were isolated from soybean root tissues and screened for plant growth-promoting (PGP) traits, biocontrol potential, and drought stress alleviation. Out of them, 80% showed PGP traits, and 20% showed antagonistic activity against Fusarium oxysporum (ITCC 2389), Macrophomina phaseolina (ITCC 1800), and Alternaria alternata (ITCC 3467), and only three of them showed drought tolerance up to 15% (-0.3 MPa). Results indicated that drought-tolerant PGP endophytic bacteria enhanced soybean seedling growth under drought stress conditions. Morphological, biochemical, and molecular characterization (16S rRNA) revealed that these three bacterial isolates, AKAD A1-1, AKAD A1-2, and AKAD A1-16, closely resemble Bacillus cereus (GenBank accession No. MN079048), Pseudomonas otitidis (MW301101), and Pseudomonas sp. (MN079074), respectively. We observed that the soybean seedlings were grown in well-watered and drought-stressed soil showed the adverse effect of drought stress on morphological (stem length, root length, plant fresh and dry weight) as well as on biochemical parameters (a decline of photosynthetic pigments, membrane damage, etc.). However, soybean seedlings inoculated with these endophytes have improved the biomass significantly (p ≤ 0.05) under normal as well as in drought stress conditions over control treatments by influencing several biochemical changes. Among these three endophytes, AKAD A1-16 performed better than AKAD A1-2 and AKAD A1-1, which was further validated by the ability to produce the enzyme 1-aminocyclopropane-1-carboxylate (ACC) deaminase in the following order: AKAD A1-16 > AKAD A1-2 > AKAD A1-1. Scanning electron microscopy images also showed a bacterial presence inside the roots of soybean seedlings. These findings supported the application of bacterial root endophytes as a potential tool to mitigate the effect of drought as well as of fungal diseases on the early seedling growth of soybean.

Project description:Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.

Project description:The capability of a plant to protect itself from stress-related damages is termed "adaptability" and the phenomenon of showing better performance in subsequent stress is termed "stress memory". While drought is one of the most serious disasters to result from climate change, the current understanding of drought stress priming in soybean is still inadequate for effective crop improvement. To fill this gap, in this study, the drought memory response was evaluated in cultivated soybean (Glycine max). To determine if a priming stress prior to a drought stress would be beneficial to the survival of soybean, plants were divided into three treatment groups: the unprimed group receiving one cycle of stress (1S), the primed group receiving two cycles of stress (2S), and the unstressed control group not subjected to any stress (US). When compared with the unprimed plants, priming led to a reduction of drought stress index (DSI) by 3, resulting in more than 14% increase in surviving leaves, more than 13% increase in leaf water content, slight increase in shoot water content and a slower rate of loss of water from the detached leaves. Primed plants had less than 60% the transpiration rate and stomatal conductance compared to the unprimed plants, accompanied by a slight drop in photosynthesis rate, and about a 30% increase in water usage efficiency (WUE). Priming also increased the root-to-shoot ratio, potentially improving water uptake. Selected genes encoding late embryogenesis abundant (LEA) proteins and MYB, NAC and PP2C domain-containing transcription factors were shown to be highly induced in primed plants compared to the unprimed group. In conclusion, priming significantly improved the drought stress response in soybean during recurrent drought, partially through the maintenance of water status and stronger expression of stress related genes. In sum, we have identified key physiological parameters for soybean which may be used as indicators for future genetic study to identify the genetic element controlling the drought stress priming.

Dataset Information

Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

Key message

Publications

Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets