scholarly journals High‐throughput phenotyping accelerates the dissection of the dynamic genetic architecture of plant growth and yield improvement in rapeseed

2020 ◽  
Vol 18 (11) ◽  
pp. 2345-2353 ◽  
Author(s):  
Haitao Li ◽  
Hui Feng ◽  
Chaocheng Guo ◽  
Shanjing Yang ◽  
Wan Huang ◽  
...  
Keyword(s):  
Plant Growth ◽  
High Throughput ◽  
Genetic Architecture ◽  
Growth And Yield ◽  
Yield Improvement ◽  
High Throughput Phenotyping

scholarly journals High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

2017 ◽  
Vol 173 (3) ◽  
pp. 1554-1564 ◽  
Author(s):  
Xuehai Zhang ◽  
Chenglong Huang ◽  
Di Wu ◽  
Feng Qiao ◽  
Wenqiang Li ◽  
...  
Keyword(s):  
Plant Growth ◽  
Qtl Mapping ◽  
High Throughput ◽  
Genetic Architecture ◽  
Maize Plant ◽  
High Throughput Phenotyping

scholarly journals High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production

2021 ◽  
Vol 12 ◽  
Author(s):  
Fabiana Freitas Moreira ◽  
Hinayah Rojas de Oliveira ◽  
Miguel Angel Lopez ◽  
Bilal Jamal Abughali ◽  
Guilherme Gomes ◽  
...  
Keyword(s):  
High Throughput ◽  
Regression Models ◽  
Genetic Architecture ◽  
Crop Improvement ◽  
Random Regression ◽  
Genome Wide Association Studies ◽  
Breeding Values ◽  
High Throughput Phenotyping ◽  
General Response ◽  
Over Time

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

scholarly journals Strong temporal dynamics of QTL action on plant growth progression revealed through high‐throughput phenotyping in canola

2019 ◽  
Vol 18 (1) ◽  
pp. 68-82 ◽  
Author(s):  
Dominic Knoch ◽  
Amine Abbadi ◽  
Fabian Grandke ◽  
Rhonda C. Meyer ◽  
Birgit Samans ◽  
...  
Keyword(s):  
Plant Growth ◽  
High Throughput ◽  
Temporal Dynamics ◽  
High Throughput Phenotyping

scholarly journals Genetic mapping of the early responses to salt stress in Arabidopsis thaliana

2020 ◽  
Author(s):  
Mariam Awlia ◽  
Nouf Alshareef ◽  
Noha Saber ◽  
Arthur Korte ◽  
Helena Oakey ◽  
...  
Keyword(s):  
Salt Stress ◽  
Quantum Yield ◽  
Plant Growth ◽  
High Throughput ◽  
Stress Responses ◽  
Genome Wide Association Study ◽  
Multivariate Analyses ◽  
Machine Learning Algorithms ◽  
Genetic Components ◽  
High Throughput Phenotyping

AbstractSalt stress decreases plant growth prior to significant ion accumulation in the shoot. However, the processes underlying this rapid reduction in growth are still unknown. To understand the changes in salt stress responses through time and at multiple physiological levels, examining different plant processes within a single setup is required. Recent advances in phenotyping has allowed the image-based estimation of plant growth, morphology, colour and photosynthetic activity. In this study, we examined the salt stress-induced responses of 191 Arabidopsis accessions from one hour to seven days after treatment using high-throughput phenotyping. Multivariate analyses and machine learning algorithms identified that quantum yield measured in the light-adapted state (Fv′/Fm′) greatly affected growth maintenance in the early phase of salt stress, while maximum quantum yield (QY max) was crucial at a later stage. In addition, our genome-wide association study (GWAS) identified 770 loci that were specific to salt stress, in which two loci associated with QY max and Fv′/Fm′ were selected for validation using T-DNA insertion lines. We characterised an unknown protein kinase found in the QY max locus, which reduced photosynthetic efficiency and growth maintenance under salt stress. Understanding the molecular context of the identified candidate genes will provide valuable insights into the early plant responses to salt stress. Furthermore, our work incorporates high-throughput phenotyping, multivariate analyses and GWAS, uncovering details of temporal stress responses, while identifying associations across different traits and time points, which likely constitute the genetic components of salinity tolerance.

scholarly journals In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR

2018 ◽  
Vol 9 ◽  
Author(s):  
Shangpeng Sun ◽  
Changying Li ◽  
Andrew H. Paterson ◽  
Yu Jiang ◽  
Rui Xu ◽  
...  
Keyword(s):  
Plant Growth ◽  
Cotton Plant ◽  
High Throughput ◽  
Growth Analysis ◽  
High Throughput Phenotyping
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