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 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 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

scholarly journals A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ronghao Wang ◽  
Yumou Qiu ◽  
Yuzhen Zhou ◽  
Zhikai Liang ◽  
James C. Schnable
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Plant Growth ◽  
High Throughput ◽  
Confidence Region ◽  
Morphological Operations ◽  
Growth Curve Analysis ◽  
High Throughput Phenotyping

High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

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