Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

Yassue, Rafael Massahiro; Galli, Giovanni; Chen, Chun‐Peng James; Fritsche‐Neto, Roberto; Morota, Gota

doi:10.1002/pld3.492

Cited by 3 publications

(1 citation statement)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 2021 ; Massahiro Yassue et al. 2023 ). In our present investigation, we reinforce this perspective by employing the SUnSet toolbox.…”

Section: Discussionmentioning

confidence: 99%

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

Jeong,

Lyu,

Jeong

et al. 2024

Plant Cell Rep

View full text Add to dashboard Cite

Key message Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Abstract Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

show abstract

“… 2021 ; Massahiro Yassue et al. 2023 ). In our present investigation, we reinforce this perspective by employing the SUnSet toolbox.…”

Section: Discussionmentioning

confidence: 99%

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

Jeong,

Lyu,

Jeong

et al. 2024

Plant Cell Rep

View full text Add to dashboard Cite

show abstract

Toward Ecologically Relevant Genetics of Interactions Between Host Plants and Plant Growth‐Promoting Bacteria

Duflos,

Vailleau,

Roux

2024

Advanced Genetics

View full text Add to dashboard Cite

The social movement to reduce reliance on pesticides and synthesized fertilizers and the growing global demand for sustainable food supplies require the development of eco‐friendly and sustainable agricultural practices. In line, plant growth‐promoting bacteria (PGPB) can participate in creating innovative agroecological systems. While the effectiveness of PGPB is highly influenced by abiotic conditions and microbe–microbe interactions, beneficial plant–PGPB interactions can also highly depend on both host and PGPB genotype. Here, the state of the art on the extent of natural genetic variation of plant–PGPB interactions and the underlying genetic architecture, in particular in Arabidopsis thaliana is reviewed. Extensive natural plant genetic variation in response to PGPB is associated with a polygenic architecture and genetic pathways rarely mentioned as being involved in the response to PGPB. To date, natural genetic variation within PGPB is little explored, which may in turn allow the identification of new genetic pathways underlying benefits to plants. Accordingly, several avenues to better understand the genomic and molecular landscape of plant–PGPB interactions are introduced. Finally, the need for establishing thorough functional studies of candidate genes underlying Quantitative Trait Loci and estimating the extent of genotype‐by‐genotype‐by‐environment interactions within the context of realistic (agro‐)ecological conditions is advocated.

show abstract

Genetic architecture of the response of Arabidopsis thaliana to a native plant-growth-promoting bacterial strain

Ramírez-Sánchez,

Gibelin-Viala,

Roux

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

By improving plant nutrition and alleviating abiotic and biotic stresses, plant growth-promoting bacteria (PGPB) can help to develop eco-friendly and sustainable agricultural practices. Besides climatic conditions, soil conditions, and microbe-microbe interactions, the host genotype influences the effectiveness of PGPB. Yet, most GWAS conducted to characterize the genetic architecture of response to PGPB are based on non-native interactions between a host plant and PGPB strains isolated from the belowground compartment of other plants. In this study, a GWAS was set up under in vitro conditions to describe the genetic architecture of the response of Arabidopsis thaliana to the PGPB Pseudomonas siliginis, by inoculating seeds of 162 natural accessions from the southwest of France with one strain isolated from the leaf compartment in the same geographical region. Strong genetic variation of plant growth response to this native PGPB was observed at a regional scale, with the strain having a positive effect on the vegetative growth of small plants and a negative effect on the vegetative growth of large plants. The polygenic genetic architecture underlying this negative trade-off showed suggestive signatures of local adaptation. The main eco-evolutionary relevant candidate genes are involved in seed and root development.

show abstract

Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

Cited by 3 publications

References 72 publications

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

Toward Ecologically Relevant Genetics of Interactions Between Host Plants and Plant Growth‐Promoting Bacteria

Genetic architecture of the response of Arabidopsis thaliana to a native plant-growth-promoting bacterial strain

Contact Info

Product

Resources

About