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

dc.contributor.authorYassue, Rafael Massahiroen
dc.contributor.authorGalli, Giovannien
dc.contributor.authorChen, Chun-Peng Jamesen
dc.contributor.authorFritsche-Neto, Robertoen
dc.contributor.authorMorota, Gotaen
dc.date.accessioned2023-09-22T17:08:27Zen
dc.date.available2023-09-22T17:08:27Zen
dc.date.issued2023-04en
dc.description.abstractPlant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growth-related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/pld3.492en
dc.identifier.eissn2475-4455en
dc.identifier.issue4en
dc.identifier.othere492en
dc.identifier.pmid37102161en
dc.identifier.urihttp://hdl.handle.net/10919/116317en
dc.identifier.volume7en
dc.language.isoenen
dc.publisherWileyen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgenome-wide association analysisen
dc.subjectgrowth traiten
dc.subjecthyperspectral wavelengthen
dc.subjectmultiphenotypeen
dc.titleGenome-wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth-related traits in maize under plant growth-promoting bacteria inoculationen
dc.title.serialPlant Directen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
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