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

Abstract

Plant 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 386-1,021 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 associated with manually measured and hyperspectral phenotypes. 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 multi-phenotype 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.

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

“…These GWAS revealed a highly polygenic architecture of response to PGPB, with the identification of multiple Quantitative Trait Loci (QTLs) with small effects. The fine mapping of these QTLs revealed candidate genes involved in plant immunity (Kamfwa et al, 2015;Vidotti et al, 2019;Yassue et al, 2023), hormonal pathways (Vidotti et al, 2019;Cotta et al, 2020;Plucani do Amaral et al, 2023), nutrient uptake and provision (Stanton-Geddes et al, 2013;Curtin et al, 2017;Torkamaneh et al, 2020;Plucani do Amaral et al, 2023;Yassue et al, 2023) and plant development (Wintermans et al, 2016;Cotta et al, 2020), which is in line with the main pathways identified by analysis of mutants affecting microbiota structure in plants (Bergelson et al, 2021).…”

“…This requires the identification of host genetic factors either by using artificial genetic variation or by exploiting natural genetic variation (Bergelson et al, 2021). The latter approach was adopted by setting up genome-wide association studies (GWAS) on diverse plants including the model plants Arabidopsis thaliana (Wintermans et al, 2016;Cotta et al, 2020;Plucani do Amaral et al, 2023) and Medicago truncatula (Stanton-Geddes et al, 2013) as well as diverse crops such as maize (Vidotti et al, 2019;Yassue et al, 2021;Yassue et al, 2023), soybean (Torkamaneh et al, 2020) and common bean (Kamfwa et al, 2015). These GWAS revealed a highly polygenic architecture of response to PGPB, with the identification of multiple Quantitative Trait Loci (QTLs) with small effects.…”