High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel.

BACKGROUND: Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS-NIR-SWIR, 400-2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS-NIR-SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS-NIR-SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties. RESULTS: Some VIs were able to estimate CHL and N (R2 > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R2 (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicted satisfactorily (R2 > 0.85 and RPD > 2.6). LWC, SLA and K were predicted moderately well, with R2 ranging from 0.54 to 0.70 and RPD from 1.5 to 1.8. The lowest prediction accuracy was for P, with R2 < 0.5 and RPD < 1.4. CONCLUSION: This study showed that VIS-NIR-SWIR reflectance spectroscopy is a promising tool for low-cost, nondestructive, and high-throughput analysis of a number of leaf physiological and biochemical properties. Full-spectrum based modeling approaches (PLSR and SVR) led to more accurate prediction models compared to VI-based methods. We called for the construction of a leaf VIS-NIR-SWIR spectral library that would greatly benefit the plant phenotyping community for the research of plant leaf traits.

Empirical Comparisons of Different Statistical Models To Identify and Validate Kernel Row Number-Associated Variants from Structured Multi-parent Mapping Populations of Maize.

Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development.

Mapping and Characterization of the fefe Gene That Controls Iron Uptake in Melon (Cucumis melo L.).

Iron (Fe) deficiency in plants limits crop growth and productivity. Molecular mechanisms that plants use to sense and respond to Fe deficiency by coordinated expression of Fe-uptake genes are not fully understood. The C940-fe chlorotic melon (Cucumis melo) mutant known as fefe is unable to upregulate Fe-uptake genes, however, the FeFe gene had not been identified. In this study, we used two F2 mapping populations to map and identify the FeFe gene as bHLH38, a homolog of subgroup Ib bHLH genes from Arabidopsis thaliana that are involved in transcriptional regulation of Fe-uptake genes in partnership with the FIT gene. A Ty1-copia type retrotransposon insertion of 5.056 kb within bHLH38 is responsible for the defect in bHLH38 in fefe, based on sequencing and expression analysis. This retrotransposon insertion results in multiple non-functional transcripts expected to result in an altered and truncated protein sequence. Hairy root transformation of fefe plants using wild-type bHLH38 resulted in functional complementation of the chlorotic fefe phenotype. Using a yeast-2-hybrid assay, the transcription factor Fit interacted with the wild-type bHLH38 protein, but did not interact with the fefe bHLH38 protein, suggesting that heterodimer formation of Fit/bHLH38 to regulate Fe-uptake genes does not occur in fefe roots. The second subgroup Ib bHLH gene in the melon genome is not functionally redundant to bHLH38, in contrast to Arabidopsis where four subgroup Ib bHLH genes are functionally redundant. Whereas the Arabidopsis bHLH transcript levels are upregulated by Fe deficiency, melon bHLH38 was not regulated at the transcript level. Thus, the fefe mutant may provide a platform for studying bHLH38 genes and proteins from other plant species.