Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

KEY MESSAGE: Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high-throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman's rank correlation coefficient of 0.852 between wet laboratory and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR spectra data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait.

The Tobacco Trichome Exudate Z-abienol and Its Relationship With Plant Resistance to Phytophthora nicotianae.

In previous research, we discovered a favorable quantitative trait locus (QTL) in cigar tobacco cultivar 'Beinhart 1000' designated as Phn15.1, which provides a high level of partial resistance to the black shank disease caused by Phytophthora nicotianae. A very close genetic association was also found between Phn15.1 and the ability to biosynthesize Z-abienol, a labdanoid diterpene exuded by the trichomes onto above-ground plant parts, and that imparts flavor and aroma characteristics to Oriental and some cigar tobacco types. Because accumulation of Z-abienol is considered to be undesirable for cultivars of other tobacco types, we herein describe a series of experiments to gain insight on whether this close association is due to genetic linkage or pleiotropy. First, in an in vitro bioassay, we observed Z-abienol and related diterpenes to inhibit hyphal growth of P. nicotianae at concentrations between 0.01 and 100 ppm. Secondly, we field-tested transgenic versions of Beinhart 1000 carrying RNAi constructs for downregulating NtCPS2 or NtABS, two genes involved in the biosynthesis of Z-abienol. Thirdly, we also field tested a recombinant inbred line population segregating for a truncation mutation in NtCPS2 leading to an interrupted Z-abienol pathway. We observed no correlation between field resistance to P. nicotianae and the ability to accumulate Z-abienol in either the transgenic materials or the mapping population. Results suggest that, although Z-abienol may affect P. nicotianae when applied at high concentrations in in vitro assays, the compound has little effect on black shank disease development under natural field conditions. Thus, it should be possible to disassociate Phn15.1-mediated black shank resistance identified in cigar tobacco cultivar Beinhart 1000 from the ability to accumulate Z-abienol, an undesirable secondary metabolite for burley and flue-cured tobacco cultivars.

GrainGenes: a data-rich repository for small grains genetics and genomics.

As one of the US Department of Agriculture-Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov.

Genetic control of kernel compositional variation in a maize diversity panel.

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g., carbohydrates, protein, and starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g., dent, sweetcorn, and popcorn) were explored, and significant differences and meaningful correlations were detected. In total, 22.9-71.0% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole-genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 72 statistically significant SNPs for 11 compositional traits. This study provides valuable insights in the phenotypic variation and genetic control underlying compositional traits that can be used in breeding programs for improving maize grain quality.