With 1 figure and 2 tables Abstract In vivo doubled haploid (DH) technology provides a means of creating new maize inbred lines relatively quickly; however, productivity is limited by false‐positive (FP) plants for haploidy and for dihaploidy, which consume resources of space and labour until detected. This work examines the potential for using stomata guard cell length measurement as a means for early detection of FP plants. We found that the true haploid and DH plants could be differentiated from FP and untreated diploid controls as early as Leaf 2 stage by stomata guard cell length measurement. Furthermore, DH plants were distinguishable from haploid and other diploid plants by the Leaf 7 growth stage. Results suggest that, when used together with screening through the anthocyanin colour marker system and flower fertility, stomata guard cell measurement is an easy, non‐destructive, early screening method that may lead to a greater efficiency in DH production systems and optimization of resource allocation for space and labour.

Pericarp thickness and ear traits are important selection criteria for breeding fresh market waxy corn. This research was conducted to better understand genetic control of these traits in popular South Korean germplasm now grown in Illinois. Pericarp thickness on five kernel regions, and ten inflorescence architecture traits were measured on ears from 264 F 2:3 families from a cross between Korean inbreds BH20 and BH30. All five pericarp thickness traits showed high heritabilities and were highly correlated. Multivariate principal components analysis (PCA) revealed that just one principal component (PC) explained most of the total phenotypic variation. A number of univariate quantitative trait loci (QTL) were detected and most were associated with more than one kernel pericarp region. Four out of seven PC-QTL were located in chromosome positions where three or more pericarp thickness univariate QTL were detected. Conversely, three PC-QTL were found in regions with just a single or two univariate QTL, indicating that these QTL regions may be more important for overall pericarp thickness than suggested by univariate analysis. The PCA, QTL, and PC-QTL results indicate that pericarp thickness on different kernel regions may be controlled by common genes with pleiotropic effects. Additive effects of QTL for thinner pericarp thickness came from both BH20 and BH30. For ear architecture traits, heritability varied from 0.38 to 0.72, and several traits were correlated. The PCA reduced these traits into three independent PCs, and all substantial component traits for these PCs were also significantly correlated. A number of univariate QTL were clustered closely, and some PC-QTL were detected in these regions. Some PC-QTL were found in chromosome regions where univariate QTL were not detected, again suggesting that these regions may have larger overall effects on ear architecture than suggested by univariate analyses. Collectively, these QTL may be useful for marker assisted introgression into germplasm more adapted to the U.S.

BACKGROUNDNicosulfuron, a sulfonylurea herbicide widely used for grass weed control in corn production, injures some sweet corn hybrids and inbreds. A specific cytochrome P450 (P450), CYP81A9, is suggested to be responsible for sensitivity to nicosulfuron and other P450‐metabolized herbicides. Corn CYP81A9 enzymes were expressed in E. coli and investigated to find the factor(s) associated with their function and variation in metabolizing nicosulfuron.RESULTRecombinant expressed CYP81A9s from tolerant sweet corn inbreds produced an active form of P450, while CYP81A9 from a sensitive inbred produced an inactive form. Nicosulfuron bound to tolerant CYP81A9s, and produced reverse‐type I ligand, while sensitive CYP81A9 showed no interaction with nicosulfuron. Investigation of 106 sweet corn inbreds showed variation in nicosulfuron injury. A survey of sweet corn CYP81A9 sequences showed mutations in codons for amino acids at 269, 284, 375, and 477 occurred in sweet corn inbreds with complete loss of P450 function (with mean injury >91%) and amino acid changes at 208 and 472 occurred in inbreds with moderate and complete loss of P450 function (with mean injury >14%).CONCLUSIONOur results support that CYP81A9 enzyme is responsible for metabolizing nicosulfuron in sweet corn, and different types of amino acid changes in CYP81A9 sequence are associated with variation in nicosulfuron injury. Therefore, a careful selection of the tolerant allele will be critical for improving tolerance to nicosulfuron and several other P450‐metabolized herbicides. © 2020 Society of Chemical Industry

Tolerance to crowding stress has played a crucial role in improving agronomic productivity in field corn; however, commercial sweet corn hybrids vary greatly in crowding stress tolerance. The objectives were to 1) explore transcriptional changes among sweet corn hybrids with differential yield under crowding stress, 2) identify relationships between phenotypic responses and gene expression patterns, and 3) identify groups of genes associated with yield and crowding stress tolerance. Under conditions of crowding stress, three high-yielding and three low-yielding sweet corn hybrids were grouped for transcriptional and phenotypic analyses. Transcriptional analyses identified from 372 to 859 common differentially expressed genes (DEGs) for each hybrid. Large gene expression pattern variation among hybrids and only 26 common DEGs across all hybrid comparisons were identified, suggesting each hybrid has a unique response to crowding stress. Over-represented biological functions of DEGs also differed among hybrids. Strong correlation was observed between: 1) modules with up-regulation in high-yielding hybrids and yield traits, and 2) modules with up-regulation in low-yielding hybrids and plant/ear traits. Modules linked with yield traits may be important crowding stress response mechanisms influencing crop yield. Functional analysis of the modules and common DEGs identified candidate crowding stress tolerant processes in photosynthesis, glycolysis, cell wall, carbohydrate/nitrogen metabolic process, chromatin, and transcription regulation. Moreover, these biological functions were greatly inter-connected, indicating the importance of improving the mechanisms as a network.