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Premature senescence in annual crops reduces yield, while delayed senescence, termed stay-green, imposes positive and negative impacts on yield and nutrition quality. Despite its importance, scant information is available on the genetic architecture of senescence in maize (Zea mays) and other cereals. We combined a systematic characterization of natural diversity for senescence in maize and coexpression networks derived from transcriptome analysis of normally senescing and stay-green lines. Sixty-four candidate genes were identified by genome-wide association study (GWAS), and 14 of these genes are supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport and signaling, and sink activity. Eight of the GWAS candidates, independently supported by a coexpression network underlying stay-green, include a trehalose-6-phosphate synthase, a NAC transcription factor, and two xylan biosynthetic enzymes. Source-sink communication and the activity of cell walls as a secondary sink emerge as key determinants of staygreen. Mutant analysis supports the role of a candidate encoding Cys protease in stay-green in Arabidopsis (Arabidopsis thaliana), and analysis of natural alleles suggests a similar role in maize. This study provides a foundation for enhanced understanding and manipulation of senescence for increasing carbon yield, nutritional quality, and stress tolerance of maize and other cereals.

Fusarium head blight (FHB) is one of the most economically destructive diseases of wheat (Triticum aestivum L.), causing substantial yield and quality loss worldwide. Fusarium graminearum is the predominant causal pathogen of FHB in the U.S., and produces deoxynivalenol (DON), a mycotoxin that accumulates in the grain throughout infection. FHB results in kernel damage, a visual symptom that is quantified by a human observer enumerating or estimating the percentage of Fusarium-damaged kernels (FDK) in a sample of grain. To date, FDK estimation is the most efficient and accurate method of predicting DON content without measuring presence in a laboratory. For this experiment, 1266 entries collectively representing elite varieties and SunGrains advanced breeding lines encompassing four inoculated FHB nurseries were represented in the analysis. All plots were subjected to a manual FDK count, both exact and estimated, near-infrared spectroscopy (NIR) analysis, DON laboratory analysis, and digital imaging seed phenotyping using the Vibe QM3 instrument developed by Vibe imaging analytics. Among the FDK analytical platforms used to establish percentage FDK within grain samples, Vibe QM3 showed the strongest prediction capabilities of DON content in experimental samples, R2 = 0.63, and higher yet when deployed as FDK GEBVs, R2 = 0.76. Additionally, Vibe QM3 was shown to detect a significant SNP association at locus S3B_9439629 within major FHB resistance quantitative trait locus (QTL) Fhb1. Visual estimates of FDK showed higher prediction capabilities of DON content in grain subsamples than previously expected when deployed as GEBVs (R2 = 0.71), and the highest accuracy in genomic prediction, followed by Vibe QM3 digital imaging, with average Pearson’s correlations of r = 0.594 and r = 0.588 between observed and predicted values, respectively. These results demonstrate that seed phenotyping using traditional or automated platforms to determine FDK boast various throughput and efficacy that must be weighed appropriately when determining application in breeding programs to screen for and develop resistance to FHB and DON accumulation in wheat germplasms.

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Grain mold is a major concern in sorghum [Sorghum bicolor (L.) Moench] production systems, threatening grain quality, safety, and nutritional value as both human food and livestock feed. The crop’s nutritional value, environmental resilience, and economic promise poise sorghum for increased acreage, especially in light of the growing pressures of climate change on global food systems. In order to fully take advantage of this potential, sorghum improvement efforts and production systems must be proactive in managing the sorghum grain mold disease complex, which not only jeopardizes agricultural productivity and profitability, but is also the culprit of harmful mycotoxins that warrant substantial public health concern. The robust scholarly literature from the 1980s to the early 2000s yielded valuable insights and key comprehensive reviews of the grain mold disease complex. Nevertheless, there remains a substantial gap in understanding the complex multi-organismal dynamics that underpin the plant-pathogen interactions involved – a gap that must be filled in order to deliver improved germplasm that is not only capable of withstanding the pressures of climate change, but also wields robust resistance to disease and mycotoxin accumulation. The present review seeks to provide an updated perspective of the sorghum grain mold disease complex, bolstered by recent advances in the understanding of the genetic and the biochemical interactions among the fungal pathogens, their corresponding mycotoxins, and the sorghum host. Critical components of the sorghum grain mold disease complex are summarized in narrative format to consolidate a collection of important concepts: (1) the current state of sorghum grain mold in research and production systems; (2) overview of the individual pathogens that contribute to the grain mold complex; (3) the mycotoxin-producing potential of these pathogens on sorghum and other substrates; and (4) a systems biology approach to the understanding of host responses.

Hybrid breeding in sorghum [Sorghum bicolor (L.) Moench] utilizes the cytoplasmic nuclear male sterility (CMS) system for seed production and subsequently harnesses heterosis. Since the cost of developing and evaluating inbred and hybrid lines in the CMS system is costly and time consuming, genomic prediction of parental lines and hybrids based on genetic data would reduce breeding cycle length and increase probability of recovering exceptional product genotype. We generated 602 hybrids by crossing two female (A) lines with 301 diverse and elite male (R) lines from the sorghum association panel and collected phenotypic data for agronomic traits over two years. We genotyped the inbred parents using whole genome resequencing and used 2,687,342 high quality (minor allele frequency > 2%) single nucleotide polymorphisms for genomic prediction. For grain yield, the experimental hybrids exhibited an average mid-parent heterosis of 40%. Genomic best linear unbiased prediction (GBLUP) for hybrid performance yielded an average prediction accuracy of 0.76 to 0.93 under the prediction scenario where both parental lines in validation sets were included in the training sets (T2). However, when only female tester was shared between training and validation sets (T1F), prediction accuracies declined by 12% to 90%, with plant height showing the greatest decline. Mean accuracies for predicting general combining ability (GCA) of male parents ranged from 0.33 to 0.62 for all traits. Our results showed hybrid performance for agronomic traits can be predicted with high accuracy, and optimizing genomic relationship is essential for optimal training population design for genomic selection in sorghum breeding.