Fast neutron radiation has been used as a mutagen to develop extensive mutant collections. However, the genome-wide structural consequences of fast neutron radiation are not well understood. Here, we examine the genome-wide structural variants observed among 264 soybean [Glycine max (L.) Merrill] plants sampled from a large fast neutron-mutagenized population. While deletion rates were similar to previous reports, surprisingly high rates of segmental duplication were also found throughout the genome. Duplication coverage extended across entire chromosomes and often prevailed at chromosome ends. High-throughput resequencing analysis of selected mutants resolved specific chromosomal events, including the rearrangement junctions for a large deletion, a tandem duplication, and a translocation. Genetic mapping associated a large deletion on chromosome 10 with a quantitative change in seed composition for one mutant. A tandem duplication event, located on chromosome 17 in a second mutant, was found to cosegregate with a short petiole mutant phenotype, and thus may serve as an example of a morphological change attributable to a DNA copy number gain. Overall, this study provides insight into the resilience of the soybean genome, the patterns of structural variation resulting from fast neutron mutagenesis, and the utility of fast neutron-irradiated mutants as a source of novel genetic losses and gains.

Mutagenesis is a useful tool in many crop species to induce heritable genetic variability for trait improvement and gene discovery. In this study, forward screening of a soybean fast neutron (FN) mutant population identified an individual that produced seed with nearly twice the amount of sucrose (8.1% on dry matter basis) and less than half the amount of oil (8.5% on dry matter basis) as compared to wild type. Bulked segregant analysis (BSA), comparative genomic hybridization, and genome resequencing were used to associate the seed composition phenotype with a reciprocal translocation between chromosomes 8 and 13. In a backcross population, the translocation perfectly cosegregated with the seed composition phenotype and exhibited non-Mendelian segregation patterns. We hypothesize that the translocation is responsible for the altered seed composition by disrupting a β-ketoacyl-[acyl carrier protein] synthase 1 (KASI) ortholog. KASI is a core fatty acid synthesis enzyme that is involved in the conversion of sucrose into oil in developing seeds. This finding may lead to new research directions for developing soybean cultivars with modified carbohydrate and oil seed composition.

Background Iron deficiency chlorosis (IDC) is an abiotic stress in soybean [Glycine max (L.) Merr.] that causes significant yield reductions. Symptoms of IDC include interveinal chlorosis and stunting of the plant. While there are management practices that can overcome these drastic yield losses, the preferred way to manage IDC is growing tolerant soybean varieties. To develop varieties tolerant to IDC, breeders may easily phenotype up to thousands of candidate soybean lines every year for severity of symptoms related to IDC, a task traditionally done with a 1–5 visual rating scale. The visual rating scale is subjective and, because it is time consuming and laborious, can typically only be accomplished once or twice during a growing season. Results The goal of this study was to use an unmanned aircraft system (UAS) to improve field screening for tolerance to soybean IDC. During the summer of 2017, 3386 plots were visually scored for IDC stress on two different dates. In addition, images were captured with a DJI Inspire 1 platform equipped with a modified dual camera system which simultaneously captures digital red, green, blue images as well as red, green, near infrared (NIR) images. A pipeline was created for image capture, orthomosaic generation, processing, and analysis. Plant and soil classification was achieved using unsupervised classification resulting in 95% overall classification accuracy. Within the plant classified canopy, the green, yellow, and brown plant pixels were classified and used as features for random forest and neural network models. Overall, the random forest and neural network models achieved similar misclassification rates and classification accuracy, which ranged from 68 to 77% across rating dates. All 36 trials in the field were analyzed using a linear model for both visual score and UAS predicted values on both dates. In 32 of the 36 tests on date 1 and 33 of 36 trials on date 2, the LSD associated with UAS image-based IDC scores was lower than the LSD associated with visual scores, indicating the image-based scores provided more precise measurements of IDC severity. Conclusions Overall, the UAS was able to capture differences in IDC stress and may be used for evaluations of candidate breeding lines in a soybean breeding program. This system was both more efficient and precise than traditional scoring methods.

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Estimating the date of maturity of soybean breeding field plots is necessary for breeding line characterization and for informing yield comparisons among varieties. The main drawback of visually dating soybean maturity is the sheer scale of note recording entailed and the frequency at which these notes need to be taken. The overall aim of this study was to build upon prior work in using low-cost UAS-based RGB cameras to estimate soybean maturity date by examining the effect of vegetation index, summary statistic of the pixel values from each region of interest (plot), statistical model, and flight frequency. Maturity dates collected from five environments with 53 experimental trials (4,415 plots) were both visually dated and imaged using a RGB camera carried by a UAS. Using the mean greenness leaf index on each plot combined with LOESS regression, we achieved high correlations between ground and UAS-based estimates (r = 0.84-0.97). Precision, quantified by broad-sense heritability estimates, was greater for UAS-based dates in 29 of 53 field trials, and nearly equivalent in 11 more field trials. We found that 54% of the significant deviations between ground and UAS-based estimates were caused by inaccurate UAS-based estimates, while errors in the ground-based estimates accounted for 46% of the deviations. Reasons for these inaccurate estimates were attributed to lodging, presence of weeds, low germination, and within-line genetic heterogeneity in the plots. A detailed description of the analysis pipeline, a user-friendly R script, and all of the images and ground data have been made publicly available to help other researchers and breeders test and adopt these methods.