Quantifying Variation in Soybean Due to Flood Using a Low-Cost 3D Imaging System.

Flood has an important effect on plant growth by affecting their physiologic and biochemical properties. Soybean is one of the main cultivated crops in the world and the United States is one of the largest soybean producers. However, soybean plant is sensitive to flood stress that may cause slow growth, low yield, small crop production and result in significant economic loss. Therefore, it is critical to develop soybean cultivars that are tolerant to flood. One of the current bottlenecks in developing new crop cultivars is slow and inaccurate plant phenotyping that limits the genetic gain. This study aimed to develop a low-cost 3D imaging system to quantify the variation in the growth and biomass of soybean due to flood at its early growth stages. Two cultivars of soybeans, i.e. flood tolerant and flood sensitive, were planted in plant pots in a controlled greenhouse. A low-cost 3D imaging system was developed to take measurements of plant architecture including plant height, plant canopy width, petiole length, and petiole angle. It was found that the measurement error of the 3D imaging system was 5.8% in length and 5.0% in angle, which was sufficiently accurate and useful in plant phenotyping. Collected data were used to monitor the development of soybean after flood treatment. Dry biomass of soybean plant was measured at the end of the vegetative stage (two months after emergence). Results show that four groups had a significant difference in plant height, plant canopy width, petiole length, and petiole angle. Flood stress at early stages of soybean accelerated the growth of the flood-resistant plants in height and the petiole angle, however, restrained the development in plant canopy width and the petiole length of flood-sensitive plants. The dry biomass of flood-sensitive plants was near two to three times lower than that of resistant plants at the end of the vegetative stage. The results indicate that the developed low-cost 3D imaging system has the potential for accurate measurements in plant architecture and dry biomass that may be used to improve the accuracy of plant phenotyping.

Morphological characterization and assessment of genetic variability, character association, and divergence in soybean mutants.

Genetic diversity is important for crop improvement. An experiment was conducted during 2011 to study genetic variability, character association, and genetic diversity among 27 soybean mutants and four mother genotypes. Analysis of variance revealed significant differences among the mutants and mothers for nine morphological traits. Eighteen mutants performed superiorly to their mothers in respect to seed yield and some morphological traits including yield attributes. Narrow differences between phenotypic and genotypic coefficients of variation (PCV and GCV) for most of the characters revealed less environmental influence on their expression. High values of heritability and genetic advance with high GCV for branch number, plant height, pod number, and seed weight can be considered as favorable attributes for soybean improvement through phenotypic selection and high expected genetic gain can be achieved. Pod and seed number and maturity period appeared to be the first order traits for higher yield and priority should be given in selection due to their strong associations and high magnitudes of direct effects on yield. Cluster analysis grouped 31 genotypes into five groups at the coefficient value of 235. The mutants/genotypes from cluster I and cluster II could be used for hybridization program with the mutants of clusters IV and V in order to develop high yielding mutant-derived soybean varieties for further improvement.

Quartet inference from SNP data under the coalescent model.

MOTIVATION: Increasing attention has been devoted to estimation of species-level phylogenetic relationships under the coalescent model. However, existing methods either use summary statistics (gene trees) to carry out estimation, ignoring an important source of variability in the estimates, or involve computationally intensive Bayesian Markov chain Monte Carlo algorithms that do not scale well to whole-genome datasets. RESULTS: We develop a method to infer relationships among quartets of taxa under the coalescent model using techniques from algebraic statistics. Uncertainty in the estimated relationships is quantified using the nonparametric bootstrap. The performance of our method is assessed with simulated data. We then describe how our method could be used for species tree inference in larger taxon samples, and demonstrate its utility using datasets for Sistrurus rattlesnakes and for soybeans. AVAILABILITY AND IMPLEMENTATION: The method to infer the phylogenetic relationship among quartets is implemented in the software SVDquartets, available at www.stat.osu.edu/∼lkubatko/software/SVDquartets.

Quantitative detection method for Roundup Ready soybean in food using duplex real-time PCR MGB chemistry.

BACKGROUND: Methodologies that enable the detection of genetically modified organisms (GMOs) (authorized and non-authorized) in food and feed strongly influence the potential for adequate updating and implementation of legislation together with labeling requirements. Quantitative polymerase chain reaction (qPCR) systems were designed to boost the sensitivity and specificity on the identification of GMOs in highly degraded DNA samples; however, such testing will become economically difficult to cope with due to increasing numbers of approved genetically modified (GM) lines. Multiplexing approaches are therefore in development to provide cost-efficient solution. RESULTS: Construct-specific primers and probe were developed for quantitative analysis of Roundup Ready soybean (RRS) event glyphosate-tolerant soybean (GTS) 40-3-2. The lectin gene (Le1) was used as a reference gene, and its specificity was verified. RRS- and Le1-specific quantitative real-time PCR (qRTPCR) were optimized in a duplex platform that has been validated with respect to limit of detection (LOD) and limit of quantification (LOQ), as well as accuracy. The analysis of model processed food samples showed that the degradation of DNA has no adverse or little effects on the performance of quantification assay. CONCLUSION: In this study, a duplex qRTPCR using TaqMan minor groove binder-non-fluorescent quencher (MGB-NFQ) chemistry was developed for specific detection and quantification of RRS event GTS 40-3-2 that can be used for practical monitoring in processed food products.

Assessment of the protein quality of nine northern adapted yellow and brown seed coated soybean cultivars by amino acid analysis.

Accurate and detailed amino acid determinations were carried out on nine northern adapted soybean cultivars to ascertain whether their amino acid profiles could be used as potentially useful indices for assessing their protein quality. The cultivars were Maple Amber, Maple Donovan, Maple Glen, Maple Isle, Maple Presto, Maple Ridge, and three brown seed coat near-isogenic lines, Maple Presto Brown, Maple Ridge Brown, and Maple Arrow Brown. Their total protein and amino acid composition were compared with those of an established cultivar, Maple Arrow. Mean protein values for the new cultivars ranged from 30.1 to 33.1% compared to Maple Arrow, which was 33.2%. The total nitrogen content was also variable among these cultivars, ranging from 5.0 to 5.4%. All nine Maple series soybean cultivars were higher in their essential amino acid (EAA) content, that is, EAA(9) = 45.2-46.5%, than the FAO/WHO reference protein pattern value of EAA(9) = 33.9%, for a 2-5-year-old child. Each of the nine new soybean cultivars was limited only in methionine and to a lesser extent in valine and isoleucine and had a protein digestibility corrected amino acid score of 91% for all cultivars, compared to the value of egg protein (97%). These results suggest that the most accurate evaluation of protein quality in soybeans, and possibly other legumes and cereals, is by the protein digestibility-corrected amino acid score.