Mutagenized populations have provided important materials for introducing variation and identifying gene function in plants. In this study, an ethyl methanesulfonate (EMS)‐induced soybean (Glycine max) population, consisting of 21,600 independent M2 lines, was developed. Over 1,000 M4 (5) families, with diverse abnormal phenotypes for seed composition, seed shape, plant morphology and maturity that are stably expressed across different environments and generations were identified. Phenotypic analysis of the population led to the identification of a yellow pigmentation mutant, gyl, that displayed significantly decreased chlorophyll (Chl) content and abnormal chloroplast development. Sequence analysis showed that gyl is allelic to MinnGold, where a different single nucleotide polymorphism variation in the Mg‐chelatase subunit gene (ChlI1a) results in golden yellow leaves. A cleaved amplified polymorphic sequence marker was developed and may be applied to marker‐assisted selection for the golden yellow phenotype in soybean breeding. We show that the newly developed soybean EMS mutant population has potential for functional genomics research and genetic improvement in soybean.
Abstract:The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the R 2 d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R 2 d . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.
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