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Seed protein content, controlled by quantitative genes, is a very important trait for soybean. To idenlify the soybean quantitative trait loci (QTL) of seed protein content, which provides a base for MAS breeding and elite genes - discovering, a cross was made between the elite cultivar Zhonghuang 13, which exhibits a comprehensive excellent performance and has the higher protein content and the variety Dongshan 69 which has the lower protein content. The BC2F2 and BC2F3 backcross populations were further developed to identify protein composite interval mapping (ICIM), two QTLs flanked by the marker interval Satt396 to Satt180 and Sct_001 to Satt654 were both detecled in the BC2F2 and BC2F3 families. In the BC2F2 families, 3 QTLs were found on A1, C1 and J linkage groups, which accounted for 10.00%, 7.07% and 9.23% phenotypic variance, respectively. In the BC2F3 families, 4 QTLs were identified on B1, C1, I and J linkage groups, which explained 17. 57%, 3. 46%, 8. 53% and 11. 80% phenotypic variance, respectively. Region Sct_001 to Satt654 may be a new interval associated wilh protein content QTL. [ABSTRACT FROM AUTHOR]

Current breeding programs dealing with fatty acid (FA) concentrations in soybean [Glycine max (L. ) Merr.] require large numbers for gas chromatographic analyses, thus it is important to develop a method for rapid determination of fatty acid by Near-Infrared Reflectance spectroscopy (NIRS) in soybeans. The objective of this work was to study the potential of fourier-transform near-infrared reflectance spectroscopy (FT-NIRS) to estimate the fatty acid concentrations in Chinese soybean varieties. One hundred and eight of soybean cultivars or lines (the calibration set: 64; the external validation set: 44) were scanned within 4000-12500 cm(-1) of wavenumbers using a standard sample cup by NIRS machinery, and analyzed the fatty acids by gas chromatograph (GC) methods. Equations were developed using partial least squares (PLS) regression and cross validation for multivariate calibration in this study. The optimal spectral region was selected from 6101.9 to 5446.5 cm(-1) based on the OPUS 5.0 software. Cross validation results showed that major FA components such as oleic acid (R2(CV) = 0.94), linoleic acid (R2(CV) = 0.87), linolenic acid (R2(CV) = 0.85), and total saturates (R2(CV) = 0.88) were accurately determined by the proposed equations as compared with the reference data obtained by the GC method. External validation results also demonstrated that equation for oleic acid had the highest predictive ability R(2)val = 0.91), root mean square error of predication (RMSEP) value was 2.47 g x kg(-1) dry weight, the ratios of RMSEP to the standard deviation (SD) was 0.29, which was usable for quality assurance application. Moreover, equations for palmitic acid, stearic acid, linoleic acid, linolenic acid, and total saturates were predicted with the determination coefficients ranging from 0.66 to 0.76, RMSEP values from 0.37 to 2.74 g x kg(-1) dry weight, and RMSEP/SD values from 0.47 to 0.53, which could be used for sample screening. Therefore, we confirmed that a reliable estimation of the major fatty acid components is possible by using NIRS technique in soybeans.