Environmental Stability and Correlation of Soybean Seed Starch with Protein and Oil Contents

Abstract

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT Seed starch content (SSC) is a decisive factor influencing soy food quality. Variation in SSC affects the composition of major components, oil, and protein in soybean seeds. Therefore, understanding G × E interaction of SSC is important to produce soybeans with stable SSC. In the present study, G × E interactions of 17 soybean genotypes having different SSC (0.24-1.48%) and correlation of SSC with crude protein (CP) and crude fat (CF) were investigated. The genotypes were evaluated for SSC and other traits at two planting dates across three locations over two years (2015 and 2016). The genotype × year, genotype × location, and genotype × year × location interactions were found to be significant (P ≤ 0.001) for SSC, CP, and CF. The average SSC content was found to be higher in 2015 than in 2016. Late planted soybeans contained higher SSC than the early planting soybeans. The SSC was negatively affected by the average daily mean and minimum temperatures and cloudiness during the pod-filling stage. Based on the mean rank, IT189276 (1.39%) was observed to be the most stable genotype among the high starch containing soybeans. Significant (P ≤ 0.0001) negative correlations were found between SSC and CP as well as CP and CF contents. However, a significant (P ≤ 0.05) positive correlation was observed between SSC and CF content. Results of this study showed that SSC affects the seed protein and oil contents and is significantly influenced by the growing environments.

Genome-wide association study of seed protein, oil and amino acid contents in soybean from maturity groups I to IV

“…Such variations in growing conditions among accessions can produce unreliable phenotypic data with relatively high residual values, especially important for quantitative traits sensitive to growing conditions (Specht et al 2001 ). For example, soybean seed starch content was negatively influenced by the daily mean and minimum temperatures (Dhungana et al 2017 ). Both starch and sucrose contents were negatively correlated with protein content but positively correlated with oil content (Li et al 2012 ; Dhungana et al 2017 ).…”

“…For example, soybean seed starch content was negatively influenced by the daily mean and minimum temperatures (Dhungana et al 2017 ). Both starch and sucrose contents were negatively correlated with protein content but positively correlated with oil content (Li et al 2012 ; Dhungana et al 2017 ). Also, high levels of free amino acids were accumulated in soybean null mutant for storage proteins (Takahashi et al 2003 ).…”

Dynamic Transcriptome Changes Related to Oil Accumulation in Developing Soybean Seeds

“…In addition to oil content, many other traits such as the content of protein, isoflavones, starch, and tocopherol in seeds also change during seed development. Previous studies showed that soybean seed oil content has a strong negative correlation ( r = −0.75; p < 0.0001) with seed protein content [47], negative correlation ( r = −0.427; p < 0.01) with isoflavones content [48], positive correlation ( r = 0.1295; p < 0.05) with starch content [49], negative correlation ( r = −0.371; p < 0.01) with alpha-tocopherol and negative correlation ( r = −0.391; p < 0.01) with beta-tocopherol [50]. In addition, some pleiotropic loci have also been identified [47,51,52].…”

Assessment of Phenotypic Variations and Correlation among Seed Composition Traits in Mutagenized Soybean Populations

“…To the best of our knowledge, the majority of the correlation studies performed on the soybean seed composition traits were conducted based on the Pearson correlation coefficient estimations [49][50][51]. However, the normal distribution of two variables is an important prerequisite for using this method.…”