Background: GRAS proteins are crucial transcription factors, which are plant-specific and participate in various plant biological processes. Thanks to the rapid progress of the whole genome sequencing technologies, the GRAS gene families in different plants have been broadly explored and studied. However, comprehensive research on the soybean (Glycine max) GRAS gene family is relatively lagging. Results: In this study, 117 Glycine max GRAS genes (GmGRAS) were identified. Further phylogenetic analyses showed that the GmGRAS genes could be categorized into nine gene subfamilies: DELLA, HAM, LAS, LISCL, PAT1, SCL3, SCL4/7, SCR and SHR. Gene structure analyses turned out that the GmGRAS genes lacked introns and were relatively conserved. Conserved domains and motif patterns of the GmGRAS members in the same subfamily or clade exhibited similarities. Notably, the expansion of the GmGRAS gene family was driven both by gene tandem and segmental duplication events. Whereas, segmental duplications took the major role in generating new GmGRAS genes. Moreover, the synteny and evolutionary constraints analyses of the GRAS proteins among soybean and distinct species (two monocots and four dicots) provided more detailed evidence for GmGRAS gene evolution. Cis-element analyses indicated that the GmGRAS genes may be responsive to diverse environmental stresses and regulate distinct biological processes. Besides, the expression patterns of the GmGRAS genes were varied in various tissues, during saline and dehydration stresses and during seed germination processes. Conclusions: We conducted a systematic investigation of the GRAS genes in soybean, which may be valuable in paving the way for future GmGRAS gene studies and soybean breeding.
Background The different leaf type associated traits of soybean ( Glycine max L.) including leaf area, leaf length, leaf width, leaf shape and petiole length are considered to be associated with seed yield. In order to identify quantitative trait loci (QTLs) affecting leaf type traits, two advanced recombinant inbred line (RIL, ZH, Zhonghuang 24 × Huaxia 3; GB, Guizao 1 × Brazil 13) populations were introduced to score phenotypic values in plants across nine different environments (years, seasons, locations and soybean growth stages). Two restriction site-associated DNA sequencing (RAD-seq) based high-density genetic linkage maps with an average distance of 1.00 centimorgan (cM) between adjacent bin markers were utilized for QTL fine mapping. Results Correlation analysis showed that most of the traits were correlated with each other and regulated both by hereditary and environmental factors. A total of 190 QTLs were identified for leaf type associated traits in the two populations, of which 14 loci were found to be environmentally stable. Moreover, these detected QTLs were categorized into 34 QTL hotspots, and four important QTL hotspots with phenotypic variance ranging from 3.89–23.13% were highlighted. Furthermore, Glyma04g05840 , Glyma19g37820 , Glyma14g07140 and Glyma19g39340 were predicted in the intervals of the stable loci and important QTL hotspots for leaf type traits by adopting Gene Ontology (GO) enrichment analysis. Conclusions Our findings of the QTLs and the putative genes will be beneficial to gain new insights into the genetic basis for soybean leaf type traits and may further accelerate the breeding process for reasonable leaf type soybean. Electronic supplementary material The online version of this article (10.1186/s12864-019-5610-8) contains supplementary material, which is available to authorized users.
Background Different soybean (Glycine max L.) leaf chlorophyll-content traits are considered to be significantly linked to soybean yield. To map the quantitative trait loci (QTLs) of soybean leaf chlorophyll-content traits, an advanced recombinant inbred line (RIL, ZH, Zhonghuang 24 × Huaxia 3) population was adopted to phenotypic data acquisitions for the target traits across six distinct environments (seasons and soybean growth stages). Moreover, the restriction site-associated DNA sequencing (RAD-seq) based high-density genetic linkage map of the RIL population was utilized for QTL mapping by carrying out the composite interval mapping (CIM) approach. Results Correlation analyses showed that most traits were correlated with each other under specific chlorophyll assessing method and were regulated both by hereditary and environmental factors. In this study, 78 QTLs for soybean leaf chlorophyll-content traits were identified. Furthermore, 13 major QTLs and five important QTL hotspots were classified and highlighted from the detected QTLs. Finally, Glyma01g15506, Glyma02g08910, Glyma02g11110, Glyma07g15960, Glyma15g19670 and Glyma15g19810 were predicted from the genetic intervals of the major QTLs and important QTL hotspots. Conclusions The detected QTLs and candidate genes may facilitate to gain a better understanding of the hereditary basis of soybean leaf chlorophyll-content traits and may be valuable to pave the way for the marker-assisted selection (MAS) breeding of the target traits.
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