QTL fine-mapping of soybean (Glycine max L.) leaf type associated traits in two RILs populations

Abstract

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.

Genome-wide identification and characterization of GRAS genes in soybean (Glycine max)

“…Soybean ( Glycine max L.) is one of the major crops abundant in high-quality protein and oil, which also contains various nutrients such as lecithins and isoflavones [ 27 ]. Many soybean transcription factor families like WKRY [ 28 ], MYB [ 29 ], NAC [ 30 ], HD-Zip [ 31 ], ARF [ 32 ] and MADS [ 33 ] have been investigated and studied.…”

High-density QTL mapping of leaf-related traits and chlorophyll content in three soybean RIL populations

“…For example, the major QTL, q20, was co-localized to previously identified loci related to LL, LW, LA, leaf shape and seed set [14], plant height [31], and branch number [32], water use efficiency [33], and shoot phosphorus content [34], indicating the presence of important genes in this region may be involved in regulating soybean plant architecture and even yield. More importantly, we also found that q20 was colocalized with Ln, which is a key regulator of leaflet shape in soybean [19]. To further analyze the relationship between Ln gene and the leaf related traits in our study, we conducted a partial single marker analysis at the Ln locus by using investigating the association of all the molecular markers (45 SNPs) distributed in the range of within 1 Mb upstream and downstream of Ln locus with the leaf related traits, including leaf width (LW), leaf length (LL), and leaf area (LA) and chlorophyll content (CC).…”

“…Therefore, the genetic basis of leaf-related traits and CC is still incomplete, especially the genetic relationship between these traits is surprisingly understudied. Most previous studies have focused on discrete analysis of individual traits in a single mapping population, and were limited in their ability to provide a comprehensive analysis for the genetic structure of complex quantitative traits [18,19]. Another constraint may be that only a part of the genetic structure of traits could be revealed by using the single biparental mapping populations, and prevent the excavation of specific favorable alleles [20,21].…”

“…As an important factor affecting phenotype, QTL × environment interaction may explain one of the reasons why QTLs can not be identified stably in different environments [26,27]. Previously, many studies have shown complex quantitative traits were controlled by both genetic and environmental factors in soybean [19,28,29]. In this study, there were significant differences in phenotypic values across genotypes, years and populations, suggesting that the leaf-related traits and CC are both influenced by the underlying genes, the environment, and different hereditary backgrounds (Table S1).…”

QTL Mapping linked to downy mildew resistance genes in maize (Zea mays)

“…Linkage analysis between phenotypic and genotypic data was carried out by Composite Interval Mapping (CIM) method which implemented in the Windows QTLs Cartographer program version 2.5 (Wang et al 2012). CIM analysis was used to perform joint analysis of data across environments to map QTLs and estimate their genetic effects (Wang et al 2019). A series of two models were used in the analysis.…”