Fine Mapping of the SCN Resistance Locus \u3ci\u3erhg1-b\u3c/i\u3e from PI 88788

Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) is the most economically damaging soybean [Glycine max (L.) Merr.] pest in the USA and genetic resistance is a key component for its control. Although SCN resistance is quantitative, the rhg1 locus on chromosome 18 (formerly known as Linkage Group G) confers a high level of resistance. The objective of this study was to fi ne-map the rhg1-b allele that is derived from plant introduction (PI) 88788. F2 and F3 plants and F3:4 lines from crosses between SCN resistant and susceptible genotypes were tested with genetic markers to identify recombination events close to rhg1-b. Lines developed from these recombinant plants were then tested for resistance to the SCN isolate PA3, which originally had an HG type 0 phenotype, and with genetic markers. Analysis of lines carrying key recombination events positioned rhg1-b between the simple sequence repeat (SSR) markers BARCSOYSSR_18_0090 and BARCSOYSSR_18_0094. This places rhg1-b to a 67-kb region of the ‘Williams 82’ genome sequence. The receptor-like kinase gene that has been previously identified as a candidate for the ‘Peking’-derived SCN resistant rhg1 gene is adjacent to, but outside of, the rhg1-b interval defined in the present study

Genotype by environment effects on promiscuous nodulation in soybean (Glycine max L. Merrill)

Open Access Journal; Published:17 March 2017Background Understanding factors influencing the expression of a trait is key in designing a breeding program. Genotype by environment interaction has great influence on most quantitative traits. Promiscuous nodulation is a trait of importance for soybean production in Africa, because of the soil bacteria Bradyrhizobium japonicum not being indigenous in most African soils. Most soybean cultivars require B. japonicum for nodulation leading to the need for seed inoculation before sowing soybean in Africa. Few cultivars have capability to nodulate with Bradyrhizobia spp. that are different from B. japonicum and native in African soils. Such cultivars are termed “promiscuous cultivars.” Field experiments were conducted in six locations in Uganda for two seasons, to investigate the extent of environmental influences on the nodulation ability of promiscuous soybean genotypes. Results Additive main effect and multiplicative interaction effects showed highly significant environment and genotype by environment (G × E) interaction effects on all nodulation traits. G × E interaction contributed more to the total variation than genotypes. The genotypes Kabanyolo I and WonderSoya were the most stable for nodules’ dry weight (NDW), which is the nodulation trait the most correlated with grain yield. Genotype UG5 was the most stable for nodules’ number (NN), and Nam II for nodules’ effectiveness (NE). The genotype NamSoy 4M had the highest performance for NN, NFW, and NDW, but was less stable. WonderSoya had the highest NE. Genotype and genotype by environment analysis grouped environments into mega-environments (MEs), and four MEs were observed for NDW, with NamSoy 4M the winning genotype in the largest ME, and Kasese B the ideal environment for that nodulation trait. Conclusion This study provides information that can guide breeding strategies. The low genetic effect that led to high environmental and G × E interaction effects raised the need for multi-environments testing before cultivar selection and recommendation. The study revealed genotypes that are stable and others that are high performing for nodulation traits, and which can be used as parental lines in breeding programs

A nematode demographics assay in transgenic roots reveals no significant impacts of the Rhg1 locus LRR-Kinase on soybean cyst nematode resistance

<p>Abstract</p> <p>Background</p> <p>Soybean cyst nematode (<it>Heterodera glycines</it>, SCN) is the most economically damaging pathogen of soybean (<it>Glycine max</it>) in the U.S. The <it>Rhg1 </it>locus is repeatedly observed as the quantitative trait locus with the greatest impact on SCN resistance. The Glyma18g02680.1 gene at the <it>Rhg1 </it>locus that encodes an apparent leucine-rich repeat transmembrane receptor-kinase (LRR-kinase) has been proposed to be the SCN resistance gene, but its function has not been confirmed. Generation of fertile transgenic soybean lines is difficult but methods have been published that test SCN resistance in transgenic roots generated with <it>Agrobacterium rhizogenes</it>.</p> <p>Results</p> <p>We report use of artificial microRNA (amiRNA) for gene silencing in soybean, refinements to transgenic root SCN resistance assays, and functional tests of the <it>Rhg1 </it>locus LRR-kinase gene. A nematode demographics assay monitored infecting nematode populations for their progress through developmental stages two weeks after inoculation, as a metric for SCN resistance. Significant differences were observed between resistant and susceptible control genotypes. Introduction of the <it>Rhg1 </it>locus LRR-kinase gene (genomic promoter/coding region/terminator; Peking/PI 437654-derived SCN-resistant source), into <it>rhg1</it>^-SCN-susceptible plant lines carrying the resistant-source <it>Rhg4</it>^{<it>+ </it>}locus, provided no significant increases in SCN resistance. Use of amiRNA to reduce expression of the LRR-kinase gene from the <it>Rhg1 </it>locus of Fayette (PI 88788 source of <it>Rhg1</it>) also did not detectably alter resistance to SCN. However, silencing of the LRR-kinase gene did have impacts on root development.</p> <p>Conclusion</p> <p>The nematode demographics assay can expedite testing of transgenic roots for SCN resistance. amiRNAs and the pSM103 vector that drives interchangeable amiRNA constructs through a soybean polyubiqutin promoter (Gmubi), with an intron-GFP marker for detection of transgenic roots, may have widespread use in legume biology. Studies in which expression of the <it>Rhg1 </it>locus LRR-kinase gene from different resistance sources was either reduced or complemented did not reveal significant impacts on SCN resistance.</p

Fine mapping and cloning of the major seed protein quantitative trait loci on soybean chromosome 20

Soybean [Glycine max (L.) Merr.] is a unique crop species because it has high levels of both protein and oil in its seed. Of the many quantitative trait loci (QTL) controlling soybean seed protein content, alleles of the cqSeed protein-003 QTL on chromosome 20 exert the greatest additive effect. The high-protein allele exists in both cultivated and wild soybean (Glycine soja Siebold & Zucc.) germplasm. Our objective was to fine map this QTL to enable positional-based cloning of its underlying causative gene(s). Fine mapping was achieved by developing and testing a series of populations in which the chromosomal region surrounding the segregating high- versus low-protein alleles was gradually narrowed, using marker-based detection of recombinant events. The resultant 77.8 kb interval was directly sequenced from a G. soja source and compared with the reference genome to identify structural and sequence polymorphisms. An insertion/deletion variant detected in Glyma.20G85100 was found to have near-perfect +/- concordance with high/low-protein allele genotypes inferred for this QTL in parents of published mapping populations. The indel structure was concordant with an evolutionarily recent insertion of a TIR transposon into the gene in the low-protein lineage. Seed protein was significantly greater in soybean expressing an RNAi hairpin downregulation element in two independent events relative to control null segregant lineages. We conclude that a transposon insertion within the CCT domain protein encoded by the Glyma.20G85100 gene accounts for the high/low seed protein alleles of the cqSeed protein-003 QTL

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately \u3c10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis

A Roadmap for Functional Structural Variants in the Soybean Genome

Gene structural variation (SV) has recently emerged as a key genetic mechanism underlying several important phenotypic traits in crop species. We screened a panel of 41 soybean (Glycine max) accessions serving as parents in a soybean nested association mapping population for deletions and duplications in more than 53,000 gene models. Array hybridization and whole genome resequencing methods were used as complementary technologies to identify SV in 1528 genes, or approximately 2.8%, of the soybean gene models. Although SV occurs throughout the genome, SV enrichment was noted in families of biotic defense response genes. Among accessions, SV was nearly eightfold less frequent for gene models that have retained paralogs since the last whole genome duplication event, compared with genes that have not retained paralogs. Increases in gene copy number, similar to that described at the Rhg1 resistance locus, account for approximately one-fourth of the genic SV events. This assessment of soybean SV occurrence presents a target list of genes potentially responsible for rapidly evolving and/or adaptive traits

Complementary genetic and genomic approaches help characterize the linkage group I seed protein QTL in soybean

Background: The nutritional and economic value of many crops is effectively a function of seed protein and oil content. Insight into the genetic and molecular control mechanisms involved in the deposition of these constituents in the developing seed is needed to guide crop improvement. A quantitative trait locus (QTL) on Linkage Group I (LG I) of soybean (Glycine max (L.) Merrill) has a striking effect on seed protein content. Results: A soybean near-isogenic line (NIL) pair contrasting in seed protein and differing in an introgressed genomic segment containing the LG I protein QTL was used as a resource to demarcate the QTL region and to study variation in transcript abundance in developing seed. The LG I QTL region was delineated to less than 8.4 Mbp of genomic sequence on chromosome 20. Using Affymetrix® Soy GeneChip and high-throughput Illumina® whole transcriptome sequencing platforms, 13 genes displaying significant seed transcript accumulation differences between NILs were identified that mapped to the 8.4 Mbp LG I protein QTL region. Conclusions: This study identifies gene candidates at the LG I protein QTL for potential involvement in the regulation of protein content in the soybean seed. The results demonstrate the power of complementary approaches to characterize contrasting NILs and provide genome-wide transcriptome insight towards understanding seed biology and the soybean genome

A Roadmap for Functional Structural Variants in the Soybean Genome

Gene structural variation (SV) has recently emerged as a key genetic mechanism underlying several important phenotypic traits in crop species. We screened a panel of 41 soybean (Glycine max) accessions serving as parents in a soybean nested association mapping population for deletions and duplications in more than 53,000 gene models. Array hybridization and whole genome resequencing methods were used as complementary technologies to identify SV in 1528 genes, or approximately 2.8%, of the soybean gene models. Although SV occurs throughout the genome, SV enrichment was noted in families of biotic defense response genes. Among accessions, SV was nearly eightfold less frequent for gene models that have retained paralogs since the last whole genome duplication event, compared with genes that have not retained paralogs. Increases in gene copy number, similar to that described at the Rhg1 resistance locus, account for approximately one-fourth of the genic SV events. This assessment of soybean SV occurrence presents a target list of genes potentially responsible for rapidly evolving and/or adaptive traits