Tri D. Vuong

SNP discovery by high-throughput sequencing in soybean.

BackgroundWith the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies (Roche GS/454, Illumina GA/Solexa, and ABI/SOLiD), have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology.ResultsA total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp.ConclusionsWe have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops.

Integrating omic approaches for abiotic stress tolerance in soybean

Soybean production is greatly influenced by abiotic stresses imposed by environmental factors such as drought, water submergence, salt, and heavy metals. A thorough understanding of plant response to abiotic stress at the molecular level is a prerequisite for its effective management. The molecular mechanism of stress tolerance is complex and requires information at the omic level to understand it effectively. In this regard, enormous progress has been made in the omics field in the areas of genomics, transcriptomics, and proteomics. The emerging field of ionomics is also being employed for investigating abiotic stress tolerance in soybean. Omic approaches generate a huge amount of data, and adequate advancements in computational tools have been achieved for effective analysis. However, the integration of omic-scale information to address complex genetics and physiological questions is still a challenge. In this review, we have described advances in omic tools in the view of conventional and modern approaches being used to dissect abiotic stress tolerance in soybean. Emphasis was given to approaches such as quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection (GS). Comparative genomics and candidate gene approaches are also discussed considering identification of potential genomic loci, genes, and biochemical pathways involved in stress tolerance mechanism in soybean. This review also provides a comprehensive catalog of available online omic resources for soybean and its effective utilization. We have also addressed the significance of phenomics in the integrated approaches and recognized high-throughput multi-dimensional phenotyping as a major limiting factor for the improvement of abiotic stress tolerance in soybean.

Pinpointing genes underlying the quantitative trait loci for root-knot nematode resistance in palaeopolyploid soybean by whole genome resequencing

The objective of this study was to use next-generation sequencing technologies to dissect quantitative trait loci (QTL) for southern root-knot nematode (RKN) resistance into individual genes in soybean. Two hundred forty-six recombinant inbred lines (RIL) derived from a cross between Magellan (susceptible) and PI 438489B (resistant) were evaluated for RKN resistance in a greenhouse and sequenced at an average of 0.19× depth. A sequence analysis pipeline was developed to identify and validate single-nucleotide polymorphisms (SNPs), infer the parental source of each SNP allele, and genotype the RIL population. Based on 109,273 phased SNPs, recombination events in RILs were identified, and a total of 3,509 bins and 3,489 recombination intervals were defined. About 50.8% of bins contain 1 to 10 genes. A linkage map was subsequently constructed by using bins as molecular markers. Three QTL for RKN resistance were identified. Of these, one major QTL was mapped to bin 10 of chromosome 10, which is 29.7 kb in size and harbors three true genes and two pseudogenes. Based on sequence variations and gene-expression analysis, the candidate genes underlying the major QTL for RKN resistance were pinpointed. They are Glyma10g02150 and Glyma10g02160, encoding a pectin methylesterase inhibitor and a pectin methylesterase inhibitor -pectin methylesterase, respectively. This QTL mapping approach not only combines SNP discovery, SNP validation, and genotyping, but also solves the issues caused by genome duplication and repetitive sequences. Hence, it can be widely used in crops with a reference genome to enhance QTL mapping accuracy.

Evaluation of Resistance Screening Methods for Sclerotinia Stem Rot of Soybean and Dry Bean

Three methods to identify levels of resistance to Sclerotinia sclerotiorum in soybean (Glycine max) and dry bean (Phaseolus vulgaris) were compared using multiple data analyses. The three methods were mycelial plug inoculations of cotyledons, cut stems, and detached leaves. Six S. sclerotiorum isolates of known relative aggressiveness were inoculated on each of three soybean and dry bean cultivars with varied response to S. sclerotiorum. For soybean, all three inoculation methods accurately identified isolate aggressiveness irrespective of cultivar, but identification of susceptible and partially resistant soybean cultivars was influenced by isolate. For dry bean, the cotyledon and cut stem methods accurately identified isolate aggressiveness, but identification of susceptible and partially resistant dry bean cultivars was influenced by isolate and inoculation method. The cut stem method had the smallest coefficient of variation and was more precise for detecting interactions. When considering root mean square residual error combined over species and experiments, coefficient of variation based on residual error, significance of isolate-by-cultivar interaction from ANOVA, rank correlation between pairs of methods, and sensitivity ratio for the three resistance screening methods under controlled environmental conditions, the cut stem method was statistically better than the cotyledon and detached leaf methods for evaluating resistance in soybean and dry bean cultivars.

Inheritance of Salt Tolerance in Wild Soybean (Glycine soja Sieb. and Zucc.) Accession PI483463

Tolerant soybean (Glycine max [L.] Merr.) cultivars aid in reducing salt damage in problem fields. New genes are important to reduce losses from salt injury. Objectives of this study were to determine inheritance of salt tolerance in wild soybean (Glycine soja Sieb. and Zucc.) PI483463 and to test allelism of tolerance genes from genotypes PI483463 and S-100, a common ancestor of southern in US cultivars. Tolerant (T) PI483463 was crossed to sensitive (S) cultivar Hutcheson to study inheritance. PI483463 (T) was crossed with S-100 (T) to test for allelism. Parents, F(1) plants, F(2) populations, and F(2:3) lines were assayed in a 100 mM salt solution to determine tolerance. F(2) from T x S cross segregated 3(T):1 (S) and the F(2:3) lines responded 1 (T): 2 (segregating):1 (S). F(2) plants from PI483463 (T) x S-100 (T) segregated 15 (T):1 (S) indicating different genes from the 2 sources. Results showed that G. soja line PI483463 had a single dominant gene for salt tolerance, which was different than the gene in G. max line S-100. The symbol, Ncl2, was designated for this new salt tolerance allele.

Landscape of genomic diversity and trait discovery in soybean

Cultivated soybean [Glycine max (L.) Merr.] is a primary source of vegetable oil and protein. We report a landscape analysis of genome-wide genetic variation and an association study of major domestication and agronomic traits in soybean. A total of 106 soybean genomes representing wild, landraces, and elite lines were re-sequenced at an average of 17x depth with a 97.5% coverage. Over 10 million high-quality SNPs were discovered, and 35.34% of these have not been previously reported. Additionally, 159 putative domestication sweeps were identified, which includes 54.34 Mbp (4.9%) and 4,414 genes; 146 regions were involved in artificial selection during domestication. A genome-wide association study of major traits including oil and protein content, salinity, and domestication traits resulted in the discovery of novel alleles. Genomic information from this study provides a valuable resource for understanding soybean genome structure and evolution, and can also facilitate trait dissection leading to sequencing-based molecular breeding.

Potential of Association Mapping and Genomic Selection to Explore PI 88788 Derived Soybean Cyst Nematode Resistance

The potential of association mapping (AM) and genomic selection (GS) has not yet been explored for investigating resistance to soybean cyst nematode (SCN), the most destructive pest affecting soybean. We genotyped 282 representative accessions from the University of Minnesota soybean breeding program using a genome‐wide panel of 1536 single nucleotide polymorphism (SNP) markers and evaluated plant responses to SCN HG type 0. After adjusting for population structure, AM detected significant signals at two loci corresponding to rhg1 and FGAM1 plus a third locus located at the opposite end of chromosome 18. Our analysis also identified a discontinuous long‐range haplotype of over 600 kb around rhg1 locus associated with resistance to SCN HG type 0. The same phenotypic and genotypic datasets were then used to access GS accuracy for prediction of SCN resistance in the presence of major genes through a sixfold cross‐validation study. Genomic selection using the full marker set produced average prediction accuracy ranging from 0.59 to 0.67 for SCN resistance, significantly more accurate than marker‐assisted selection (MAS) strategies using two rhg1‐associated DNA makers. Reducing the number of markers to 288 SNPs in the GS training population had little effect on genomic prediction accuracy. This study demonstrates that AM can be an effective genomic tool for identifying genes of interest in diverse germplasm. The results also indicate that improved MAS and GS can enhance breeding efficiency for SCN resistance in existing soybean improvement programs.

Genetic architecture of cyst nematode resistance revealed by genome-wide association study in soybean

BackgroundBi-parental mapping populations have been commonly utilized to identify and characterize quantitative trait loci (QTL) controlling resistance to soybean cyst nematode (SCN, Heterodera glycines Ichinohe). Although this approach successfully mapped a large number of SCN resistance QTL, it captures only limited allelic diversity that exists in parental lines, and it also has limitations for genomic resolution. In this study, a genome-wide association study (GWAS) was performed using a diverse set of 553 soybean plant introductions (PIs) belonging to maturity groups from III to V to detect QTL/genes associated with SCN resistance to HG Type 0.ResultsOver 45,000 single nucleotide polymorphism (SNP) markers generated by the SoySNP50K iSelect BeadChip (http//www.soybase.org) were utilized for analysis. GWAS identified 14 loci distributed over different chromosomes comprising 60 SNPs significantly associated with SCN resistance. Results also confirmed six QTL that were previously mapped using bi-parental populations, including the rhg1 and Rhg4 loci. GWAS identified eight novel QTL, including QTL on chromosome 10, which we have previously mapped by using a bi-parental population. In addition to the known loci for four simple traits, such as seed coat color, flower color, pubescence color, and stem growth habit, two traits, like lodging and pod shattering, having moderately complex inheritance have been confirmed with great precision by GWAS.ConclusionsThe study showed that GWAS can be employed as an effective strategy for identifying complex traits in soybean and for narrowing GWAS-defined genomic regions, which facilitates positional cloning of the causal gene(s).

Genomic-assisted phylogenetic analysis and marker development for next generation soybean cyst nematode resistance breeding.

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is a serious soybean pest. The use of resistant cultivars is an effective approach for preventing yield loss. In this study, 19,652 publicly available soybean accessions that were previously genotyped with the SoySNP50K iSelect BeadChip were used to evaluate the phylogenetic diversity of SCN resistance genes Rhg1 and Rhg4 in an attempt to identify novel sources of resistance. The sequence information of soybean lines was utilized to develop KASPar (KBioscience Competitive Allele-Specific PCR) assays from single nucleotide polymorphisms (SNPs) of Rhg1, Rhg4, and other novel quantitative trait loci (QTL). These markers were used to genotype a diverse set of 95 soybean germplasm lines and three recombinant inbred line (RIL) populations. SNP markers from the Rhg1 gene were able to differentiate copy number variation (CNV), such as resistant-high copy (PI 88788-type), low copy (Peking-type), and susceptible-single copy (Williams 82) numbers. Similarly, markers for the Rhg4 gene were able to detect Peking-type (resistance) genotypes. The phylogenetic information of SCN resistance loci from a large set of soybean accessions and the gene/QTL specific markers that were developed in this study will accelerate SCN resistance breeding programs.

Gene expression profiling soybean stem tissue early response to Sclerotinia sclerotiorum and in silico mapping in relation to resistance markers.

White mold, caused by Sclerotinia sclerotiorum (Lib.) de Bary, can be a serious disease of crops grown under cool, moist environments. In many plants, such as soybean [Glycine max (L.) Merr.], complete genetic resistance does not exist. To identify possible genes involved in defense against this pathogen, and to determine possible physiological changes that occur during infection, a microarray screen was conducted using stem tissue to evaluate changes in gene expression between partially resistant and susceptible soybean genotypes at 8 and 14 hours post inoculation. RNA from 15 day‐old inoculated plants was labeled and hybridized to soybean cDNA microarrays. ANOVA identified 1270 significant genes from the comparison between time points and 105 genes from the comparison between genotypes. Selected genes were classified into functional categories. The analyses identified changes in cell‐wall composition and signaling pathways, as well as suggesting a role for anthocyanin and anthocyanidin synthesis in the defense against S. sclerotiorum. In‐silico mapping of both the differentially expressed transcripts and of public markers associated with partial resistance to white mold, provided evidence of several differentially expressed genes being closely positioned to white mold resistance markers, with the two most promising genes encoding a PR‐5 and anthocyanidin synthase.