Rhg4 is a major genetic locus that contributes to soybean cyst nematode (SCN) resistance in the Peking-type resistance of soybean (Glycine max), which also requires the rhg1 gene. By map-based cloning and functional genomic approaches, we previously showed that the Rhg4 gene encodes a predicted cytosolic serine hydroxymethyltransferase (GmSHMT08); however, the novel gain of function of GmSHMT08 in SCN resistance remains to be characterized. Using a forward genetic screen, we identified an allelic series of GmSHMT08 mutants that shed new light on the mechanistic aspects of GmSHMT08-mediated resistance. The new mutants provide compelling genetic evidence that Peking-type rhg1 resistance in cv Forrest is fully dependent on the GmSHMT08 gene and demonstrates that this resistance is mechanistically different from the PI 88788-type of resistance that only requires rhg1. We also demonstrated that rhg1-a from cv Forrest, although required, does not exert selection pressure on the nematode to shift from HG type 7, which further validates the bigenic nature of this resistance. Mapping of the identified mutations onto the SHMT structural model uncovered key residues for structural stability, ligand binding, enzyme activity, and protein interactions, suggesting that GmSHMT08 has additional functions aside from its main enzymatic role in SCN resistance. Lastly, we demonstrate the functionality of the GmSHMT08 SCN resistance gene in a transgenic soybean plant.

The soybean cyst nematode (SCN), Heterodera glycines, is one of the most important pathogens of soybean. Periodic monitoring of SCN population densities and virulence phenotypes is necessary for developing management strategies utilizing resistant cultivars, the primary strategy used to combat this pest. Therefore, we conducted a statewide survey of Missouri to determine SCN population densities and virulence phenotypes during 2015–2016 and compared these results with a similar survey conducted in 2005. SCN population densities were determined for 393 soil samples representing 74 soybean-producing counties across eight geographical regions of Missouri. Eighty-eight percent of samples tested positive for SCN, up from 50% in 2005, and population densities ranged from 125 to 99,000 eggs per 250 cm3 of soil. The virulence phenotypes of 48 SCN populations also were determined. For this, female indices (FI) were calculated by dividing the mean number of females that develop on the roots of a set of resistant soybean indicator lines by the mean number of females that develop on the roots of susceptible cultivar Lee74 after 30 days in the greenhouse then multiplying by 100 to obtain a percentage. Notably, all SCN populations evaluated during 2015–2016 had a FI > 10 on PI 88788, the most widely used source of resistance in Missouri, in contrast to 78% in 2005. Moreover, 50% of these populations had a FI > 50 on PI 88788, up from 16% in 2005. Forty-three percent of populations tested also had a FI > 10 on Peking, the second most common source of resistance by farmers. Our results show that over the last decade, SCN has become more prevalent in Missouri fields. Additionally, the percentage of individuals within SCN field populations that are virulent on PI 88788 and Peking has markedly increased. The results stress the importance of rotating cultivars with different types of resistance when using resistant cultivars to manage SCN.

Soybean cyst nematode (SCN) is an important pathogen of soybean causing more than $1 billion in yield losses annually in the United States. Planting SCN resistant soybean cultivars is the primary management strategy. Resistance genes derived from the plant introductions (PI) 88788 (rhg1-b) and PI 548402 (Peking; rhg1-a and Rhg4) are the main types of resistance available in commercial cultivars. The PI 88788 rhg1-b resistance allele is found in the majority of SCN resistant cultivars in the north central US. The widespread use of PI 88788 rhg1-b has led to limited options for farmers to rotate resistance sources to manage SCN. Consequently, an over reliance on a single type of resistance has resulted in the selection of SCN populations that have adapted to reproduce on these resistant cultivars. Here we evaluated the effectiveness of rotating soybean lines with different combinations of resistance genes to determine the best strategy for combating the widespread increase in virulent SCN and limit future nematode adaptation to resistant cultivars. Eight SCN populations were developed by either continuous selection of a virulent SCN field population (HG type 1.2.5.7) on a single resistance source or in rotation with soybean pyramiding different resistance gene alleles derived from PI 88788 (rhg1-b), PI 437654 (rhg1-a and Rhg4), PI 468916 (cqSCN-006 and cqSCN-007) and PI 567516C (Chr10). SCN population densities were determined for eight generations. HG type tests were conducted after the eighth generation to evaluate population shifts. The continued use of rhg1-b or 006/007 had limited effectiveness for reducing SCN type 1.2.5.7 population density, whereas rotation to the use of rhg1-a/Rhg4 resistance significantly reduced SCN population density, but selected for broader SCN virulence (HG type 1.2.3.5.6.7). A rotation of rhg1-a/Rhg4 with a pyramid of rhg1-b/006/007/Chr10 was the most effective combination at both reducing population density and minimizing selection pressure. Our results provide guidance for implementation of a strategic SCN resistance rotation plan to manage the widespread virulence on PI 88788 and sustain the future durability of SCN resistance genes.

Southern root‐knot nematode (SRKN) is one of the most yield‐suppressing pathogens in soybean [(Glycine max (L.) Merr.] in the United States. With limited chemical and cultural management options, the use of genetic resistance is the most efficient and economical approach to control SRKN. A major quantitative trait locus (QTL) mapped to chromosome 10 is the primary source of resistance in soybean cultivars; however, limited studies have been conducted to evaluate its efficacy in minimizing yield suppression under field conditions with SRKN pressure. This study evaluated the yield performance of 202 elite soybean lines in field conditions with variable distribution of SRKN. Soybean lines were characterized based on the presence of the major QTL on chromosome 10 and grown in four‐row yield plots across six environments over 2 yr. Plots were soil sampled and enumerated to determine nematode population densities. No statistically significant differences in yield performance between resistant and susceptible lines were observed in the absence of nematode pressure, indicating no yield drag associated with resistance. Under SRKN pressure, the group of resistant lines yielded, on average, 19.1 and 23.3% higher than the susceptible lines in 2018 and 2019, respectively. For the susceptible lines, the presence of 1,000 SRKN second‐stage juveniles (J2) in 100 cm−3 of soil negatively affected yield by 6.2%. The presence of the major resistance allele on chromosome 10 reduced yield losses by approximately six‐fold in comparison to the susceptible group which provided significant yield protection under high SRKN pressure.

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.