Identification of genomic loci conferring broad-spectrum resistance to multiple nematode species in exotic soybean accession PI 567305

Cataloging SCN resistance loci in North American public soybean breeding programs

Soybean cyst nematode (SCN) is a destructive pathogen of soybeans responsible for annual yield loss exceeding $1.5 billion in the United States. Here, we conducted a series of genome-wide association studies (GWASs) to understand the genetic landscape of SCN resistance in the University of Missouri soybean breeding programs (Missouri panel), as well as germplasm and cultivars within the United States Department of Agriculture (USDA) Uniform Soybean Tests-Northern Region (NUST). For the Missouri panel, we evaluated the resistance of breeding lines to SCN populations HG 2.5.7 (Race 1), HG 1.2.5.7 (Race 2), HG 0 (Race 3), HG 2.5.7 (Race 5), and HG 1.3.6.7 (Race 14) and identified seven quantitative trait nucleotides (QTNs) associated with SCN resistance on chromosomes 2, 8, 11, 14, 17, and 18. Additionally, we evaluated breeding lines in the NUST panel for resistance to SCN populations HG 2.5.7 (Race 1) and HG 0 (Race 3), and we found three SCN resistance-associated QTNs on chromosomes 7 and 18. Through these analyses, we were able to decipher the impact of seven major genetic loci, including three novel loci, on resistance to several SCN populations and identified candidate genes within each locus. Further, we identified favorable allelic combinations for resistance to individual SCN HG types and provided a list of available germplasm for integration of these unique alleles into soybean breeding programs. Overall, this study offers valuable insight into the landscape of SCN resistance loci in U.S. public soybean breeding programs and provides a framework to develop new and improved soybean cultivars with diverse plant genetic modes of SCN resistance.

3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes

Despite the increased efficiency of sequencing technologies and the development of reduced-representation sequencing (RRS) approaches allowing high-throughput sequencing (HTS) of multiplexed samples, the per-sample genotyping cost remains the most limiting factor in the context of large-scale studies. For example, in the context of genomic selection (GS), breeders need genome-wide markers to predict the breeding value of large cohorts of progenies, requiring the genotyping of thousands candidates. Here, we introduce 3D-GBS, an optimized GBS procedure, to provide an ultra-high-throughput and ultra-low-cost genotyping solution for species with small to medium-sized genome and illustrate its use in soybean. Using a combination of three restriction enzymes (PstI/NsiI/MspI), the portion of the genome that is captured was reduced fourfold (compared to a "standard" ApeKI-based protocol) while reducing the number of markers by only 40%. By better focusing the sequencing effort on limited set of restriction fragments, fourfold more samples can be genotyped at the same minimal depth of coverage. This GBS protocol also resulted in a lower proportion of missing data and provided a more uniform distribution of SNPs across the genome. Moreover, we investigated the optimal number of reads per sample needed to obtain an adequate number of markers for GS and QTL mapping (500-1000 markers per biparental cross). This optimization allows sequencing costs to be decreased by ~ 92% and ~ 86% for GS and QTL mapping studies, respectively, compared to previously published work. Overall, 3D-GBS represents a unique and affordable solution for applications requiring extremely high-throughput genotyping where cost remains the most limiting factor.

Breeding for disease resistance in soybean: a global perspective

KEY MESSAGE

This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

Exploring Soybean Resistance to Soybean Cyst Nematode

Resistance to the soybean cyst nematode (SCN) is a topic incorporating multiple mechanisms and multiple types of science. It is also a topic of substantial agricultural importance, as SCN is estimated to cause more yield damage than any other pathogen of soybean, one of the world's main food crops. Both soybean and SCN have experienced jumps in experimental tractability in the past decade, and significant advances have been made. The rhg1-b locus, deployed on millions of farm acres, has been durable and will remain important, but local SCN populations are gradually evolving to overcome rhg1-b. Multiple other SCN resistance quantitative trait loci (QTL) of proven value are now in play with soybean breeders. QTL causal gene discovery and mechanistic insights into SCN resistance are contributing to both basic and applied disciplines. Additional understanding of SCN and other cyst nematodes will also grow in importance and lead to novel disease control strategies.

Molecular Breeding to Overcome Biotic Stresses in Soybean: Update

Soybean (Glycine max (L.) Merr.) is an important leguminous crop and biotic stresses are a global concern for soybean growers. In recent decades, significant development has been carried outtowards identification of the diseases caused by pathogens, sources of resistance and determination of loci conferring resistance to different diseases on linkage maps of soybean. Host-plant resistance is generally accepted as the bestsolution because of its role in the management of environmental and economic conditions of farmers owing to low input in terms of chemicals. The main objectives of soybean crop improvement are based on the identification of sources of resistance or tolerance against various biotic as well as abiotic stresses and utilization of these sources for further hybridization and transgenic processes for development of new cultivars for stress management. The focus of the present review is to summarize genetic aspects of various diseases caused by pathogens in soybean and molecular breeding research work conducted to date.

Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans

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.