Soybean [Glycine max (L.) Merr.] is one of the most important crops globally. It produced 70.86% of the global supply of plant-based protein meal and 28.88% of the plant-based oil (second only to palm oil) in the 2020/2021 market year (Market View Data Base, Untied Soybean Board 2021. Total world soybean production in 2020 was 353.5 million metric tons (Mt), and the estimated cultivated area was 127.0 million ha. While cultivated throughout the world, 96.2% of soybean production is concentrated in ten countries: Brazil (121.8 million Mt), the USA (112.5 million Mt), Argentina (48.8 million Mt), China (19.6 million Mt), India (11.2 million Mt), Paraguay (11.0 million Mt), Canada (6.4 million Mt), Russia (4.3 million Mt), Ukraine (2.8 million Mt), and Bolivia (2.8 million Mt) (FAOSTAT 2020; Fig. 1). A major constraint to soybean production is disease loss. Of more than 200 pathogens known to infect soybean, only about 35 are economically important (Hartman et al. 2016). The most prevalent diseases in major soybean production regions of the world are presented in 1. The type and severity of disease and the degree of yield and seed quality loss vary with region and year, depending on the climate and the growing season weather, cultural and disease control practices, and the genetic diversity of the pathogens and the soybean cultivars. Unfortunately, the proportion of global soybean yield loss due to diseases increased from ~ 11% in 1994 to 27% in 2006. In 1994, soybean diseases caused losses of nearly 15 million Mt (10.87% of total production), valued at more than $3 billion across the top ten soybean production countries (Wrather et al. 1997). In 1998, the world soybean yield losses due to diseases were more than 28 million Mt (18.49% of total production), more than doubled the losses in 1994 (> $6 billion) (Wrather et al. 2001). In 2006, a total of 59.9 million Mt of soybean production were reduced in the world, accounting for more than 27% of the total soybean production (220.4 million Mt) (Wrather et al. 2010).

Fig. 1
figure 1

Global soybean yield production in 2020 (data obtained from FAOSTAT) and major diseases in top ten soybean production countries. SCN: soybean cyst nematode; SDS: sudden death syndrome; PSS: Phomopsis seed decay; CLB: Cercospora leaf blight; SMV: soybean mosaic virus; BYMV: bean yellow mosaic virus; AMV: Alfalfa mosaic virus

Table 1 Soybean diseases in major soybean production regions of the world

In a recent report of soybean production losses caused by diseases in the USA and Canada from 2010 to 2014, yearly losses ranged from 10.06 to 13.92 million Mt (11.7–14.2% of total soybean production) (Allen et al. 2017). These losses are the result of many diseases caused by a range of fungi, bacteria, phytoplasmas, nematodes, and viruses. Recent meta-analyses of soybean disease losses in the USA over the last 24 years found that the greatest losses across states and years were from soybean cyst nematode (SCN) (Heterodera glycines Ichinohe), charcoal rot [Macrophomina phaseolina (Tassi) Goid], and seedling diseases (caused by several oomycetes and fungi) (Bandara et al. 2020; Roth et al. 2020). Important intermittent diseases caused by variations in the weather were Phytophthora root and stem rot (Phytophthora sojae Kaufmann & Gerdemann), sudden death syndrome (SDS) (Fusarium virguliforme O’Donnell and T. Aoki), and Sclerotinia stem rot [Sclerotinia sclerotiorum (Lib.)] (Roth et al. 2020). Root-knot nematode (Meloidogyne spp.), reniform nematode (Rotylenchulus reniformis Linford & Oliveira), and Diaporthe diseases were emerging diseases. Disease pressure appears to be increasing as greater yield losses have been observed over time (Bandara et al. 2020).

In Brazil, estimates in 1997 reported that the greatest disease losses were from stem canker (Diaporthe aspalathi (E. Jansen, Castl. & Crous) and D. caulivora (Athow & Caldwell) J.M. Santos, Vrandecic & A.J.L. Phillips), brown spot (Septoria glycines Hemmi), Cercospora leaf blight (CLB)/purple seed stain (PSS) [Cercospora kikuchii (Matsumoto & Tomoyasu) M. W. Gardner], and charcoal rot followed by soybean cyst nematode, seedling diseases, and Sclerotinia stem rot (Wrather et al. 1997). However, after soybean rust [Phakopsora pachyrhizi (Sydow. & Sydow.)] was introduced in Brazil in 2002, it quickly became the most suppressive soybean pathogen causing yield losses of nearly sixfold greater than CLB/PSS, the second most damaging disease in the country (Wrather et al. 2010). Soybean rust is particularly damaging in Brazil due to the year-round survival of the pathogen in production areas unlike in neighboring Argentina, where the pathogen must be re-introduced each year, therefore resulting in significantly less damage than in Brazil. The major soybean diseases in Argentina include SDS, charcoal rot, Cercospora leaf blight, brown spot, target spot [Corynespora cassiicola (Berk. & M.A. Curtis) C.T. Wei], and Sclerotinia stem rot. The most prevalent soybean disease in China is soybean mosaic virus (SMV). Other major diseases in China include frogeye leaf spot (Cercospora sojina Hara), SCN, anthracnose (Colletotrichum spp.), root rot (P. sojae, Pythium spp., Fusarium spp.), bacterial diseases, Sclerotinia stem rot, downy mildew [Peronospora manshurica (Naum.) Syd.], and soybean rust (Wrather et al. 1997, 2001, 2010). Prominent diseases in India include viruses, Sclerotium blight (Sclerotium rolfsii Sacc.), anthracnose (Colletotrichum spp.), and soybean rust (Wrather et al. 2010).

Russia and Ukraine are the most soybean productive countries in the world. Common soybean diseases in Russia include SCN, SMV, downy mildew, frogeye leaf spot, Phyllosticta leaf spot (Pleosphaerulina sojicola Miura, syn. Phyllosticta sojicola C. Massal.), CLB/PSS, brown spot, bacterial bustle (Xanthomonas axonopodis pv. glycines), and bacterial blight (Pseudomonas syringae pv. glycinea Coerper) (Bushnev et al. 2020; Sinegovskaya 2021). In Ukraine, SMV is a major concern which often infects together with bean yellow mosaic virus (BYMV), and Alfalfa mosaic virus (AMV) in the right-bank region (Kyrychennko et al. 2012; Mishchenko et al. 2017), while in the Forest-Steppe region, Alternaria leaf spot, downy mildew, Fusarium wilt and root rot, brown spot, and bacterial blight are the most prevalent soybean diseases (Sergiienko et al. 2021).

Africa and Australia represent geographical regions with the potential to become major soybean producers in the future (Hartman and Murithi, 2019). Africa produces about 1% of global soybean production (FAOSTAT, 2020). The major soybean diseases in Africa include soybean rust, frogeye leaf spot, red leaf blotch (Coniothyrium glycines), and SDS (Murithi et al. 2016; Hartman and Murithi, 2019). Australia produced 17,323 tons of soybean in 2020 (FAOSTAT 2020), and the major soybean diseases include charcoal rot, sclerotinia stem rot, Phytophthora root rot, and soybean rust (Ryley 2013).

In the future, soybean diseases may be continuously severe and difficult to manage, especially with the significant changes in the global climate (Roth et al. 2020). Since 1981, global temperatures have risen 0.18 °C per decade ( and are expected to rise 6 °C by the next century (Mikhaylov et al. 2020). Temperatures and water precipitation are expected to increase in many areas (Tebaldi et al. 2006; Karl et al. 2009), but the increase in rainfall will be followed by more frequent extreme weather events as well as more frequent and severe droughts, making the overall weather patterns less consistent and predictable (Prein et al. 2021). It is estimated that rising temperatures have hindered agricultural production gains by 21% and made the management of plant diseases increasingly challenging (Jones 2021; Ortiz-Bobea et al. 2021). In the USA, it is predicted that climate changes may reduce average soybean yields by 86–92% by 2050 (Yu et al. 2021). These climate changes may alter the types, severities, and geographical distributions of soybean diseases, especially for the intermittent diseases that are heavily influenced by environmental factors, such as Phytophthora root and stem rot, SDS, and Sclerotinia stem rot (Roth et al. 2020).

Effective soybean disease management includes cultural practices (crop rotation, tillage, clean seed, etc.), chemical applications (foliar, seed, or soil), but the most important component is the deployment of resistant cultivars (Grau et al. 2004). Resistant cultivars can carry either vertical resistance, horizontal resistance, or both. Vertical resistance is contributed by resistance genes (R genes) for specific diseases, such as SCN (Rhg), Phytophthora root and stem rot (Rps), soybean rust (Rpp), frogeye leaf spot (Rcs), bacterial blight (Rpg), and SMV (Rsv and Rsc). R genes have been widely deployed conferring complete resistance to some pathotypes of the pathogen. The R genes typically follow a gene-for-gene interaction with the corresponding avirulence (Avr) factors from the pathogen, and resistance occurs only when the R gene and Avr factors both exist (Whitham et al. 2016). Therefore, R genes are pathotype (race)-specific, i.e., they may confer full protection to some pathotypes of the pathogen, while they are completely susceptible to others. R genes are often non-durable, and can be quickly overcome, due to the fast shift of the pathogen populations. For instance, the Rpp1 and Rpp3 genes mediated resistance to soybean rust were defeated the following year after the disease first occurred in Brazil in 2001 (Garcia et al. 2008; Langenbach et al. 2016). Another example is the Rps1k gene which has been traditionally deployed since the 1990s, can be defeated by most of the newly emerged pathotypes of Phytophthora sojae (McCoy et al. 2021). Although there are some exceptions such as Rcs3 which has provided durable resistance against all known races of frogeye leaf spot in the USA (Boerma and Phillips 1983; Mian et al. 2008), searching for novel sources of resistance genes is a vital task for the deployment of vertical resistance and sustainability of the global soybean value chain.

In contrast, horizontal resistance (sometimes called partial resistance or tolerance) is quantitative and conferred by multiple minor effect genes and/or quantitative trait loci (QTL). Unlike vertical resistance that occurs only to some specific pathogens, horizontal resistance is widely involved in multiple soybean diseases and is known as the only type of resistance to many soybean diseases, including SDS, Sclerotinia stem rot, root-knot nematode, and most Pythium species. Horizontal resistance is usually considered pathotype non-specific (Dorrance et al. 2008; St. Clair 2010; Mundt 2014; Nelson et al. 2018; Karhoff et al. 2019), although some isolate specific QTLs have also been identified in soybean (Lee et al. 2014; Stasko et al. 2016; Lin et al. 2021). Therefore, horizontal resistance is considered more durable.

figure a

The traditional introgression of resistance genes into resistant cultivars can take more than ten years starting from making crosses between the recurrent parents and the resistance donor parents. Fortunately, with the development of molecular marker technology, especially with the sequencing of the soybean genome and the development of low cost of high-throughput genotyping (such as the BARCSoySNP6K and BARCSoySNP50K iSelect BeadChips), breeders can make selections more efficiently and accurately (Song et al. 2013, 2020). Marker-assisted selection (MAS) has proved to be the most successful approach in the selection of R genes or major QTLs (Ribaut and Hoisington 1998). The markers used for MAS have evolved from the low-efficiency restriction fragment length polymorphism (RFLP) markers to simple sequence repeat (SSR) markers, and currently, to more efficient and cost-friendly SNP markers in modern soybean breeding programs. However, for minor effect QTLs, genomic selection (GS) has been demonstrated to outperform MAS with higher accuracy and efficiency (Bao et al. 2014; Wen et al. 2018). For example, Bao et al. (2014) genotyped 282 soybean accessions for resistance to SCN HG type 0 and discovered that GS using full marker set produced significantly more accurate predictions than MAS using two rhg1-associated DNA markers. In another study for soybean resistance to white mold (Wen et al. 2018), the GS prediction accuracy was estimated at 0.64, which was significantly higher than that of MAS (0.47–0.51), although MAS was still 24–26% higher than using random SNPs. Moreover, with the recent development of new technologies such as GWA studies, numerous SNP markers have been identified for soybean resistance against various diseases and have the potential to be deployed in the future (Wen et al. 2014; Vuong et al. 2015; Zhang et al. 2015a; Chang et al. 2016; Rincker et al. 2016a; Coser et al. 2017; Moellers et al. 2017; Lin et al. 2020). On the other hand, genome-editing technology (such as CRISPR/Cas9) allows plant breeders to fine-tune gene regulation toward the improvement of crop resistance to various diseases (Chen et al. 2019).

figure b

To assist soybean breeders to develop effective breeding strategies under the global climate change, reducing the world soybean yield loss due to diseases and ensure the continuous growth and sustainability of the global soybean production in the next decade, this review aims to: 1. provide comprehensive atlas of soybean genes and QTLs conferring resistance to 28 economically important and emerging diseases, including their donor source, genetic position, tightly linked markers, resistance spectrum, and testing methods; 2. validate high-quality QTLs across different studies based on the overlapping of their genomic positions; and 3. offer comprehensive future perspectives and breeding suggestions for disease-related pipelines. This review may also serve as a guideline and toolbox for soybean breeders around the world.

Section I. Soybean resistance to nematode diseases

Plant–parasitic nematodes are the major constraints for soybean production worldwide. Nematodes alone are responsible for a projected loss of $78 billion annually worldwide with a 10–15% average yield loss in soybean (Lima et al. 2017). The intensity of yield loss caused by parasitic nematodes are variable and typically depends on several factors including the nematode species, the nematode population density, management practices, the genetic background of soybean varieties, and soil and environmental factors (Bradley et al. 2021). In recent decades, nematode infestation has been spread in most soybean producing countries in the world including the USA, Brazil, Canada, South Africa, Japan, China, and India. Soybean cyst nematode, southern root-knot nematode, reniform, and lance nematodes are the major plant–parasitic nematodes in soybean around the world resulting in losses of as much as 100% (Wrather and Koening 2009; Kim et al. 2016; Bradley et al. 2021). The detailed information of each specific nematode and breeding efforts to enhance the levels of resistance is described below.

Soybean cyst nematode

Among plant–parasitic nematode species, soybean cyst nematode (SCN, caused by Heterodera glycines Ichinohe) is the most destructive sedentary and obligate parasite of soybean causing up to 30% yield loss (Mueller et al. 2016). The annual production losses caused by SCN are more than twice as much as any other diseases in North America, causing projected yearly losses of billions of dollars worldwide. In 1915, Japan reported the first occurrence of SCN, and later in 1954 it was identified in North Carolina, USA (Winstead et al. 1955; Riggs, 2004), and later in Ontario, Canada (Anderson et al. 1988). Subsequently, it spread to most soybean-producing countries causing severe yield losses worldwide. For instance, more than 3.5 million Mt of production losses caused by SCN were reported in 28 states of the USA (Koenning and Wrather 2010; Allen et al. 2017) corresponding to more than $1 billion in value (Liu et al. 2012). Later, SCN infestation was identified in Quebec province, Canada (Mimee et al. 2015) and some of the soybean cultivated provinces in China (Peng et al. 2016).

While crop damage due to SCN is devastating, the symptoms above the ground level are not every time noticeable, and infestations are typically only identified in the advanced phase of infection. At this stage, a significant amount of damage has already taken place. Symptoms include chlorosis, stunting, reduced root development, and decreased nodule formation (Niblack et al. 2006). Several traditional practices including biological, chemical, and physical methods have been attempted to control SCN infestation but were found inadequate for the management of the disease. The development and deployment of resistant cultivars along with crop rotation methods are the preferably efficient practice for the management of SCN (Davis and Tylka 2000).

Breeding for SCN resistance involves the genetic mapping of QTLs/genes associated with the resistant phenotype and understanding the underlying resistance mechanism. The first Rhg (resistance to H. glycines) locus was reported around the mid-1950s (Ross and Brim 1957) which described plant introductions (PIs) 88788 and ‘Peking’ (PI 548402) as sources of SCN resistance. These two accessions were integrated into the soybean breeding programs through cycles of backcrossing. With the rapid progress in the availability of molecular markers and mapping techniques, numerous SCN-resistance loci have been reported by the soybean research community. Table 2 summarizes the main reported QTLs linked to SCN resistance. In soybean, SCN resistance trait is typically multi-genic and quantitatively inherited (Anand and Rao-Arelli 1989; Guo et al. 2005; Vuong et al. 2010, 2011). The resistance found in Peking was governed by three independent recessive genes (Caldwell et al. 1960). Since then, numerous genes/QTLs conferring SCN resistance have been mapped to date. Among these QTLs, two loci rhg1 and Rhg4 found on chromosomes 18 and 8, respectively, which confers resistance to SCN races 1, 2, 3, 4, and 5, have been extensively investigated (Kim et al. 2016). In diverse soybean germplasm lines, the rhg1 locus has been constantly mapped and identified at a sub-telomeric region on the chromosome (Chr.) 18 (Kim et al. 2016). Using rhg1, several markers were developed, of which Satt309 (predicted at about 0.4 cM from rhg1 locus) has been extensively applied for MAS in soybean research (Cregan et al. 1999; Silva et al. 2007). Another major QTL for SCN resistance showed a total phenotypic variation of about 9–28% to SCN HG types 2.5.7 (race 1) and 0 (race 3) and was described as Rhg4 gene from different resistant plant accessions (Concibido et al. 2004). Meksem et al. (2001) described that rhg1 and Rhg4 equally demonstrated about 98% of phenotypic variation in the ‘Forrest’ cultivar conferring resistance to race 3 of SCN. Rhg4 mediated resistance is largely associated with race 3 of SCN, in addition to some minor resistance against race 2 (HG types, race 1 (2.5.7), and race 14 ( In Peking and PI 437654 accessions, rhg1 and Rhg4 loci are essential to provide complete resistance against some SCN races. QTL mapping in PI 567516C identified two SCN-resistance QTLs on chromosomes 10 and 18, which were not linked to major rhg1 or Rhg4 loci (Vuong et al. 2010). These QTLs conferred resistance against races 1, 2, 3, and LY1 of SCN (Young 1998). Interestingly, the QTL detected on Chr. 18 is far away from the rhg1 locus. Another two QTLs were mapped on chromosomes 10 and 18 in PI 567305 (Kim et al. 2016) and were showing elevated resistance to various SCN HG types, identical with the study demonstrated by Vuong et al. (2010) in PI 567516C. Therefore, these results indicated that both PI 567305 and PI 567516C harbor novel QTLs which can provide SCN resistance. Recently, the genetic analysis of the PI 567305 line through Infinium SoySNP6K BeadChips and genotype-by-sequencing (GBS) revealed major QTLs on chromosomes 10 and 18 (Vuong et al. 2021) conferring resistance to SCN as well as other two important nematode species such as root-knot and reniform nematodes. The unique genetic structure of PI 567305 investigated using haplotype and copy number variation analysis suggested the presence of different resistance mechanisms from PI 88788 or Peking-type.

Table 2 Validated genes/loci conferring resistance to soybean cyst nematode disease (caused by Heterodera glycines)

In addition, three resistance loci for race 3 of SCN were detected in a GWA study of 282 soybean accessions, among which two out of these three were correlated to rhg1 and earlier mapped, FGAM1, SCN-resistance locus whereas the third one was positioned at Chr. 18 (Zhang et al. 2017). About 8 novel QTLs for resistance to race 3 of SCN was also identified by Vuong et al. (2011). Furthermore, 13 significant SNPs for SCN resistance were also identified in 7 diverse genomic regions by Zhang et al. (2017). Out of these 13, 10 SNPs were novel, whereas the remaining 3 were linked to earlier mapped QTLs including rhg1 and Rhg4. An investigation performed by Zhao et al. (2017) demonstrated the identification of 13 important SNPs (4 novels) on five chromosomes which conferred resistance to SCN race 1. Later, twelve SNPs significantly linked to SCN resistance were identified on chromosomes 7, 8, 10, and 18. Of these twelve, three were positioned close to the rhg1 locus (Tran et al. 2019). Using these data, multiple candidate genes conferring SCN resistance have been discovered. Liu et al. (2019) described 10 genes having 27 mutations, among which three genes overlapped between the two phenotypic mutants suggesting possible involvement of these genes in nematode resistance.

The copy number of rhg1 has been categorized into two repeat types such as high (> 6 repeats, as in PI 88788) and low (about 3 repeats, as in Peking) (Cook et al. 2012). Yu et al. (2016) demonstrated that, in the case of rhg1, both gene-based polymorphism and copy number variation were significantly important for SCN resistance. It also indicated that rhg1 resistance sources with a high copy number provided elevated resistance against SCN. Altogether it was proposed that rhg1 locus may facilitate SCN resistance through copy number variation of numerous genes encoding amino acid transporter (AAT), a WI12 (wound-inducible) protein, and an α-soluble N-ethylmaleimide-sensitive factor (NSF) Attachment Protein (α-SNAP) (Kandoth et al. 2017; Liu et al. 2017). Furthermore, Patil et al. (2019) categorized the rhg1-b locus into two classes, rhg1-b (like lines of PI 88788-type) and rhg1-b1 (like lines of Cloud-type) and revealed genetic basis of broad-spectrum resistance through interactions of copy number variation among rhg1 and Rhg4 genes. Liu et al. (2012) reported that the resistance at the Rhg4 locus was provided through the serine hydroxymethyltransferase (SHMT) gene, whose encoding protein catalyzes the reversible conversion of tetrahydrofolate and serine to tetrahydrofolate and glycine, respectively. The two polymorphisms in the gene GmSHMT08 positioned at the first and second exons, 389 G/C and 1165 A/T, results in modification of amino acids such as arginine vs. proline and tyrosine vs. asparagine, respectively, and further alteration of the kinetic properties (Liu et al. 2012). GmSHMT08 encoded protein shows a multifarious role in addition to essentially being involved in the enzymatic reaction of SCN resistance (Kandoth et al. 2017). It has additional functions including structural stability, ligand binding, and interactions with other proteins (such as GmSNAP18). Kandoth et al. (2017) showed that rhg1-a allele is required in Forrest cultivar for SCN resistance although it does not impart any selection pressure on nematodes to shift from HG type 7. However, the nematodes were exposed to EXF67 cv. shifted to HG type indicating the bi-genic phenomenon of resistance and necessity of Rhg4 in Peking-type facilitating resistance.

Cook et al. (2014) showed the distribution of nonsynonymous SNPs in the GmSNAP11 gene, its paralogous copy identified as GmSNAP18, with novel alleles that participated in SCN resistance, especially α-SNAP is crucial for resistance in soybean varieties derived from PI-88788. Further, Lakhssassi et al. (2017) demonstrate that the predicted protein of α-SNAP corresponds to truncated GmSNAP11 and not to GmSNAP18 (289 amino acids, aa). GmSNAP11 exists in Forrest Pecking type in two different forms such as GmSNAP11-T1 (239 aa) and GmSNAP11-T2 (244 aa). A nonsynonymous SNP known as map-5149 tightly linked to resistance against race 3 of SCN was identified in GmSNAP11 (Li et al. 2016a, b, c). Altogether, these results suggest the novel nature of GmSNAP11 providing SCN resistance in soybean.

Marker-assisted selection (MAS) is an effective and routinely performed strategy to develop SCN resistant soybean lines, representing the most rapid, cost-effective, accurate, and reliable method. Shi et al. (2015) developed functional Kompetitive Allele-Specific PCR (KASP) marker assays (GSM381 and GSM383 at rhg1; GSM191 at Rhg4) which were effectively applied for rapid and quick selection of SCN resistance, as well as identification of Peking and PI 88788 types of resistance. Kadam et al. (2016) developed KASPar (KBioscience Competitive Allele-Specific PCR) assays from SNPs at rhg1, Rhg4, and other novel QTLs. They effectively differentiated the copy number variation at rhg1 into three groups including (1) high resistant such as PI 88788 type, (2) low copy resistant such as Peking type, and (3) susceptible single copy such as Williams82 type numbers. Tian et al. (2019) developed cleaved amplified polymorphic sequences (CAPS) markers using GmSNAP11 (minor resistant to SCN) and combined with markers Rhg-389 and rhg1-2 for genotyping a panel consisting of 209 soybean accessions with variable SCN resistance.

The underlying molecular mechanisms of SCN resistance are complex and yet to be unveiled. Some studies suggested that there could be several disease-resistance proteins involved in SCN resistance, comprising Nucleotide-binding site-leucine-rich repeats (NBS-LRR), cytochrome P450s, RING domain proteins, zinc-finger domain proteins, protein kinases, transcription factors such as MYB and WRKY. Kofsky et al. (2021) studied the transcriptome of wild SCN resistant soybean (Glycine soja) ecotype, ‘NRS100’, and proposed biochemical mechanisms. This included the downregulation of the jasmonic acid (JA) signaling pathway to permit resistance response led by salicylic acid (SA) signaling-activation and polyamine synthesis which further maintains structural stability of root cell walls.

Soybean root-knot nematode

Root-knot nematodes (Meloidogyne spp.) are considered the most economically important and widely distributed parthenogenic plant–parasitic nematodes in the world (Trudgill and Blok 2001). Southern Root-knot nematode [SRKN, M. incognita (Kofold & White) Chitwood] was considered as one of the major plant–parasitic nematodes based on scientific and economic importance (Jones et al. 2013). The observed symptoms of SRKN in soybean are similar with the symptoms of abiotic stresses, including stunted growth, wilting, leaf discoloration, and deformation of the roots. The magnitude of crop losses depends on historical crop rotation and field usage, environmental parameters, initial nematode population density, soil type, and genetic background (Vieira et al. 2021).

SRKN is challenging to control due to its short life cycle and high reproductive rates (Trudgill and Blok 2001). Chemical approaches used to be an effective management option, however, most commercial nematicides and soil fumigants were banned due to toxicity to humans, animals, and environments (Abad et al. 2008). Crop rotation is especially challenging and limited since most flowering plants are hosts to SRKN. The use of genetic resistance becomes the most sustainable—economically, environmentally, and socially—alternative to efficiently control the damage caused by SRKN in soybean (Vieira et al. 2021).

Significant efforts have been taken to identify soybean accessions resistant to SRKN. Luzzi et al. (1987) screened over 2700 soybean accessions from the USDA Soybean Germplasm Collection and found that ‘Amredo’, PI 96354, PI 408088, and PI 417444 showed lower gall indices, fewer eggs per root system, and eggs per gram of root than the resistant check Forrest (PI 548655) (Luzzi et al. 1987). Harris et al. (2002) screened 608 PIs from Southern China and reported that PI 594753A and PI 594775A had similar resistance levels as PI 96354 (Harris et al. 2002). The first report on the genetic control of the resistance to SRKN indicated that reduced galling in the cultivar Forrest was controlled by a single dominant gene designated as Rmi1 (Luzzi et al. 1994a). Hybridizations between PI 96354 × Forrest and Forrest × PI 417444 resulted in individual F3 plants and F3 populations with higher galling than Forrest, PI 96354, and PI 417444, implying the resistance from Forrest (Rmi1) differs from PI 96354 and PI 417444 by at least one gene (Luzzi et al. 1994b).

The first genetic mapping of resistance to SRKN (race 3) in soybean identified two QTLs on chromosomes 10 and 18, accounting for 31% and 14% of phenotypic variation, respectively (Tamulonis et al. 1997). The combination of both resistance QTLs enhanced the levels of resistance to SRKN race 3, the predominant race in the U. S. (Li et al. 2001a). An additional major QTL on Chr. 7 accounting for 62% of the phenotypic variation was reported to confer resistance to SRKN race 2, a predominant race in soybean production areas of South Africa (Fourie et al. 2008). In addition, two minor QTLs on Chr. 8 (7.4% of the phenotypic variation) and 13 (5.6% of the phenotypic variation) were reported to confer resistance to SRKN race 3 (Xu et al. 2013) (Table 3).

Table 3 Soybean loci conferring high resistance to southern root-knot nematode (caused by Meloidogyne spp.)

To better understand the mechanisms of soybean resistance to root-knot nematode, fine-mapping analyses were conducted for the major QTL on Chr. 10. Pham et al. (2013) identified three candidate genes with cell wall modification-related functions, including Glyma.10g016600 (Extensin 1 encoding function), Glyma.10g016700 (Extensin 2 encoding function), and Glyma.10g017100 (Pectinesterase 1 encoding function). In another independent study, five candidate genes were identified, including Glyma.10g017100, Glyma10g02150, Glyma.10g017200, Glyma.10g017300, and Glyma.10g017400, all with pectinesterase encoding-related functions (Xu et al. 2013). Moreover, a GWA study using a panel of diverse soybean accessions narrowed down this QTL to a 12-kb region with five significant single nucleotide polymorphisms (SNPs) located within Glyma.10g017100 accounting for 25 to 40% of phenotypic variations (Passianotto et al. 2017).

Multiple reports have shown that SRKN resistant soybean genotypes can sustain yield under variable levels of nematode infection. Yield suppression can reach as much as 97% in susceptible genotypes while resistant genotypes may show less than 1% (Herman et al. 1990). Kinloch et al. (1984) reported a negative correlation between yield and number of galls under high pressure, which translated in resistant cultivars yielding as much as 5 times greater than highly susceptible cultivars (Kinloch et al. 1984). Vieira et al. (2021) evaluated the yield performance of 202 elite soybean lines in field conditions with variable distributions of SRKN and reported resistant lines yielding on average 20% higher than susceptible lines. The presence of the major resistance allele on Chr. 10 reduced yield losses by approximately sixfold in comparison to the susceptible group (1.1% and 6.2% per 1000 SRKN second-stage juveniles in 100 cm−3, respectively), which provided significant yield protection under high SRKN pressure (Vieira et al. 2021). However, because of the high concentration and wide distributions of SRKN, the limited and narrow base of genetic resistance, and lack of alternative management options, a resistance-breaking population in soybean could result in devastating yield losses (Vieira et al. 2021). Consequently, further work is needed to unveil and stack novel sources of resistance resulting in enhanced and more durable resistance in the future (Vieira et al. 2021).

Reniform nematode and Lance nematode

Reniform nematode (Rotylenchulus reniformis Linford & Oliveira) (RN), a sedentary semi-endoparasite, first emerged in Hawaii on cowpeas [Vigna unguiculata (L.) Walp.] in 1931 and was identified in Georgia, USA, in 1940 (Linford and Oliveira 1940; Smith 1940; Gavilano et al. 2013). It has now become a major yield-limiting parasitic nematode species in soybean growing areas in southern and southeastern states of the USA, due to its wide range of hosts (over 300 plant species), and the ability of surviving in broad soil range and dry soil for an extended period (Herald and Thames 1982; Herald and Robinson 1990; Wrather et al. 1995; Robinson et al. 1997; Robbins et al. 1999; Koenning and Wrather 2010). The infestation on the roots of the host is initiated by the vermiform female adults, which is different from common sedentary endo-parasitic nematode genera (Heterodera, Globodera, and Meloidogyne). Female RN establish feeding sites known as syncytium and eventually become sedentary. The common name of RN refers to its kidney shape characteristics. The male RN are involved in mating but do not feed (Linford and Oliveira 1940; Gaur and Perry 1991; Ganji et al. 2013; Robbins 2013). Typical symptoms of RN infection include root decay, stunting, and foliar chlorosis (Cook et al. 1997; Kinloch 1998; Rivera and Thiessen 2020). Annual soybean yield losses of up to 33% were reported in soybean cultivars that were partially or not resistant to RN, resulting in an average loss of 28,000 Mt in southern USA in 2019 (Kim et al. 2016; Allen et al. 2020). Like other nematode pests, deployment of resistant varieties has been the most effective and economical strategy to control RN in soybean field (Kim et al. 2016).

The relationship between SCN and RN has drawn interest since they both establish syncytium as their feeding sites. Early literatures reported that there were common sources of resistances for SCN and RN (Rebois et al. 1970). Field and greenhouse screening assays were subsequently conducted, and the studies indicated that soybean cultivars that derived their resistance from PI 88788 were resistant to SCN but susceptible to RN whereas cultivars that derived their resistance from Peking and PI 437654 were resistant to both SCN and RN (Robbins et al. 1994a, 1994b, 1999; Robbins and Rake 1996). Greenhouse screening assays were commonly used to evaluate RN resistance for soybean. Disease screening protocol for RN was well-established by Robbins et al. (1999), in which the reproductive index (RI) was calculated based on the number of nematodes at test termination (Pf) and initial infestation density (Pi) (RI = Pf/Pi). High level of RN resistance has been reported in soybean cultivars including Peking, ‘Dyer’, ‘Custer’, Pickett’, Forrest, ‘Hartwig’, and ‘Anand’ (Rebois et al. 1968; Robbins et al. 1994b; Davis et al. 1996). Lee et al. (2015) also reported RN resistance in PI 404198A, PI 438498, PI 467327, PI 468915. PI 494182, PI 507470, PI 507471, PI 507476, and PI 567516, all showing similar or less RI than the resistant check Anand.

Three QTLs conferring RN resistance in soybean have been identified on chromosomes 11, 18, and 19, respectively, from PI 437564 (Ha et al. 2007). Other studies have reported and confirmed resistant loci on chromosomes 8 (Lee 2021), 11 (Jiao et al. 2015; Wilkes et al. 2020; Usovsky et al 2021), 12 (Lee et al. 2016), 13 (Lee 2021), 15 (Lee 2021), and 18 (Jiao et al. 2015; Lee et al. 2016; Wilkes et al. 2020; Lee 2021; Usovsky et al. 2021). Recently, Usovsky et al. (2021) discovered the pleiotropic effect of two genes [GmSNAP18 (rhg1-a, rhg1-b, and rhg1-b1 allele) and GmSNAP11 (qSCN11 locus)], conferring resistances to both SCN and RN in PI 438489B using universal soybean linkage panel (USLP 1.0) and next-generation whole-genome resequencing (WGRS) technology (Table 4).

Table 4 Soybean loci conferring resistance to reniform nematode (caused by Rotylenchulus reniformis)

Lance nematodes (Hoplolaimus spp.) (LN) are migratory ecto-endo plant–parasitic nematodes that are widespread throughout the USA (Sher 1963; Astudillo and Birchfield 1980; Yan et al. 2016). A total of seven species have been identified and reported in the southeastern USA, including Hoplolaimus galeatus Thorne, 1935; H. columbus Sher, 1963; H. magnistylus Robbins, 1982; H. stephanus Sher, 1963; H. seinhorsti Luc, 1958; H. tylenchiformis von Daday, 1905; and H. concaudajuvencus Golden and Minton, 1970 (Lewis and Fassuliotis 1982; Robbins 1982; Koenning et al. 1999). However, only three species (H. columbus, H. galeatus, and H. magnistylus) have been considered economically important lance nematodes in soybean production in the USA (Holguin et al. 2016). The outbreak of H. columbus was first detected in South Carolina and predominantly prevailed in South Carolina, North Carolina, and Georgia while H. galeatus and H. magnistylus were commonly reported in soybean production areas in Alabama, Arkansas, Mississippi, and Tennessee (Lewis and Fassuliotis 1982; Robbins 1982; Koenning et al. 1999). These nematodes primarily damage the structures of the epidermis and cortex in the root (Lewis and Fassuliotis 1982; Lewis, 1989) and cause root stunting/shedding, foliar chlorosis, as well as severely limiting lateral root growth under heavy infestations (Kinloch 1998; Timper 2009). Soybean yield losses from the infestation of these LN species can be as high as 70% (Mueller and Sanders 1987; Noe 1993). Although the resistance of host plants is the most effective way to control plant–parasitic nematodes, efforts to identify genetic resistance for LN have been limited. Therefore, the application of field sanitation and crop rotation with non-host crops is helpful to control LN populations and reduce LN damage in soybean production areas.

Section II. Soybean resistance to oomycete diseases

Crop germination and stand are key factors for a successful cropping season for soybean growers. During seed establishment, seedlings are subject to attack by several soilborne pathogens, resulting in lack of germination, damping-off or plant death. Poor plant stands due to diseases result in replanting and increased costs. Among the soilborne pathogens impacting soybean are the oomycetes, which include Phytophthora, Pythium, and Phytopythium. The impact of these soilborne diseases is not only limited to the beginning of the season, as root infections can occur at later stages, often reducing yield without significant above ground symptoms. In 2005, losses to soybean seedling diseases in the USA were estimated at 0.89 million Mt (Wrather and Koenning 2009). From 2006 to 2009, soybean yield losses due to seedling diseases have increased considerably ranking second only to soybean cyst nematode (Koenning and Wrather 2010). There are also oomycete diseases that occur in the canopy, like downy mildew caused by [Peronospora manshurica (Naum.) Sdy.], which under conducive conditions could affect seed quality and yield (Dunleavy 1987). Key species have been recognized as major contributors in disease development and most breeding efforts have focused on minimizing impacts by Phytophthora and Pythium (Dorrance et al. 2009; Rupe et al. 2011). Recent efforts have expanded the knowledge of oomycete species causing disease on soybean, but the range of this potential species varies with the locations (Rojas et al. 2017), and among those, some species are considered emerging such as Phytophthora sansomeana E.M. Hansen & Reeser (McCoy et al. 2018).

Phytophthora root and stem rot

Phytophthora root and stem rot (PRSR) of soybean is one of the most prevalent and widely distributed soybean diseases, causing reduced yield and worldwide losses of 2.3 million Mt per year (Erwin and Ribeiro 1996; Koenning and Wrather 2010; Allen et al. 2017). Phytophthora sojae Kaufmann & Gerdemann, the main causal agent of this disease, was initially reported in the mid-1950s in the Midwest region of the USA (Kaufmann and Gerdemann 1958) and has since become a major concern for soybean production causing annual losses of approximately 1.2 million Mt in the USA (Wrather et al. 2010). P. sojae is an oomycete pathogen that survives in the soil as oospores. Under optimal conditions, oospores germinate and infect seeds and roots causing seed rot and damping-off of seedlings. P. sojae may also cause root and stem rot that results in wilting and plant death. While the typical brown to purple water-soaked lesions on the stem appear mid-late season on infected plants, early-season infection may also result in an uneven plant stand and possibly need of replanting (Bienapfl et al. 2011; Dorrance et al. 2016).

Screening of P. sojae for race identification and soybean line resistance has been based on the use of hypocotyl inoculations (Dorrance et al. 2008; Stewart and Robertson 2012; Lin et al. 2014). For P. sojae, Rps 1a, 1b, 1c, 1 k, 3a, 3b, 3c, 4, 6, 7, or 8 are part of the set of differentials, and recent surveys have tested isolates identifying emerging races. Of those, Rps1a-1 k, Rps3a, Rps6 and Rps8 are deployed through resistant cultivars. However, there are reports of resistance breakdown of Rps1 in soybean-producing states in the Midwest of USA (Dorrance et al. 2016; Matthiesen et al. 2021; McCoy et al. 2021). In lower frequency, Rps3a and Rps6 were also defeated by some isolates in the Midwest. Since not all identified resistance genes have been deployed, it is important to monitor races for future breeding efforts as some of the remaining resistance genes have also been overcome by a few field isolates (Dorrance et al. 2016; McCoy et al. 2021).

Fortunately, novel Rps genes or alleles have been identified conferring broad-spectrum resistance to P. sojae races. To date, more than 40 Rps genes or alleles have been reported worldwide (Table 5). Intriguingly, the Rps genes/alleles were not evenly distributed but were clustered on some specific chromosomes. For instance, more than half of the Rps genes/alleles (Rps1a-1 k, Rps7, Rps9, RpsUN1, RpsYD25, RpsYD29, RpsHN, RpsQ, RpsWA, RpsWY, RpsHC18, RpsX, RpsGZ, RpsDA, RpsT1, RpsT2, RpsT3, and Rps14) were mapped in a nucleotide-binding site-leucine-rich repeat (NBS-LRR) gene enriched region on Chr. 3; Six Rps genes/alleles were located on chromosomes 13 (Rps3a, Rps3b, Rps3c, Rps8, RpsSN10 and RpsCD) and 18 (Rps4, Rps5, Rps6, RpsJS, Rps12, and Rps13), respectively. The rest of Rps genes were located at chromosomes 2 (RpsZS18), 7 (Rps11), 10 (RpsSu), 16 (Rps2 and RpsUN2), 17 (Rps10), and 19 (RpsYB30) (Table 5).

Table 5 Soybean resistance genes/alleles (Rps) and validated QTL/loci conferring resistance to Phytophthora root and stem rot (caused by P. sojae and other Phytophthora spp.)

Fine mapping studies toward map-based cloning of Rps genes have also been reported. The first cloned Rps gene is the Rps1k from Williams82, from which three highly similar coiled coil (CC)-NBS-LRR genes were identified and verified through transgenic progenies (Gao et al. 2005). Unfortunately, none of these genes can be identified in any versions/sources of the Williams82 genome assemblies including unassembled contigs (Wang et al 2021). In another study, RpsUN1 and RpsUN2 were further narrowed to a 151 kb and 36 kb genomic regions using 826 F2:3 families. Expression analyses via reverse-transcription (RT)-PCR and RNA-seq suggested that Glyma.03g034600 and Glyma.16g215200/Glyma.16g214900 were high-confidence candidate genes for RpsUN1 and RpsUN2, respectively (Li et al. 2016a, b, c). Most recently, a map-based cloning study revealed that the Rps11 gene encoded a 27.7 kb NBS-LRR gene, and is derived from rounds of unequal recombination events, which resulted in promoter fusion and LRR expansion that contributed to the broad-spectrum resistance (Wang et al. 2021). More importantly, Rps11 alone can defeat 127 isolates (80% of all tested isolates) widely distributed across the USA (Ping et al. 2016; Wang et al. 2021). It is expected that commercial soybean varieties carrying the Rps11 gene will soon be available in the market.

In Phytophthora studies, pathogen inoculation methods to assess populations could also influence the outcome; For instance, hypocotyl inoculation has been a standard method to detect vertical resistance and is a premier step to exclude the influence of potential R genes before detecting horizontal resistance (Dorrance et al. 2018). On the other hand, the most commonly used methods to detect horizontal resistance to P. sojae are layer test and tray test which were based on colonized substrate to deliver the pathogen to the plant tissue (Dorrance et al. 2008; Wang et al. 2012). More recently, a hydroponic assay was developed that can detect both vertical and horizontal resistance through infection of soybean root system with zoospores (Lebreton et al. 2018). Different phenotypic traits can be collected including lesion size, root mass, shoot biomass, root scores, and corrected dry weight (CDW) (Dorrance et al. 2008; de Ronne et al. 2020, 2021). Twenty-one validated QTLs were stably identified in at least two independent studies (Table 5). These QTLs were distributed on 13 soybean chromosomes and may be of high priority to develop soybean varieties with horizontal resistance against P. sojae. Notably, qRps18-01 (formerly named QDRL-18 or OH-18–1), a major QTL conferring more than 20% of horizontal resistance (Lee et al. 2014; Karhoff et al. 2019; Rolling et al. 2020), as well as other newly identified QTLs, are being integrated into future soybean varieties through collaborated efforts. Moreover, more than 130 additional QTLs were also reported which provided diverse options for soybean breeders (Supplementary Table 1).

With respect to other Phytophthora species, P. sansomeana E.M. Hansen & Reeser is an emergent pathogen in soybean-producing areas and causes root rot diseases. Lin et al. (2021) identified and validated two QTLs that contributed horizontal resistance to this pathogen from improved soybean varieties developed at the Michigan State University soybean breeding program (Table 5). Marker-assisted resistance spectrum analysis indicated five patterns of interactions between QTLs and P. sansomeana isolates. The validated QTLs can be efficiently integrated into future soybean varieties using MAS with low linkage drag of undesirable agronomic traits, since both donor parents are improved soybean varieties.

Pythium damping-off and root rot

The genus Pythium is typically linked with early-season diseases, such as seedling root rot and damping-off, and multiple species have been implicated (Zhang et al. 1998; Zhang and Yang. 2000). Among the most damaging species, P. aphanidermatum, P. ultimum, P. irregulare, and P. sylvaticum have been used to screen potential sources of resistance for breeding efforts to reduce the impact of these pathogens (Ellis et al. 2013b; Scott et al. 2019; Lin et al. 2020; Clevinger et al. 2021). Horizontal resistance is currently the only type of resistance identified for most Pythium species, except Rpa1, which was identified from cv. ‘Archer’ as a single dominant resistance gene against P. aphanidermatum (Table 6) (Cianzio et al. 1991; Kirkpatrick et al. 2006; Bates et al. 2008; Rosso et al. 2008). The Rpa1 gene is located on Chr. 13 (molecular linkage group F, MLG F), 10.6 cM and 26.6 cM from the SSR markers Satt510 and Satt114, respectively (Rosso et al. 2008). In addition to Rpa1, two QTLs were identified for P. aphanidermatum from Archer, which were located on chromosomes 4 and 7, and accounting for 8.29–13.85% and 4.5–13.85% of phenotypic variations, respectively (Urrea et al. 2017). Moreover, Archer also confers resistance to seed rot and root discoloration caused by P. ultimum and other species of Pythium including Phytopythium. vexans (formerly Pythium vexans), P. irregulare, and hyphal swelling (HS) group (Bates et al. 2004, 2008; Kirkpatrick et al. 2006; Rupe et al. 2011), yet the genes/QTLs conferring those resistances in Archer are unclear.

Table 6 Soybean loci associated with resistance to Pythium damping-off and root rot (caused by Pythium spp.)

Horizontal resistance was also identified for other Pythium spp. Lin et al. (2020) identified and validated two QTLs for P. sylvaticum using QTL mapping and GWA methods. The two QTLs were located on chromosomes 10 and 18 and explained 9.8–11.2% and 9.3–11.3% of phenotypic variations, respectively. Remarkably, pleiotropic QTLs have been frequently identified for resistance to several Pythium species or varieties. For example, Scott et al. (2019) identified one QTL on Chr. 3 for resistance to P. ultimum var. ultimum and P.ultimum var. sporangiiferum, and other two QTLs (on chromosomes 13 and 17, respectively) that both confer resistance to P. irregulare and P. ultimum var. ultimum. In a more recent study, a major QTL was identified (nearest marker Gm08_8695745_A_C) conferring resistance to P. irregulare (16.7–24.1% of phenotypic variations), P. sylvaticum (4.9–21.4%), and P. torulosum (66.6%), and another large effect QTL (nearest marker Gm06_31863080_C_T) for resistance to P. sylvaticum (26.2–26.9%) and P. irregulare (6.1–26.6%) (Clevinger et al. 2021). In the future, these validated and pleiotropic QTLs will be of high priority in MAS to develop soybean varieties with tolerance to different Pythium pathogens.

Downy mildew

Soybean downy mildew, caused by Peronospora manshurica (Naum.) Sdy., is a common leaf disease throughout the world (Lim et al. 1989). Although severe yield loss is rarely reported, soybean downy mildew can reduce the size and quality of soybean seeds (Palmer et al. 2004; Taguchi-Shiobara et al. 2019). Three resistance genes, Rpm1, Rpm2, and Rpmx, have been reported from soybean varieties ‘Kanrich’, ‘Fayette’, and PI 88788, and ‘AGS129’, respectively, although the genetic and physical location of the resistance genes remain unclear (Geeseman et al. 1950ab, Bernard and Cremeens 1971; Lim et al. 1984; Lim 1989; Chowdhury et al. 2002). Recently, quantitative resistance to soybean downy mildew was first reported in Japan (Taguchi-Shiobara et al. 2019). Remarkably, QRpm3-1 and QRpm7-1 were identified and confirmed in several mapping populations across multiple years, each explaining 18–72% and 28–91% of the observed phenotypes (Table 7).

Table 7 Soybean loci associated with resistance to downy mildew (caused by Peronospora manshurica)

Section III. Soybean resistance to fungal diseases

Sudden death syndrome and Fusarium wilt and root rot

In the USA, Sudden Death Syndrome (SDS) was initially detected in the State of Arkansas in 1971 (Rupe and Weidemann 1986; Rupe 1989) and has since spread to the majority of soybean producing states (Hartman et al. 2016). In recent years, SDS has been detected in South Dakota (Tande et al. 2014), New York (Cummings et al. 2018), and North Dakota (Nelson et al. 2018). In Brazil, it was first observed in 1981/82 in the State of Minas Gerais (Nakajima et al. 1996). It received the name of red root rot (PVR), as it is still known in that country. This important disease also occurs in Argentina (Ploper 1993), Canada (Anderson and Tenuta 1998), Bolivia (Yorinori 1999), Paraguay (Yorinori 2002), and Uruguay (Ploper et al. 2003).

The major causal agent of SDS identified in the USA is the fungus Fusarium virguliforme O’Donnell and T. Aoki (formerly F. solani (Mart.) Sacc. f. sp. glycines) (Aoki et al. 2003), although a recent study reported that F. Brasiliense also causes SDS in the USA (Wang et al. 2019). SDS and F. virguliforme were also reported in Malaysia (Chehri et al. 2014) and South Africa (Tewoldemedhin et al. 2014). In Brazil, four fungi have been reported to cause SDS, including F. virguliforme, F. brasiliense, F. crassistipitatum, and F. tucumaniae. In addition, F. brasiliense, F. crassistipitatum, and F. tucumaniae have been reported to cause SDS in other countries in South America (Aoki et al. 2003, 2005, 2012).

Significant yield losses can occur due to SDS (Aoki et al. 2003). SDS favors cool and wet environment. The symptoms of SDS can be observed on the roots and the aboveground foliage. The fungus initiates its infestation by colonizing the soybean roots, causing root rot and necrosis, which leads to the loss of root mass and root nodules. The fungus may sporulate on the roots producing clusters of conidia that appear to be blue. The aboveground symptom of SDS is caused by the translocation of phytotoxin, the symptoms include interveinal chlorosis and necrosis; leaf abscission at the top of the petiole rather than the base; and eventually, early plant death. Foliage symptoms are generally observed in the later reproductive stages after flowering but may develop earlier (Roy et al. 1997; Aoki et al. 2003; Hartman et al. 2016; Chang et al. 2018).

Cultural practices and planting resistant varieties are the most common methods used to manage SDS (Wrather et al. 1995; Luckew et al. 2012). The soybean community has devoted substantial effort to identifying QTLs that underlie SDS resistance. To date, more than 200 resistance-associated markers have been identified (Table 8 and Supplementary Table 2). After mapping a resistance locus, it is important to confirm and incorporate it into multiple genetic backgrounds to determine whether it will maintain its effect and be useful in a breeding program. Based on the classification of Chang et al.(2018) as well as the studies thereafter, twenty-five confirmed QTLs have been identified from at least two independent studies (Table 8), including a single locus on chromosomes 2, 4, 5, 8, 9, 14, 16, and 19, two on chromosomes 3, 13, 15 and 17, and three on chromosomes 6, 18, and 20. Most of these loci were confirmed in at least one field study, except qRfv06-03, which was confirmed in three greenhouse studies (Abdelmajid et al. 2012; Bao et al. 2015; Luckew et al. 2017), and qRfv20-03, which was validated in a greenhouse study and a growth chamber study (Swaminathan et al. 2016; de Farias Neto et al. 2007). Notably, qRfv05-01 confers resistance to both F. virguliformes and F. tucumaniae, a causal agent of SDS in South America. Ninety additional loci were also reported and may need confirmation in future studies (Supplementary Table 2). The confirmed QTLs can be pyramided into elite cultivars with high confidence for durable resistance. There are no reports on genetic mechanisms of the genes but, stacking the two distinct SDS resistance mechanisms, resistance to root rot and leaf scorch is the better strategy to increase resistance (Wang et al. 2016).

Table 8 Validated loci associated with resistance to soybean sudden death syndrome (SDS) disease (caused by Fusarium virguliformes)

In addition to SDS, other Fusarium spp. pathogens (such as F. redolens, F. proliferatum, F. oxysporum, F. equiseti, F. acuminatum, F. moniliforme, F. graminearum, F. semitectum, F. chlamydosporum, F. compactum, F. merimoides, F. roseum, F. tricinctum, F. avenaceum, and F. sporotrichioides) can also infect soybean, causing wilt, damping-off, and root rot (Arias et al. 2013). Of these Fusarium spp., F. graminearum was highly aggressive (root rot severity > 90%), causing seed rot and seedling damping-off in South America, Canada, and the USA (Pioli et al. 2004; Broders et al. 2007; Xue et al. 2007; Ellis et al. 2013a; Arias et al. 2013; Cheng et al. 2017a). Horizontal resistance is the only type of resistance so far identified for F. graminearum. Since the first report of five QTLs from ‘Conrad’ and ‘Sloan’, a total of thirty QTLs have been identified, accounting for 3.1–40.2% of phenotypic variations on 13 soybean chromosomes (Table 9). Based on the physical locations of the tightly linked or flanking markers, five loci can be validated from two or more QTL mapping or GWA studies, including qRfg08-01 (17.2–47.4 Mb) and qRfg08-02 (4.0–9.2 Mb) on Chr. 8, qRfg13-01 (11.1–39.3 Mb) on Chr. 13, and qRfg19-01 (47.5–47.8 Mb) and qRfg19-02 (9.2–41.3 Mb) on Chr. 19 (Table 9). These QTLs can be of higher interest to develop resistant soybean varieties against F. graminearum.

Table 9 Soybean loci conferring resistance to Fusarium graminearum

Stem canker/Phomopsis seed decay

The Diaporthe/Phomopsis complex, the genus Diaporthe Nitschke (asexual morph Phomopsis) (Sacc.) comprises several species of fungi causing important diseases in soybean: northern and southern stem canker, Diaporthe seed decay, and pod and stem blight (Santos et al. 2011). This complex is dispersed worldwide resulting in greater yield losses in soybean than any other single fungal pathogen (Sinclair 1993). Phomopsis seed decay (PSD) is mainly caused by Phomopsis longicolla (D. longicolla), while soybean stem canker (SSC) is primarily caused by two different species, D. aspalathi (E. Jansen, Castl. & Crous) (syn. Diaporthe phaseolorum var. meridionalis) and D. caulivora (Athow & Caldwell) J.M. Santos, Vrandecic & A.J.L. Phillips (syn. Diaporthe phaseolorum var. caulivora) (Fernández et al. 1999; Pioli et al. 2003; Santos et al. 2011; Udayanga et al. 2015) and D. sojae is the cause of pod and stem blight (Udayanga et al. 2015). Recently, D. gulyae, D. bacilloides, and D. ueckerae have also been associated with soybean diseases (Mathew et al. 2018; Petrović et al. 2021).

Northern stem canker (caused by D. caulivora) was first observed in the late 1940s in the northern USA (Athow and Caldwell 1954) and resulted in severe yield losses in the mid-1950s. Hildebrand (1956) developed a greenhouse assay for stem canker which involved growing the fungus on sterilized wooden toothpicks and inserting the toothpicks into the soybean stems. Susceptible cultivars develop a canker and die, while resistant cultivars do not develop a canker symptom. Hildebrand noted that seedlings of ‘Hawkeye’ and ‘Blackhawk’ appeared resistant when inoculated, became susceptible at mid-stage, and then grew increasingly resistant as the plants matured. In the late 1990s, northern stem canker emerged as an important disease in the northern USA and Ontario, Canada (Wrather et al. 2003a). Thickett et al. (2007) developed a cut stem assay by placing inoculum on the cut surface of seedling stems which were severed above the unifoliate leaves. After two weeks, the length of the lesions was longer on the susceptible cultivars, and results agreed with field observations. To date, little has been done to elucidate the genetic resistance to D. caulivora.

Southern stem canker (caused by D. aspalathi) was first reported in the 1970s causing an estimated loss of $37 million in 1983 (Backman et al. 1985; Weaver et al. 1988). Initially identified as D. phaseolorum var. caulivora, southern isolates were noticeably different from northern isolates in culture (McGee and Biddle 1987). The name of the fungus was changed to D. phaseolorum var. meridionalis and is now D. aspalathi (Rensburg et al. 2006; Santos et al. 2011). Southern stem canker begins as a canker on the lower stem during mid-reproductive development (Weaver et al. 1988; Rupe 2016). The canker grows on one side of the stem but does not girdle the stem producing a toxin that results in distinctive foliar symptoms before prematurely killing the plant. Consistent cultivar reactions to southern stem canker were observed in the field, but the occurrence of the disease varied from year to year. Keeling (1985) reported that cultivar responses to inoculating 10-day-old seedlings with infested toothpicks were in good agreement with field ratings. The toothpick inoculation method was later used on 60-day-old field plants and compared to inoculating the plant with ascospores. Both methods consistently produced stem canker symptoms and were able to identify cultivar responses from very susceptible to very resistant (Keeling 1988). Single dominant resistance genes to southern stem canker were reported from the cultivar ‘Tracy-M’, Rdc1 and Rdc2 (later renamed Rdm1 and Rdm2, respectively) (Kilen and Hartwig 1987), in ‘Crockett’, Rdc3 (later renamed Rdm3), and in ‘Dowling’, Rdc4 (later renamed Rdm4) (Bowers et al. 1993) (Table 10). Rdc4 was also found in the cultivar ‘Hutcheson’ (Tyler 1996). Initially, all these genes appeared to be equally effective against all isolates of D. aspalathi (Keeling, 1988), but a report from Argentina isolates of D. aspalathi were found virulent on one or more of each of these genes (Pioli et al. 2003). Interestingly, they found a number of isolates of D. aspalathi that were virulent on lines with Rdc1 and lines with Rdc2 but were avirulent on Tracy-M which has both Rdc1 and Rdc2. Moderate levels of resistance to southern stem canker have been reported from the field and greenhouse inoculations, but the genetic nature of that resistance has not been explored.

Table 10 Soybean loci conferring resistance to stem canker (caused by Diaporthe aspalathi and D. caulivora) and Phomopsis seed decay (caused by D. longicolla)

Phomopsis seed decay

Phomopsis seed decay (PSD) of soybean is the major cause of poor seed quality and significant yield loss in most soybean-growing regions (Sinclair, 1993). PSD is favored by hot and humid environmental conditions and is usually worse with early maturing cultivars planted early in the season. Severe symptoms are shriveled, elongated, or cracked, chalky appearance, but seed infection is usually symptomless. These symptomless infections can result in pre- and post-emergence damping-off (Sinclair 1993; Kulik and Sinclair 1999; Koenning 2010). Resistance to PSD has been reported in PI 82264 (Walters and Caviness 1973), PI 181550 (Athow 1987), the cultivar ‘Delmar’ (Crittenden and Cole 1967; Brown et al. 1987), PI 200501, and ‘Arksoy’ (Ross 1986), and in PI 80837, PI 417479, and PI 360841 (Brown et al. 1987) (Table 10). PI 417,479 was reported to have two dominant genes for resistance to PSD, one located on linkage group F and one on linkage group H (Zimmerman and Minor 1993). The PSD resistant line, ‘MO/PSD-0259’ was developed from PI 417479 (Elmore et al. 1998; Minor et al. 1993). MO/PSD-0259 was used to develop two PSD resistant lines, ‘SS 93–6012’ and ‘SS 93–6181’ (Wrather et al. 2003b). The resistance in PI 80837 was determined to be conferred by a single dominant gene that is different from the one in MO/PSD-0259 (Jackson et al. 2005). A genetic study using a greenhouse inoculation method with progenies derived from a cross between the resistant cultivar ‘Taekwangkong’ and the susceptible cultivar ‘SS2-2’ reported two QTLs associated with PSD resistance which were tightly linked with genes for maturity (Sun et al. 2013). Many PIs in maturity groups III, IV, and V were identified as resistant to PSD across three states (Li et al. 2010a). Resistance to PSD was identified in six commercial cultivars in inoculated and non-inoculated tests (Li et al. 2017a). In a study evaluating the response of PIs to purple seed stain (PSS), nine PIs with resistance to PSS were also resistant to PSD (Li et al. 2019). PI 80837 also has resistance to both PSS and PSD (Jackson et al. 2005, 2006). A cut stem seedling assay similar to that described for inoculations with D. caulivora by Thickett et al. (2007) was used with D. longicolla (Li 2018). This method gave similar results as field tests. A draft genome sequence for D. longicolla has been published (Li et al. 2015a, 2017a), and the glycoside hydrolase subnetwork appears to be important in pathogeneses (Li et al. 2018).

Numerous management practices can be applied to control PSD, including deep tillage, crop rotation with non-legume crops, treating seeds with fungicides, and applying fungicides during pod-fill. To date, the most effective management option is the use of resistant cultivars (Park 1991; Roy et al. 1994; Jackson et al. 2005; Pathan et al. 2009; Mengistu et al. 2010). A report by Sun et al. (2013) identified two QTLs for PSD resistance associated with days to maturity in soybean (Table 10). This was an important discovery because early maturing soybean genotypes are often highly susceptible to PSD due to the weather conditions during pod and seed development. Several screening methods have been used to identify sources of resistance, including those mentioned above for stem canker, seed plate assay (Li et al. 2011), and cut-stem inoculation method.

Sclerotinia stem rot

Sclerotinia stem rot (or white mold), caused by Sclerotinia sclerotiorum (Lib.), can cause significant yield losses in soybean and overall reduction of seed quality in North Central USA and northeastern China under conducive cool and wet weather conditions (Hoffman et al. 1998; Kurle et al. 2001; Peltier et al. 2012; Sun et al. 2020). For example, in 2004 and 2009, Sclerotinia stem rot caused yield losses of 1.63 and 1.61 million Mt, respectively, in the USA alone (Peltier et al. 2012). More recently, over 1.08 million Mt of production losses were recorded in 2014 in the North Central USA and Ontario, Canada (Allen et al. 2017). The disease steadily ranked among the top 10 most destructive diseases associated with yield losses in the northern USA and Ontario, Canada (Allen et al. 2017).

Horizontal resistance is the only type of soybean resistance identified for Sclerotinia stem rot. The first report for horizontal resistance identified three minor QTLs (explaining 6.5–9.6% of phenotypic variations) on linkage groups M, K, and C2 using a bi-parental population of 152 F3 derived RILs (Kim and Diers 2000). More recently, the assembly of the soybean reference genome and advancements in GWA have enabled more accurate dissection of genomic regions associated with resistance to Sclerotinia stem rot (Schmutz et al. 2010). For example, Bastien et al. (2014) identified four significant markers for resistance, which were located at chromosomes 1, 15, 19, and 20, explaining 6.3–14.5% of phenotypic variations. The locus on Chr. 15 (renamed qRss15-01 in this review) was further validated in an F4:5 RIL population where significantly shorter lesions were observed for 24 resistant genotypes. In another GWA study, a major locus was identified and validated on Chr. 13 (Qswm13-1, and renamed qRss13-01 in this review), which explained 23.33% of phenotypic variations (Zhao et al. 2015). From 2014 to 2021, a total of nine GWA studies have been published (Bastien et al. 2014; Iquira et al. 2015; Zhao et al. 2015; Wei et al. 2017; Wen et al. 2018; Boudhrioua et al. 2020; Sun et al. 2020; Jing et al. 2021; Zou et al. 2021). Combining the studies of QTL mapping and GWA, 14 loci have been validated from at least two mapping studies (Table 11). The 14 loci were distributed at 11 chromosomes (1, 4, 6, 8, 9, 10, 12, 13, 15, 17, and 19) and contributed as high as 32% of the phenotypic variations. These validated loci may be of high priority for soybean breeders to use for improving partial resistance to Sclerotinia stem rot. In addition to the validated QTLs, more than 200 QTLs have also been identified and may be validated in the future (Supplementary Table 3),

Table 11 Validated soybean loci associated with quantitative resistance to Sclerotinia stem rot (caused by Sclerotinia sclerotiorum)

Soybean rust

Asian soybean rust (ASR) caused by Phakopsora pachyrhizi (Sydow. & Sydow.) is one of the most destructive diseases in soybean. When environmental conditions are conducive for disease development, ASR spreads fast, causing severe crop damage, leading to significant seed quality reduction and yield losses of as much as 80% (Yorinori et al. 2005). Losses vary upon weather conditions, genotype, and the maturity stage at the time of infection (Wang and Hartman 1992) and are mainly attributed to premature leaf fall, reduced green leaf area in the canopy, reduced dry matter accumulation and reduced harvest index (Kumudini et al. 2008). Soybean rust can also be caused by P. meibomiae, which resembles P. pachyrhizi in both symptoms and spore appearance. Yet the rust caused by P. meibomiae occurs mainly in South and Central America and causes little damage on soybean. This review will be focused on ASR.

ASR is primarily diagnosed with a magnifying glass or microscope, but the polymerase chain reaction (PCR) reaction is also useful when sporulating pustules are not visible (Frederick et al. 2002). The key feature of ASR is the appearance of uredinia and urediniospores. Therefore, it is recommended that infected leaf samples be incubated in a humid chamber and left overnight to enhance rust development and sporulation for accurate diagnosis.

Many management strategies have been proposed to control ASR, including cultural practices, nutrition management, biological and fungicide applications, and host genetic resistance (Tadesse 2019). The application of fungicides is the preferred management tool used by farmers in regions where ASR is prevalent, but it increases production costs and environmental footprint. Since host plant resistance appears as an affordable method for managing ASR, considerable efforts have been directed toward screening soybean germplasm for resistance to P. pachyrhizi and the development of resistant cultivars.

Resistance to ASR

Screening for reaction to ASR can be carried out in the field, in locations where the presence of inoculum and environmental conditions are appropriate for disease development, or in the greenhouse with controlled inoculations and incubation at high relative humidity (Childs et al. 2018a). In the latter case, it is necessary to collect and maintain the P. pachyrhizi isolates to be used in the inoculations. Spores can be stored in sub-zero freezers, but, as an obligate parasite, inoculum must be produced on living soybean seedlings.

Resistance to ASR in soybean plants is evaluated based on the presence or absence of lesions, color of the lesions, number of uredinia per lesion, and level of sporulation (Bromfield 1984). More recent studies have evaluated resistance using quantitative traits (Bonde et al. 2006; Walker et al. 2011; 2014). During a compatible interaction in a susceptible soybean plant, abundant sporulation and tan lesions occur, whereas in incompatible interactions (resistance), lesions are reddish-brown (RB) with less sporulation. Immune reactions (IM) have also been observed without visible lesions (Bromfield, 1984).

However, it has been pointed out that the number of uredinia per lesion and the level of sporulation are not necessarily correlated with the color of the lesion (Yamanaka et al. 2015a). Yamanaka et al. (2010) analyzed five traits including lesion color, the number of uredinia per lesion, frequency of lesions that had uredinia, frequency of open uredinia, and level of sporulation, and observed high correlations between all the traits except the color of the lesion. In this sense, Yamanaka et al. (2016) selected the number of uredinia per lesion, the frequency of lesions that had uredinia, and the level of sporulation to assess the degree of resistance.

Resistance or susceptibility studies focus on understanding the defensive response. To date, eight major resistance genes (Rpp1-7, Rpp1-b) have been mapped (Table 12) (Childs et al. 2018b; Hossain 2019). But these Rpp gene-mediated resistances against ASR have been overcome in nature several times. For example, the soybean resistance provided by Rpp1 and Rpp3 was defeated by the P. pachyrhizi MT isolate only two years after ASR was first detected in Brazil (Pierozzi et al. 2008).

Table 12 Soybean loci conferring resistance to soybean rust (caused by Phakopsora pachyrhizi)

The improvement effort to know the physical location of the Rpp genes (resistance to P. pachyrhizi) is a great challenge today. However, despite the publication of the soybean genome (Schmutz et al. 2010), no Rpp gene has yet been cloned. For this reason, other authors have tried to identify the candidate genes linked to the Rpp3 gene through a massive transcriptomic approach, using NILs populations. These genes are mostly related to phenylpropanoid branch isoflavonoid pathway-specific phytoalexin, glyceollin biosynthesis (Hossain 2019).

The presence of multiple virulence genes in the pathogen population and the lack of multiple resistance genes in the host give the soybean rust pathogen a competitive advantage. Therefore, the deployment of specific single genes for resistance is unlikely to be a successful strategy (Jarvie 2009).

Although varieties with pathotype-specific resistance genes were released, the stability of this resistance is uncertain since the large number of races of this fungus already described demonstrates the great variability of the pathogen. Understanding the molecular mechanisms involved in defense responses is of primary importance to plan strategies to control stress and, consequently, to increase the adaptation of plants to limiting conditions. Molecular markers have been considered tools for a large number of applications ranging from the location of a gene to the improvement of plant varieties through MAS. Also, the analysis of the soybean genome has generated a large amount of information and several databases with molecular markers are being generated that could be used for genetic improvement (Vuong et al. 2016; Tadesse 2019).

Strategies for ASR resistance

The introgression of vertical resistance through classical breeding followed by MAS allows the development of resistant varieties and their use as an efficient and cost-effective method to control soybean rust (Tadesse 2019). An example to highlight is the pyramiding of several Rpps genes in a single line. Yamanaka et al. (2015a, b) managed to develop highly resistant experimental lines with stacks of three genes: Rpp2 + Rpp3 + Rpp4 and Rpp2 + Rpp4 + Rpp5.

Pathotype-specific resistance genes and molecular markers are known to facilitate selections. However, the resistance provided by major genes tend to be broken rapidly; thus, research should be focused on the role of quantitative minor genes (QTLs) which are more likely to provide durable resistance to this highly variable pathogen.

To date, only one attempt to enhance resistance ASR based on transgenic technology has been recorded (Soto et al. 2020). In this study, constitutive expression of the NmDef02 gene from Nicotiana magalosiphon demonstrated significantly increased resistance in soybean against Phakopsora pachyrhizi in field experiments.

The most recent and novel attempt to control this disease is the treatment of liquid suspension of cellulose nanofibers (CNF) to plants before inoculation with the pathogen. The authors suggest that this application changes the hydrophobicity of the leaf surface, suppressing P. pachyrhizi CHSs (chitin synthases) expression related to chitin formation, which are associated with reduced formation of pre-infection structures (Saito et al. 2021).

Frogeye leaf spot, Cercospora leaf blight and purple seed stain

There are three soybean diseases caused by Cercospora spp.: frogeye leaf spot (FLS), Cercospora leaf blight (CLB), and purple seed stain (PSS). FLS, caused by C. sojina Hara, is an important foliar disease in soybean in the USA, Brazil, and China (Laviolette et al. 1970; Bernaux 1979; Dashiell and Akem 1991; Akem and Dashiell 1994; Ma 1994; Mian et al. 1998). Symptoms start on leaves as small, light brown circular spots which develop into a darkish brown to reddish margin (Dashiell 1991). In addition to foliar symptoms, C. sojina can cause lesions on pods and infect soybean seeds. FLS is favored by warm temperatures and frequent rainfalls (Phillips 1999) and remains active throughout the growing season (Laviolette et al. 1970; Kim et al. 2013), which make FLS a major disease in the southern USA as well as in some regions of the Midwestern USA (Yang et al. 2001; Mengistu et al. 2002; Mian et al. 2008). Yield losses can range from 10 to 60% mainly due to the reduction in photosynthesis and leaf area by necrotic lesions and/or premature defoliation (Laviolette et al. 1970; Bernaux 1979; Dashiell and Akem 1991; Akem and Dashiell 1994; Ma 1994; Mian et al. 1998). Screening methods for FLS include field evaluations with natural inoculum or with inoculations, and greenhouse inoculations of seedlings (Mian et al. 2008; Mengistu et al. 2012). Mian et al. (2008) proposed a set of 12 differential cultivars to determine races of C. sojina. With these differentials, they described 11 races from a collection of 93 C. sojina isolates collected in the USA. Three resistance genes (Rcs, Resistant to C. sojina) have been identified including Rcs1, Rcs2, and Rcs3 (Table 13) (Athow and Probst, 1952; Athow et al. 1962; Phillips and Boerma 1982). Rcs3 appears to confer resistance to all known races of C. sojina in the USA. Rcs3 was further fine mapped on Chr. 16 (MLG J) (Mian et al. 1999; Missaoui et al. 2007a, b). In recent years, Rcs(PI 594891) and Rcs(PI 594774) were fine mapped and approved by the Soybean Genetic Committee as QTL that confers resistance to FLS (Hoskin 2011; Pham et al. 2015); In addition, two major QTLs were mapped on chromosomes 6 and 8, respectively, conferring resistance to C. sojina race 2 (ATCC 44,531) (Sharma and Lightfoot 2014); Rcs15-02 was mapped on Chr. 6 (MLG C2); the ss715594329—ss715594474 interval was mapped on chromosome 6 (MLG C2) (Smith 2021); the ss715610717—ss715610843 interval was mapped on chromosome 11 (MLG B1)(Smith 2021); the ss715614578—ss715615158 interval was mapped on chromosome 13 (MLG F) (McAllister et al. 2021); and Rcs15-01 was mapped on Chr. 19 (MLG L) (Lee 2021).

Table 13 Soybean loci conferring resistance to frogeye leaf spot (caused by Cercospora sojina) and Cercospora leaf blight/purple seed stain

CLB and PSS are two closely related diseases caused by the same or similar pathogens. The causal agent of both CLB and PSS was identified as Cercospora kikuchii (Matsumoto & Tomoyasu) M. W. Gardner (Matsumoto and Tomoyasu 1925; Walters 1980); however, recent studies have found C. flagellaris and C. sigsbeckiae were the primary species associated with both diseases in the southern USA. CLB begins as a purpling of the upper leaves starting during seed development. This purpling can cover the entire leaf surface. Symptoms can advance to blighting where the entire leaf becomes chlorotic and necrotic with the leaflets falling off leaving the petioles attached. The pathogen produces a toxin, ‘cercosporin’, whose production requires light exposure. As a result, CLB symptoms begin at the upper end top of the plant and progress to the lower leaves. In severe cases, the whole plant may be defoliated. Yield losses for PSS have been estimated at 0.12–0.28 million Mt (Allen et al. 2017) whereas CLB causes an estimated yield loss of 23% in the USA (Wrather et al. 1997). On seed, infection causes a purpling of the seed coat. Seed infection is usually not associated with yield loss but can reduce seed germination and may lead to infected seedlings. Although both CLB and PSS are favored by high moisture and warm temperatures during early pod development (Jones 1968; Schuh 1990), the occurrence of these diseases appears to be independent of each other (Orth and Schuh, 1994; Walters 1985).

Based on natural field inoculum, Srisombun and Supapornhemin (1993) reported resistance to PSS in the soybean cultivar ‘SJ2’ and that this resistance may be due to a single dominant gene. Resistance to PSS was also reported in PI 80837, PI 417274, PI 417460, and the cultivar ‘Gnome’ (Wilcox et al. 1975; Ploper et al. 1992). The resistance in PI 80837 was attributed to a single gene on linkage group G, Rpss1 (Jackson et al. 2006, 2008) (Table 13). Additional PIs were identified as resistant sources to both CLB and PSS (Alloatti et al. 2015) or only to PSS (Li et al. 2019). Several studies of population genetics have found differences in genetic structure among populations and pathogenicity of groups throughout the Americas (Almeida et al. 2005; Cai et al.2009; Lurá et al. 2011). It is unknown if the reactions of these soybean lines to CLB and PSS will remain consistent with the new species of Cercospora associated with these diseases.

Charcoal rot

The worldwide distributed charcoal rot disease of soybean is caused by Macrophomina phaseolina (Tassi) Goid (Smith and Wyllie 1999). M. phaseolina is a soilborne plant pathogen causing disease infection in more than 500 plant species (Su et al. 2001; Mengistu et al. 2007). Charcoal rot is one of the primary diseases of soybean in the USA and Canada (Bandara et al. 2020; Roth et al. 2020) resulting estimated yield losses between 0.73 and 2.0 million Mt from 2010 to 2014 (Allen et al. 2017). Disease severity is favored by the increase in soil and air temperature (28–35 °C) (Mengistu et al. 2014), and symptoms include stunted growth, leaf chlorosis, premature yellowing and early maturation, or incomplete pod filling (Gupta et al. 2012; Mengistu et al. 2016). Management strategies include crop rotation with non-host crops, such as cotton, wheat, and barley that can lower inoculum load in the soil, and avoidance of water stress especially during the reproductive stage of soybeans. (Almeida et al. 2003; García‐Olivares et al. 2012; Vibha 2016). Biological control with Trichoderma isolates has been proposed by researchers as a possible alternative to control charcoal rot (Khalili et al. 2016; Orojnia et al. 2021). However, host plant resistance is the most viable method to control the disease (Mengistu et al. 2011; Coser et al. 2017). Little is known regarding the genetics and heritability of the pathogen and there is a lack of reliable and efficient screening method for this disease (Mengistu et al. 2008). Until 2018, no soybean genotype having a high level of resistance to M. phaseolina had been identified (Mengistu et al. 2018). Recently, a report by Nataraj et al. (2019), summarized eleven soybean genotypes identified as moderately resistant to charcoal rot along with pedigree information. Reznikov et al. (2019) found that cv. ‘Munasqa RR’ carried superior resistance to M. phaseolina. In addition, the University of Missouri-Fisher Delta Research Center has released varieties showing superior resistance to charcoal rot (Chen et al. 2020, 2021b). Based on field research studies conducted over the last several years, over 2,000 soybean genotypes have been screened for CR resistance, and of these genotypes, approximately 25 have been identified as having moderate resistance against charcoal rot (Mengistu et al. 2007, 2011, 2013). Recently, Mengistu et al. (2021) screened a set of 120 soybean accessions known to have resistance to one or more races of SCN. Twelve of these accessions have been identified to have moderate charcoal rot resistance combined with resistance to SCN. These accessions are archived and will be available through the Germplasm Resources Information Network (GRIN) system of the USDA. Even though moderately resistant cultivars have been identified, the lack of identifying a complete resistance has delayed the progress to better understanding the genetics of resistance. Most of those genotypes were screened using at least one of the six screening methods for the disease assessment including: colony-forming unit index (CFUI); root stem severity (RSS); percent height of stem discoloration (PHSD); foliar symptoms (FS); cut-stem inoculation method; and seed plate assay (SPA) (Mengistu et al. 2007; Twizeyimana et al. 2012; da Silva et al. 2019). Of all these methods, CFUI and RSS have been the stay methods for charcoal rot assessment currently used in the field.

Recently, QTL mapping and GWA studies were reported on multiple genomic regions harboring horizontal resistance to charcoal rot in soybean, which may be used to facilitate breeding and MAS against this pathogen (Table 14) (Coser et al. 2017; da Silva et al. 2019, 2020; Ghorbanipour et al. 2019). More efforts are needed to identify complete resistant sources and develop tightly linked molecular markers to facilitate breeding resistant varieties.

Table 14 Soybean loci conferring resistance to charcoal rot (caused by Macrophomina phaseolina)

Brown stem rot

Brown stem rot (BSR) is a devastating soybean disease caused by a soilborne fungus, Phialophora gregata (syn. Cadophora gregata), which was first discovered in central Illinois in 1944 (Allington and Chamberlain 1948; Harrington and McNew 2003). There are two different types of P. gregata pathogen identified (Type I and II): Type I causes pith browning and interveinal chlorosis and necrosis of leaves, but Type II only causes pith browning (Gray 1972; Harrington et al. 2003). The disease caused annual yield loss of 0.35 million Mt in the Northern USA (Allen et al. 2017; Klos et al. 2000), and yield reduction can reach as high as 38% (Bachman et al. 2001). The most effective strategy to control BSR is the introgression of resistance genes into soybean cultivars (Klos et al. 2000; McCabe and Graham 2020). From previous studies, three genes (Rbs1, Rbs2, and Rbs3) for BSR resistance in soybean have been identified through allelism tests (Table 15) (Hanson et al. 1988; Willmot and Nickell 1989). Later, it was determined that all three genetic loci were in an overlapping region of Chr. 16 (28.9–36.2 Mb) (Lewers et al. 1999; Bachman et al. 2001). Recently, Rincker et al. (2016a) concluded that all three loci for BSR resistance were located in the same region, and that the resistance was conferred by a single gene based on their fine mapping (Rincker et al. 2016a) and GWA studies (Rincker et al. 2016b). To evaluate BSR resistance in soybean, Sebastian et al. (1983) established a greenhouse root-dip method, which has been modified and refined by further studies (Hanson et al. 1988; Willmot and Nickell 1989; Lewers et al. 1999; Bachman et al. 2001). Soybean PIs that have BSR resistance include PI 84946–2, PI 86150, PI 90238, PI 95769, PI 88820, PI 424285A, PI 424353, PI 424611A, PI 437833, and PI 437970 (Chamberlain and Bernard 1968; Tachibana and Card 1972; Hanson et al. 1988; Nelson et al. 1989; Wilmot and Nickell 1989).

Table 15 Soybean loci conferring resistance to brown stem rot (caused by Phialophora gregata)

Rhizoctonia damping-off and root rot

Rhizoctonia damping-off and root rot is an important disease in soybean and can cause pre- and postemergence damping-off, seed rot, root rot, hypocotyl lesions, and web blight (Dorrance et al. 2003; Rahman et al. 2020). The causal agent, Rhizoctonia solani Kuhn, is a soilborne necrotrophic complex species that can host corn, soybean, and other crops such as wheat and potato, suggesting that management of Rhizoctonia root rot by rotations between these crops may not be effective (Ajayi-Oyetunde and Bradley 2017, 2018). The isolates of R. solani can be classified into 14 anastomosis groups (AGs) and more subgroups based on their genetic similarity. Different AGs may incite different symptoms of disease on soybean. For example, AG-2-2IIIB, AG-4 and AG-5 can cause seed rot, pre- and post-emergence damping-off, hypocotyl and root rot, and foliar blight on soybean, while AG-3, AG-7, and AG-11 cause very little damage (Ajayi-Oyetunde and Bradley 2018). The management of Rhizoctonia root rot may include clean seeds, tillage, fungicides, and deployment of resistant cultivars if possible. Unfortunately, currently there is no commercial resistant cultivars available to the market, and the genetic research against Rhizoctonia root rot is inadequate. Only three SSR markers, Satt281, Satt177, and Satt245 (Table 16) have been found associated with partial resistance to AG-4 isolate (Zhao et al. 2005), although more germplasm lines and soybean varieties have been identified as potential sources of resistance (Muyolo et al. 1993; Bradley et al. 2001; Sharma 2020).

Table 16 soybean loci conferring resistance to Rhizoctonia damping off and root rot (caused by Rhizoctonia solani)

Other fungal diseases

Taproot decline

Taproot decline is a disease caused by Xylaria necrophora sp. nov. (Garcia-Aroca et al. 2021), a recently identified pathogen that was overlooked since some of the symptoms were similar to other soybean root diseases including SDS and charcoal rot. This soilborne pathogen can affect seedlings; however, the symptoms in the field develop later in the season producing interveinal chlorosis followed by necrosis. It has been noted that X. necrophora will affect the root to the point that pulling plants from the ground causes the root system to break with black stroma visible on the root tissue (Allen et al. 2017). The disease is mostly managed with cultural practices, but cultivar trials are ongoing. The cv. ‘Osage’ (PI 648270) has tolerance to this pathogen (Purvis 2019). Osage was developed in Arkansas and also has resistance to SDS, stem canker, and frogeye leaf spot (Chen et al. 2007).

Red leaf blotch

Red leaf blotch affects soybean plants in several Eastern, Central, and Southern African countries. The disease (also known as Pyrenochaeta leaf spot or blotch, and Dactuliophora leaf spot) can cause yield losses of up to 50% (Hartman et al. 1987, 2016). The causal agent is Coniothyrium glycines (R.B. Stewart) Verkley & Gruyter, a fungus previously named Phoma glycinicola, Dactuliochaeta glycines, Dactuliophora glycines, and Pyrenochaeta glycines. The disease affects foliage, petioles, pods, and stems, and may cause severe leaf blotching, defoliation, and premature senescence. Because of the potential negative consequences of this disease to US agriculture if introduced, C. glycines is listed as a select agent by the Federal Select Agent Program (Tooley 2017).

Since the 1980s, soybean germplasm has been evaluated under field conditions in African countries for reaction to red leaf blotch. Despite this extensive field testing, no sources of resistance have yet been identified among US soybean commercial cultivars, local lines, or exotic soybean lines. These evaluations were carried out in regions where red leaf blotch is endemic (Sinclair 1989). A field method to assess the infection of soybean by the pathogen was developed and used to evaluate cultivar reaction and efficacy of chemical control (Levy et al. 1990).

A seedling inoculation method has also been proposed which allows optimal infection in less space over a shorter period than field trials and without relying on the occurrence of natural inoculum and disease conducive environmental conditions. Soybean genotypes that represent nearly 90% of the genes present in US soybean were evaluated and found to be susceptible, which is consistent with previous field evaluations (Tooley 2017).

Studies are necessary to evaluate genetic variability within the pathogen population from different countries, and to assess potential interactions with soybean genotypes. With limited genomic information of the pathogens known, there are no molecular genotyping or detection methods available. Recently, the draft genome sequences of three C. glycines isolates were reported, enhancing the knowledge of this species (Blagden et al. 2019).

Section IV Soybean resistance to bacterial diseases

Bacterial blight

Soybean bacterial blight caused by Pseudomonas savastanoi pv. Glycinea Coerper (formerly Pseudomonas syringae pv. glycinea) is a widespread soybean disease. Although bacterial blight is not a major suppressor of soybean yield in the USA (Williams and Nyvall 1980; Hwang and Lim 1992), the interaction between soybean and the pathogen was well known as a model system to study gene-for-gene host-parasite relationships (Huynh et al. 1989). Five resistance genes/alleles have been identified named Rpg1-b, Rpg1-r, Rpg2, Rpg3, and Rpg4, conferring resistance to the corresponding Psg avirulence factors AvrB, AvrRpm1, AvrA, AvrC, and AvrD, respectively (Staskawicz et al. 1987; Keen and Buzzell. 1991; Ashfield et al. 1998; Khan et al. 2011; Whitham et al. 2016). The Rpg1-b and Rpg1-r genes were located on MLG F (Chr. 13) (Ashfield et al. 1998) and have been cloned in 2004 and 2014, respectively (Ashfield et al. 2004, 2014). Rpg2 is loosely linked with Rpg1, and Rpg3 is linked with Rpg4 at 40.5 ± 3.2 recombination units (Table 17) (Keen and Buzzel. 1991).

Table 17 Soybean genes/loci conferring resistance to bacteria diseases

Bacterial pustule

Soybean bacterial pustule is a common disease in regions with warm and wet conditions (Bernard and Weiss 1973; Kennedy and Tachibana 1973; Matsuo et al. 2017). The causal agent, Xanthomonas axonopodis pv. glycines, can cause small, pale green spots with elevated pustules in the center of lesions, which can grow into large necrotic lesions causing premature defoliation (Kennedy and Tachibana 1973; Narvel et al. 2001). The first identified resistance gene is rxp from cv. ‘CNS’ and was initially mapped between Satt014 and Satt372 on MLG D2 (Chr. 17) (Feaster 1951; Hartwig and Lehman 1951; Bernard and Weiss 1973; Hwang and Kim 1987; Palmer et al. 1992; Narvel et al. 2001). Further studies narrowed the rxp locus down to a 33 kb genomic region between markers SNUSSR17_9 and SNUSNP17_12, with two candidate genes identified (Kim et al. 2010). In addition, another single recessive resistance gene was identified from PI 96188. The gene was located on MLG O (Chr. 10) and was closely linked with Sat_108 (Kim et al. 2011). QTLs have also been reported against bacterial pustule (Van et al. 2004; Seo et al. 2009; Chang et al. 2016). For example, Seo et al. (2009) reported four QTLs on chromosomes 9, 14, 17 and 20, explaining 2.7–20.9% of phenotypic variations (Table 17).

Section V Soybean resistance to virus diseases

Soybean mosaic virus

Soybean mosaic virus (SMV) is a major global viral pathogen in soybean that can compromise the soybean value chain by causing expressive yield losses of up to 90% in severe outbreaks (Ren et al. 1997a; Wang et al. 2001). SMV is widely distributed in soybean-growing countries including Brazil, Canada, China, Japan, Korea, and the USA (Cho and Goodman 1979; Li et al. 2010b, 2015b). In China, the occurrence of SMV is gradually increasing throughout the country and it currently represents the most prevalent disease in soybean with annual yield losses reaching over 50% (Zhang et al. 1980, 2015b). Typical SMV symptoms include reduced seedling viability and vigor, flower abortion, reduction of pod set, seed number, and seed size (Hill et al. 1987; Ren et al. 1997b; Gunduz et al. 2004). The severity of the symptoms is dependent on the host genotype, virus strain, plant stage at infection, as well as environmental factors (Bos 1972).

SMV is classified into strains based on its virulence and observed symptoms and differs between countries. In the USA, SMV isolates are classified into seven strains (G1–G7), where G1 is the least virulent affecting only susceptible genotypes whereas G7 is the most virulent capable of infecting both resistant and susceptible soybean genotypes (Cho and Goodman 1979). In China, SMV is classified into 21 groups (SC1–SC21) according to geographical regions and individual genotypes responses (Moon et al. 2009; Li et al. 2010b). Genetic resistance is the most efficient strategy to control SMV (Gunduz et al. 2004). To date, four independent loci for SMV resistance, Rsv1, Rsv3, Rsv4, and Rsv5 have been identified (Kiihl and Hartwig 1979; Buzzell and Tu 1984; Buss et al. 1997; Li et al. 2010c; Klepadlo et al. 2017) although most of the modern commercial cultivars are susceptible to SMV, particularly to more virulent strains (Table 18) (Zheng et al. 2005a, b; Shakiba et al. 2012a).

Table 18 Soybean loci conferring resistance to soybean mosaic virus (SMV)

Rsv1 is the first SMV resistance locus identified and was mapped on Chr. 13 (MLG F). It represents the most common resistance locus in soybean germplasm (Kiihl and Hartwig 1979), conferring resistance to less virulent strains (G1-G3) and susceptibility to more virulent strains (G5-G7). A total of ten unique alleles have been identified including Rsv1, Rsv1-t, Rsv1-y, Rsv1-m, Rsv1-k, Rsv1-r, Rsv1-s, Rsv1-n, Rsv1-h, and Rsv1-c (Kiihl and Hartwig 1979; Roane et al. 1983; Chen et al. 1991, 2001, 2002; Shakiba et al. 2013). Rsv3 was mapped on Chr. 14 (MLG B2) and confers resistance to more virulent strains (G5-G7) while susceptible to less virulent strains (G1–G4) (Tu and Buzzell 1987). The Rsv3 locus contains at least six alleles identified in ‘OX686’, ‘Harosoy’, ‘L29’, PI 61944, PI 61947, and PI 399091 (Buzzell and Tu 1989; Buss et al. 1999; Gunduz et al. 2001; Shakiba et al. 2012b; Cervantes-Martinez et al. 2015). Rsv4 was mapped on Chr. 2 (MLG D1b) and confers complete resistance to all SVM strains (Buss et al. 1997; Ma et al. 2002; Gunduz et al. 2004). A total of four alleles have been identified from ‘V94-5152’, PI 88788, and ‘Beeson’ (Buss et al. 1997; Ma et al. 2002; Gunduz et al. 2004; Shakiba et al. 2013). Since the reaction (hypersensitive reaction) observed in Rsv1 and Rsv3 is different from that in Rsv4, it is suggested that Rsv4 has unique molecular defense mechanisms (Ma et al. 2002; Gunduz et al. 2004; Saghai Maroof et al. 2008). Recently, Klepadlo et al. (2017) suggested that Rsv1-y should be named as an independent locus Rsv5 because of segregation in resistance to SMV in progenies derived from PI 96983 (Rsv1) and ‘York’ (Rsv1-y).

In addition to Rsv1, Rsv3, Rsv4, and Rsv5, several other genes named Rsc5 (Karthikeyan et al. 2017), Rsc7 (Yan et al. 2015), and Rsc8 (Zhao et al. 2016) have been mapped on Chr. 2 (MLG D1b), and Rsc3 (Yang et al. 2013), Rsc14Q (Ma et al. 2011) and Rsc15ZH (Li et al. 2020) on Chr. 13 (LG F) for resistance to Chinese SMV strains. Due to differences in SMV strain classification systems between USA and China, likely Rsc3, Rsc14Q and Rsc15ZH share the same locus of Rsv1 whereas Rsc5, Rsc7, Rsc8 share the same locus as Rsv4 (Table 18). Although rare, the combination of the four resistance loci is naturally available in soybean genotypes and can be achieved through gene pyramiding. Combining multiple resistance genes may provide more effective and durable resistance and minimize the occurrence of resistance-breaking emerging populations.

Alfalfa mosaic virus

Alfalfa mosaic virus (AMV) is a member of the genus Alfamovirus in the family Bromoviridae. It has a worldwide distribution and infects more than 600 species in 22 dicotyledonous families, including agriculturally valuable crops such as alfalfa, tomato, lettuce, potato, soybean, and common bean. AMV is transmitted by more than 15 species of aphids, including the soybean aphid [Aphis glycines Matsumura (Hemiptera: Aphididae)], in a nonpersistent manner. It is also transmitted by mechanical inoculation and in some species, such as alfalfa and in reduced values in soybean, through the seed (Truol et al. 1985; Clark and Perry 2002; Hartman et al. 2016). Seed transmissibility was proven to be virus strain and host genotype-dependent in soybean (He et al. 2010).

AMV is known as a very complex virus which has four bacilliform particles, elongated with rounded ends. The particles are 18 nm in diameter and 30, 34, 43, and 56 nm in length. The viral genome consists of three single strands of RNA (2.0, 2.6, and 3.6 kb in length) and a fourth sub-genomic RNA, known as RNA 4 encoding the coat protein (Hartman et al. 2016; Loesch-Fries 2021).

Symptoms caused by AMV in soybean range from mosaic to mottle patterns of contrasting mixes of bright yellow and dark. It is often referred to as a calico or flashy mosaic. Leaf malformation, stunting, reduced pod set, and seed coat mottling have also been mentioned. Depending upon soybean genotype, environmental conditions and strain of the virus involved, symptoms can either persist or disappear in the new tissues of infected plants (Mueller et al. 2007; Hartman et al. 2016).

Synergism between AMV and SMV has been reported. AMV symptoms are more severe and persist throughout the season in plants infected by both viruses. The observation that co-infection of AMV and SMV results in disease synergism suggests enhancement of potential that AMV may become a serious viral disease of soybean (Malapi-Nelson et al. 2009).

Recommended management strategies include selection of resistant cultivars and the use of clean virus-free seed. Resistance to AMV in the Brazilian cultivars ‘Pérola’ and ‘Planalto’ and their common ancestor ‘Hood’ was reported to be controlled by a single dominant gene (Almeida et al. 1982). Two cultivars, ‘Wuyuezha’ and ‘Baimaodou’, were described as tolerant in China (Che et al. 2020). In the USA, resistance to AMV was found in PI 153282. Genetic studies revealed the existence of one dominant gene, which was named Rav1, and DNA marker analysis allowed its location on a genetic map (Kopisch-Obuch et al. 2008) (Table 19).

Table 19 Soybean loci conferring resistance to other soybean viruses

Bean pod mottle virus

Bean pod mottle virus (BPMV), a member of genus Comovirus in the family Comoviridae, is a major viral pathogen of soybean first identified in Arkansas in 1951 (Walters 1958). The adult bean leaf beetle, Cerotoma trifurcate Forster (Coleoptera: Chrysomelidae), has been known as a main vector of BPMV, but it is also a destructive insect feeding on leaves, stems, and pods in soybean production regions in the USA (Pedigo and Zeiss 1996; Giesler et al. 2002). Plant responses to this pathogen can range from mild chlorotic mottling to severe mosaic on younger soybean leaves co-occurring with green stem symptoms (Giesler et al. 2002; Zheng et al. 2005b; Rodriguez and Thiessen 2020). BPMV can also cause plant stunting, leaf distortion, wilting, and reduced pods per plant and seed size and quality under severe infection (Myhre et al. 1973; Schwenk and Nickell 1980; Giesler et al. 2002). Soybean yield reductions resulting from BPMV infection have been reported as high as 52% (Hopkins and Mueller 1984; Gergerich 1999), and it can be maximized by the infection before V6 stage (Fehr et al. 1971) or the co-infections with soybean mosaic virus (Ross 1968; Rodriguez and Thiessen, 2020). Although Ross (1986) developed and released four BPMV-resistant soybean germplasm lines, these lines showed mild symptoms with systemic infections, and there is still no commercial soybean variety with BPMV resistance (Zheng et al. 2005b; Rodriguez and Thiessen 2020). Genetic loci for BPMV resistance have not been thoroughly investigated in soybean, but several studies have successfully engineered BPMV resistance in transgenic soybean plants by overexpressing ds-specific ribonuclease gene PAC1 (RNase III family) from Schizosaccharomyces pombe and RNAi-based strategies (Reddy et al. 2001; Zhang et al. 2011; Yang et al. 2019). However, with previously identified 15 G. soja and 12 G. tomentella lines showing tolerance with mild symptoms or no systematic infection to BPMV, soybean breeders may want to incorporate those useful genetic sources into G. max by interspecific crosses for further investigation in specific loci and molecular marker development in soybean (Zheng et al. 2005b). The virus infection assay for BPMV was well described by Zheng et al. (2005b) using four diverse isolates (K-G7, K-Ha1, K-Ho1, and AR) and enzyme-linked immunosorbent assay (ELISA).

Soybean vein necrotic virus

Soybean vein necrosis virus (SVNV) was first reported in Arkansas and Tennessee in 2008 (Tzanetakis et al. 2009) and is now found in 22 states in the USA as well as in Canada and Egypt (Zhou 2012; Ali and Abdalla 2013; Conner et al. 2013; Han et al. 2013; Jacobs et al. 2013; Smith et al. 2013; Kleczewski 2016; Abd El-Wahab and El-Shazly 2017; Escalante et al. 2018). It is now the most prevalent virus in North America (Zhou and Tzanetakis 2013). Symptoms caused by SVNV begin as clearing of the main leaflet veins that progressively become necrotic. When severe, these symptoms can expand to encompass the entire leaflet (Tzanetakis et al. 2009). Seeds of plants infected by SVNV can have lower oil and protein content (Groves et al. 2016; Anderson et al. 2017), with higher levels of linoleic acid and lower levels of oleic acid (Anderson et al. 2017). It is not known if the virus reduces overall yields. There is evidence of seed transmission (Groves et al. 2016), but SVNV is vectored primarily by thrips which transmit SVNV in a persistent and propagative manner (Zhou et al. 2013). The primary thrips vector is Neohydatothrips variabilis, but the thrips Frankliniella tirtici and F. fusca also transmit the virus at lower rates (Zhou et al. 2018).

Two studies have identified resistance related to SVNV. Zhou et al. (2020) compared the feeding preferences of N. variabilis on 11 soybean accessions and suggested breeders consider PI 547422 as a source of resistance. In a more recent study, seven soybean genotypes were inoculated under controlled conditions using SVNV-infected thirps (N. variabilis), and their results suggested that the genotypes ‘51–23’, ‘91–38’, and ‘SSR51-70’ were resistant to SVNV and 51–23 was tolerant (some symptom development, but very low virus titer) (Zambrana-Echevarria 2021).

An alternative mechanism to control SVNV is blocking the vector-virus interaction via synthetic glycopeptides that compete with SVNV glycopeptides to reduce transmission of SVNV by N. variabilis (Zhou and Tzanetakis 2020). These peptides reduced the transmission of SVNV by at least 50% (Zhou and Tzanetakis 2020).

Soybean dwarf virus

Soybean dwarf virus (SbDV) was first noticed in Hokkaido in 1969 and remains a major soybean yield suppressor in northern Japan (Tamada et al. 1969; Harrison et al. 2005). The symptoms of SbDV include dwarfing (stunting), downward curling, rugosity, and interveinal yellowing of the leaves. Rsdv1 is the only gene known to confer major resistance to SbDV (Uchibori et al. 2009; Yamashita et al. 2013). Another gene, Raso1, was found conferring resistance to foxglove aphid, a transmission vector of SbDV, but a further study indicated that Raso1 needs at least one additional gene for resistance to SbDV (Table 19) (Ohnishi et al. 2012).

Conclusions and future perspectives

With the identification and implementation of molecular markers tightly linked with resistance genes, the introgression of vertical resistance through MAS became a practice routinely performed by public and private soybean breeding programs. Efforts to understand minor genes with small but accumulative effects for horizontal resistance will also be needed. What’s more, to expand the sources of resistance and discover resistance genes and QTLs to ensure the sustainability of soybean production, continuous efforts are needed to screen diverse germplasm lines. For example, the USDA Germplasm Collection (GRIN) provides more than 20,000 soybeans accessions worldwide and more resistance sources can be expected to be identified. Germplasm lines and elite soybean cultivars with resistance to multiple diseases combined with high-yielding potential and desired agronomic traits are being developed. In addition, interaction among resistance loci, allelic and copy number variations, their interactions with environment, and impact on virulence of pathogens and disease development deserve close attention in future research.

Advances in genomics facilitated the introduction of next generation sequencing (NGS)-based high-density molecular markers which are quickly evolved and became available at an accessible cost for both public and private breeding programs (Song et al. 2013, 2020). Genome-wide studies revealed many novel regions of the soybean genome significantly associated with resistance to different pathogens, and traits that were often considered qualitative in nature evolved to some extent into quantitative traits with major and minor alleles with small effects contributing to the observed phenotypes. The rise of digitally smart-agriculture and the application of machine learning and artificial intelligence for characterizing the response of breeding lines to specific diseases represented another breakthrough in breeding for genetic resistance. Disease assessment screening protocols often reported on categorical scales based on subjective ratings are gradually being replaced by precise quantitative metrics representing the observed phenotypes (Gazala et al. 2013; Khalili et al. 2020; Gui et al. 2021; Liu et al. 2021). In combination with advanced predictive analytics and mega environmental data, one can predict the response of soybean breeding lines to specific or multiple diseases in diverse environments, which can be a powerful tool to anticipate the deployment of resistant cultivars to potential disease outbreaks and extreme environmental conditions.

Throughout this review, the impact of pathogens in global soybean production and their respective yield losses have been discussed. Substantial yearly production losses in the order of billions of dollars due to diseases have been repeatedly reported in the literature for decades (Wrather et al. 1997; 2001; Allen et al. 2017; Savary et al. 2019; Bandara et al. 2020). Genetic resistance is the most effective and sustainable approach for the disease management in soybean globally, representing a critical pillar bolstering the global soybean value chain and food security. Although hundreds of significant genomic regions conferring resistance to multiple pathogens have been reported in this review, there are many components of genetic resistance still to be enlightened and continuously investigated. For instance, limited advancements have been achieved in understanding the pathogen infectious dynamics and underlying genetic regulations. The substantial shift and emergence of novel and/or resistance-breaking strains and emergence of pathogen races impose a threat to previously validated resistance genes. In addition, the pleiotropic effect of resistance genes and the interaction among those in terms of durable broad-based resistance levels, yield penalty, as well as environmental interactions are now becoming critically important due to the availability of big genomic data and emergence of advanced analytical algorithms (Patil et al. 2019).

Whole genome resequencing facilitated the characterization of diverse lines with superior haplotypes or alleles among unexplored germplasm which could be used to deploy durable resistance in plant breeding program. The future breeding era is likely to be genomics-assisted breeding (GAB) including marker-assisted recurrent selection (MARS), marker-assisted backcrossing (MABC), haplotype-based breeding, and genomic selection (GS) (Varshney et al. 2021). Trait-associated genes would be mapped with NGS-based trait mapping and system biology approach. Future genetic variations can be estimated by targeting induced local lesions in genomes (TILLING), Eco-TILLING populations, and multiparent advanced generation intercross (MAGIC) or can be created through genome/gene editing (GE). GE has been emerged at an unprecedented speed and probably become a primary technique for translating genomic information to improvement of the crop in the field. However, the success of the development of CRISPR/Cas9 transformants is subject to effective genetic transformation system. Unfortunately, soybean is a recalcitrant crop for plant transformation technology and most of the GE studies are in primary phase of development. Although a few studies have successfully show the introduction of Cas12a-RNP in soybean protoplast (Kim et al. 2017), enormous efforts may be needed to implement these tools into soybean.

All in all, the early establishment of the soybean research field, the vast availability of unexplored genetic diversity through soybean accessions, the breakthrough advancements in genomics and analytics, and the dynamism of the environment, pathogens, and host genetic background will significantly improve the efficiency and accuracy of global soybean breeding in the next decades, ensuring the sustainability and growth of soybean production worldwide.