Theoretical and Applied Genetics

, Volume 132, Issue 3, pp 713–732 | Cite as

Contribution of recent technological advances to future resistance breeding

  • Javier Sánchez-MartínEmail author
  • Beat Keller
Review Article


The development of durable host resistance strategies to control crop diseases is a primary need for sustainable agricultural production in the future. This article highlights the potential of recent progress in the understanding of host resistance for future cereal breeding. Much of the novel work is based on advancements in large-scale sequencing and genomics, rapid gene isolation techniques and high-throughput molecular marker technologies. Moreover, emerging applications on the pathogen side like effector identification or field pathogenomics are discussed. The combination of knowledge from both sides of cereal pathosystems will result in new approaches for resistance breeding. We describe future applications and innovative strategies to implement effective and durable strategies to combat diseases of major cereal crops while reducing pesticide dependency.


Barley (Hordeum vulgare), rice (Oryza sativa), rye (Secale cereale) and bread wheat (Triticum aestivum) are small-grain cereal crops of the Poaceae grass family whose domestication and cultivation facilitated the establishment of sedentary agrarian societies, nourishing humankind ever since (Shiferaw et al. 2013). However, diseases caused by a myriad of pathogens reduce cereal yields by 13% annually at the global level (Oerke 2006). Plant disease control is a necessity, not only for economic reasons but also to ensure global food security. Diseases can cause major crop failures triggering food shortages and, occasionally, alter the course of humankind by causing devastating famines (Chakraborty and Newton 2011).

Crop disease management has been mainly achieved through cultural measures (Hobbs et al. 2008), pesticides (Cooper and Dobson 2007) and resistance breeding (Dangl et al. 2013). Cultural practices may reduce disease incidence, but they show limitations (Jørgensen et al. 2014) leaving pesticides and resistance breeding as the genuinely effective practices combating cereal diseases. However, pesticides compromise biodiversity and ecosystem function (Dormann et al. 2007), threatening also human health (Enserink et al. 2013). In addition, pathogens and pests are increasingly showing pesticide resistance (Lucas et al. 2015). Consequently, policymakers seeking low pesticide-input agricultural production systems are formulating more stringent pesticide regulations, especially in Europe (Lamichhane et al. 2015). Therefore, crop disease management through disease-resistant varieties will play a capital and increasingly important role in ensuring future global food security and the relevance of resistance breeding and its underlying research is likely to increase massively.

The benefits of using disease-resistant varieties are manifold. First, they reduce direct yield losses. Second, their use is an environmental-friendly measure to reduce pesticide dependence. Third, such varieties expand the adaptability of crops to areas previously limited by high disease incidences. Fourth, their use in agriculture commonly does not result in additional costs, which is particularly attractive in regions where low-income farmers cannot afford pesticides. Finally, it is a worthwhile investment. For example, the benefit/cost ratio of developing leaf rust-resistant wheat varieties by CIMMYT was 27 times the initial outlay (Marasas et al. 2004).

A wide-ranging review covering resistance breeding in the four main small-grain cereal crops would go beyond the scope of this article, although crop-specific examples are incorporated when instructive. Instead, we aim at increasing awareness of current possibilities in resistance breeding and at describing possible future applications and strategies. Here, after briefly revisiting the plant immune system, we present recent progress in genomics and high-throughput genotyping technologies (HTGT) identifying host (resistance gene cloning, genetic diversity assessment, etc.) as well as pathogen factors (avirulence gene identification, field pathogenomics, etc.), which when combined shed light on the molecular function of host-plant interactions. We then discuss how the deeper understanding in host-plant interactions supports the implementation of rational strategies for crop disease control through host resistance. This article will hopefully stimulate discussions on future strategies to devise durable pathogen control strategies improving agricultural yields while promoting sustainability.

All components of the plant immune system are useful for resistance breeding

The plant immune system relies on two main classes of receptors to recognize potential intruders and protect plants against pathogen invasion. The first class consists of membrane-spanning pattern-recognition receptors (PRRs) that recognize evolutionarily conserved pathogen- (or microbial)-associated molecular patterns (PAMPs/MAMPs) leading to PAMP-triggered immunity (PTI). Importantly, due to the conserved nature of PAMPs, PTI plays an important role against non-adapted microbes, known as non-host resistance (NHR), and adapted pathogens in partially resistant/susceptible hosts, termed basal resistance (Zipfel 2014; Couto and Zipfel 2016). In non-host resistance, all genotypes of a species are resistant to all genetic variants of a (non-adapted) pathogen species (Heath 2000), which in turn can infect a more or less closely related plant species (Schulze-Lefert and Panstruga 2011). Basal resistance was defined by Jones and Dangl (2006) quantitatively as a defense that is often a manifestation of residual levels of PTI, inhibiting spread after successful infection of adapted pathogens (Niks and Marcel 2009; Poland et al. 2009; Roux et al. 2014). This basal resistance is, mainly, what breeders have traditionally called quantitative disease resistance (QDR) and is present in most, if not all, plant pathosystems (Parlevliet 1992). QDR is usually controlled by several genes associated with genomic regions called quantitative trait loci (QTL). QDR leads to an incomplete phenotypic resistance, following a near-continuous distribution of phenotypes (from very susceptible to very resistant), resulting from the effects of each gene acting—with small or larger effects—at different stages of the pathogen infection process, leading to a reduction rather than an absence of the disease (Niks et al. 2015). On the other hand, host-adapted pathogens might be able to completely suppress PTI by producing and secreting effector proteins into host cells, allowing infection, resulting in effector-triggered susceptibility (ETS) (Jones and Dangl 2006; Dodds and Rathjen 2010). In turn, these effectors can be recognized, directly or indirectly, by host intracellular receptors encoded by resistance (R) genes, leading to effector-triggered immunity (ETI) that usually results in a hypersensitive response (HR) at the infection site, without affecting surroundings cells, whereas host cells retain viability during PTI (Jones and Dangl 2006). PTI and ETI were initially considered as mostly independent layers in the zig-zag model of plant immunity proposed by Jones and Dangl (2006). However, this first assumption has been challenged by recent studies showing that there is no strict PTI/ETI dichotomy. For instance, HR is not restricted to ETI, the differentiation between PAMPs and effectors sometimes cannot be made or PRRs and resistance proteins encoded by R genes belong to the same protein classes; we will discuss below the case of the Stb6 gene of wheat that was identified as a major R gene but found to encode a plasma-membrane receptor-like protein, similar to the receptors involved in PTI. Moreover, network analyses have pointed out that PTI and ETI overlap with common events in downstream signaling, such as mitogen-activated protein kinase (MAPK) signaling, reactive oxygen species (ROS) production or hormone signaling, where induced responses are transient in PTI and prolongated in ETI (Tsuda and Katagiri 2010) and the amplitude of those responses is likely dependent on the level required for effective immunity (Thomma et al. 2011). All these discoveries indicate that there is a continuum between PTI and ETI based on the sharing of common signaling machinery, but used differently in PTI- and ETI-based immune responses. The reader is referred to the excellent reviews written by Thomma et al. (2011), Tsuda and Katagiri (2010) or Cook et al. (2015) for a more comprehensive view on this topic. In the last years, great progress has been made in elucidating the molecular mechanisms underlying PTI-, QDR- and ETI-based resistance thanks to the isolation of many of the genes that control these resistance responses. All well-defined plant PRRs are membrane-localized receptor-like kinases (RLKs) (Monaghan and Zipfel 2012) or wall-associated kinases (WAKs) (Hurni et al. 2015). The activation of PRRs leads to activation of mitogen-activated protein kinases (MAPKs) and calcium-dependent protein kinases (CDPKs), two convergent hubs downstream of PRRs that are involved in PTI transcriptional reprogramming and might be the targets of pathogen effectors to hijack PTI signaling pathways leading to susceptibility (Macho and Zipfel 2014). For the case of ETI-based resistance, most of the R genes cloned so far encode nucleotide-binding domain leucine-rich repeat containing (NLR) proteins, although the growing number of R genes isolated demonstrates that they are much more structurally diverse than originally thought (Cesari 2018). Finally, the picture is more complicated with regard to QDR, as illustrated by the (few) cloned genes so far, which exhibits a broad range of molecular functions (Poland et al. 2009; Niks et al. 2015). Some of the causal genes are involved in pathogen recognition [altered or weak forms of canonical R genes like Pi35 (Fukuoka et al. 2014) or PRRs as Xa21 (Pruitt et al. 2015)], transporters, such as Lr34 (Krattinger et al. 2009) or Lr67 (Moore et al. 2015a), host metabolism [Yr36 (Fu et al. 2009) or Stv11 (Wang et al. 2014a)] or defense [Pi21 (Fukuoka et al. 2009) or mlo (Büschges et al. 1997)]. Thus, molecular mechanisms underlying QDR show a broader mechanistic basis compared to ETI-based resistance. Genes controlling PTI, QDR and ETI responses are valuable genetic resources that have been employed, with varying degree of success, use and acceptance, during decades in resistance breeding. In the following, we briefly mention some of past achievements and strategies employed in traditional resistance breeding using these genetic resources.

R gene-based resistance results in strong and mostly complete resistance (if the R gene is not overcome in the pathogen population), translating to an easily scorable phenotype, whereby this type of resistance has been very popular in breeding programs during decades (McDowell and Woffenden 2003). However, as R gene-mediated resistance exerts a high selection pressure on populations toward virulence, in many cases R genes are quickly overcome (Brown 2015). Thus, R gene-mediated resistance tends to be short-lived particularly when deploying varieties with only one resistance gene (McDonald and Linde 2002). Combining multiple R genes, “pyramiding” in breeding terms, has been proposed as a strategy to increase the durability of R gene-based resistance (Pink 2002) as the likelihood of simultaneous mutations of multiple effectors is low (McDonald and Linde 2002). Besides, R genes with different, but complementary resistance spectra, could be combined providing gene pyramids with resistance to a broad set of pathogen races. Pyramiding of R genes has been based on phenotyping or marker-assisted selection (MAS), which has resulted in the release of many cultivars widely cultivated (Ellis et al. 2014). However, it is important to remember that the appearance of multivirulent pathogen populations can jeopardize the effectiveness of gene pyramids (Brown 2015), which has made plant breeders reticent to use this strategy in their breeding programs. Nevertheless, new genetic and genomic technologies offer the opportunity of incorporating R gene-based resistance in a multitiered strategy combined with other types of resistance to enhance resistance in crops (Zhang and Coaker 2017).

Since PAMPs/MAMPs are conserved and essential for pathogen viability and some PRRs have proven to exhibit broad taxonomic functionality, NHR has been recently seen as a promising resistance breeding strategy to achieve durable and broad-spectrum resistance through the heterologous expression of PRRs (Lacombe et al. 2010; Wulff et al. 2011). Besides, the naturally occurring functional diversity present in crops such as rice (Ayliffe et al. 2011), barley (Atienza et al. 2004) or wheat (Rodrigues et al. 2004) opens up the door to use NHR in resistance breeding in cereal crops, although its practical use in breeding programs is still in an early stage (Bettgenhaeuser et al. 2014; Lee et al. 2016; Elmore et al. 2018). Biotech-based approaches have already proven the applicability of NHR. For example, the maize Rxo1 gene provides resistance to bacterial leaf streak when transferred to rice (Zhao et al. 2005) or the wheat Lr34 does likewise against mildew and rust when transferred to barley (Risk et al. 2013). Non-transgenic approaches based on mutation screening and traditional hybridization with close and interfertile species have been practiced with considerable success, for example, in barley using Hordeum bulbosum as a donor of NHR against important barley diseases (Shtaya et al. 2007; Johnston et al. 2013).

Although QDR only confers partial resistance, it is of high practical importance in resistance breeding because it is associated with two features very attractive to breeders: broad-spectrum and durable. Because QDR, contrarily to R gene-mediated resistance, does not rely on the recognition of rapidly evolving pathogen effectors, QDR is usually effective against all pathogenic races (Lagudah 2011). Besides, several studies have reported QDR as more durable compared to R gene-mediated resistance (Parlevliet 2002; Niks et al. 2015). As QDR is controlled by multiple genes with partial and inconsistent effects, the overcoming by one pathogen variant of one (or even few) of those genes does not confer a significant evolutionary advantage over other pathogen races (Poland et al. 2009). This is the reason why QDR has been proposed as a strategy to achieve stable, broad-spectrum and long-lasting resistance to crop diseases (McDonald and Linde 2002). This is of particular relevance in the current context of emerging epidemics, ever-changing pathogen populations and human-driven spread of pathogens (Velásquez et al. 2018). R-mediated resistance is race-specific and protects crops against a particular effector repertoire present in some pathogen races, whereas QDR is non-race-specific and confers (partial) resistance against all pathogenic races. Therefore, some authors assume that QDR will play an essential role to control epidemics (Roux et al. 2014).

The phenotype exhibited by genotypes with QDR is the sum of the positive and negative alleles across multiple genes (Corwin and Kliebenstein 2017). A single genotype will only rarely have all favorable alleles that exist in the gene pool. However, breeding allows the development of genotypes containing many favorable allelic variants thanks to transgressive segregation: there, progeny of a cross between two genotypes, intermediately resistant or even partially susceptible, exhibit novel or extreme phenotypes compared to the parental lines resulting from epistatic interactions between complementary, but different, genes present in the parental lines (Rieseberg et al. 1999). Transgressive segregation is a widespread phenomenon in nature, present in almost any parental combination tested, and, unlike heterosis, heritably stable. Breeders have taken advantage of transgressive segregation to move polygenic resistance into elite varieties by means of recurrent selection (cycles of intercrossing genotypes and selection against highest susceptibility) (Wallwork and Johnson 1984; Parlevliet and van Ommeren 1988) or actively selecting the accumulation of minor genes conferring resistance, the so-called purposeful selection (Parlevliet et al. 1985; Parlevliet and Kuiper 1985). CIMMYT breeders have adopted a “single backcross-selected bulk” scheme (Singh and Trethowan 2007) as a breeding system to produce wheat varieties with near-immunity to rusts by combining in single genotypes multiple minor genes involved in QDR (Singh et al. 2014).

While R gene-mediated resistance has mostly benefited from recent technological advances (e.g., MAS or new gene cloning approaches), QDR was affected to a lesser extent. The polygenic nature underlying QDR has hampered MAS due to the low precision of markers estimating QTL effects (Dekkers and Hospital 2002). Indeed, the application of MAS for breeding QDR has been far less effective than phenotypic selection (St.Clair 2010). Nevertheless, commercial breeders have considerably elevated the levels of QDR in their germplasms by phenotypic selection under field conditions (Parlevliet and van Ommeren 1988; Niks et al. 2015). Reliable phenotyping complemented with methods involving different MAS approaches has only been applied in transferring major-effect QTLs similarly to what has been done for qualitative genes (reviewed in St.Clair 2010). However, none of those MAS techniques has proven effective in tracking multiple minor-effect QTLs in a single genotype. Very recently, genomic selection (GS), initially proposed by Meuwissen et al. (2001) to capture the total genetic variance in complex quantitative traits controlled by using whole-genome marker profiles has been suggested as a strategy to effectively predict and select for QDR (Poland and Rutkoski 2016). The ever-decreasing costs of NGS technologies allow the availability of genome-wide markers that can be used to elaborate whole-genome prediction models to better capture all small-effect loci involved in QDR, a trait whose genetic complexity was not captured in the past with few markers through MAS approaches (reviewed in Poland and Rutkoski 2016).

Resistance gene identification: novel genes are needed

When breeding for resistance against cereal diseases, every genetic resource available should be used. Both R- or QDR-mediated resistance are highly useful and are not mutually exclusive—rather, they are complementary. In the following, we propose the way forward on the efficient identification and isolation of both types of genes.

Genebanks: treasures of genetic diversity waiting to improve resistance breeding

Genebanks curate diverse plant genetic resources (PGR) encompassing hundreds of thousands of accessions of crop wild relatives, old landraces and local cultivars (Crop Trust 2018). These accessions represent an immense source of genetic variation for resistance breeding (Hajjar and Hodgkin 2007; McCouch et al. 2013) and much of their genetic diversity has not been used in breeding as specific case studies have revealed (Müller et al. 2018). Therefore, it is highly likely that PGR harbor a complete set of “old,” mostly uncharacterized resistance genes that may support resistance breeding. Indeed, PGR have already contributed to resistance breeding in two important ways. Firstly, allele mining on previously cloned resistance genes has identified novel functional allelic variants of, just to name a few, Mla (a barley powdery mildew resistance gene) (Seeholzer et al. 2010), Pm3 [a wheat powdery mildew resistance gene (Yahiaoui et al. 2004; Srichumpa et al. 2005; Yahiaoui et al. 2006; Bhullar et al. 2009; Yahiaoui et al. 2009; Bhullar et al. 2010a, b)] or Pi54 [a rice blast resistance gene, (Vasudevan et al. 2015)]. The combination of such alleles in a single genetic background through transgenic approaches or deployment in multilines has shown to improve resistance in the field (Brunner et al. 2010; Fukuoka et al. 2015a; Koller et al. 2018). Importantly, the availability of different allelic forms is of interest for a geographic-based deployment of R gene diversity to match the pathogen’s virulence profile. Moreover, some of these allele-mining studies have unveiled new allelic variants with a unique, broader spectrum of resistance (Das et al. 2012; Devanna et al. 2014; Fukuoka et al. 2015b). Secondly, PGR have acted as reservoirs of genuinely new disease resistance genes that were left behind during domestication and modern breeding: for example, the rice Xa21 (Song et al. 1995) or the wheat Yr36 genes (Huang et al. 2016) conferring resistance to bacterial leaf blight and stripe rust, respectively. Although the contributions of PGR to resistance breeding have been already relevant as exemplified by the above-mentioned cases, there is much more potential. Most breeders only reluctantly use non-adapted, old genetic material and an important challenge of the future will be to overcome the problems that are caused by classical introgression of such genetic resources.

However, a combination of the latest advances in genomics, next-generation sequencing (NGS) and HTGT technologies with population genomics has the potential to unveil the vast and untapped resistance genes hold in PGR populations to ultimately improve crop resistance. In the following, we suggest strategies toward identification of new resistance sources using PGR collections.

GWAS-based isolation of disease resistance in PGR collections

Identification of resistance genes or quantitative trait loci (QTLs) is supported by genome-wide association studies (GWAS). GWAS explore historical recombination events accumulated over multiple generations in unrelated individuals, where all alleles for a trait of interest, with major and minor effects, are expected to be represented. This contrasts with the low genetic diversity of bi-parental mapping populations (Yan et al. 2011). Until very recently, there were two major difficulties when working with PGR collections. On the one hand, the scarcity of genetic makers made it difficult to introgress resistance genes into elite varieties due to linkage drag. On the other hand, due to the same problem derived from the lack of markers, much of the genetic diversity in PGR collections has not been explored. Both difficulties can be overcome with NGS and HTGT technologies. Technological progress and plummeting costs of NGS services allow the whole-genome sequencing of hundreds (or thousands) of accessions in plant species with small genomes like rice (The 3000 Rice Genomes Project 2014). With multiple genome sequences fully annotated, GWAS studies are currently used in mining resistance to different diseases affecting rice, uncovering multiple new allelic variants and resistance genes that are serving to broaden the diversity of resistance of commercial varieties (Raboin et al. 2016 and references therein).

However, for species with larger and more complex genomes such as wheat, barley or rye, whole-genome sequencing of thousands of accessions is still neither possible nor practical. Consequently, different reduced-representation sequencing technologies were developed for genotyping large PGR collections. For example, array-based SNP platforms allow a cost-effective and high-resolution genome-wide assessment of the genome diversity of PGR collections of barley (Bayer et al. 2017), rice (Chen et al. 2014; McCouch et al. 2016), wheat (Wang et al. 2014b; Winfield et al. 2016; Allen et al. 2017) and rye (Bauer et al. 2017). All these SNP-based arrays have benefited from high-quality whole-genome reference sequences of wheat (IWGSC 2018), barley (Mascher et al. 2017) and rice (International Rice Genome Sequencing Project 2005) to assign accurate physical positions to the markers. This is of special relevance to efficiently isolate disease resistance genes following map-based cloning or GWAS analysis approaches by facilitating the anchoring of molecular markers to small physical intervals pointing out directly a small set of candidates genes (Keller et al. 2018; Togninalli et al. 2018). In the near future, genomic information derived from multiple reference genomes can be expected in cultivated wheat, barley and their wild relatives. This information is highly useful both for the identification of candidate resistance genes by GWAS or through means of map-based cloning (Box 1).

Box 1: Genomics-assisted identification of resistance genes—the relevance of wheat and barley pangenomes

A single reference genome cannot capture the full genetic variability of resistance genes present in a species. Different genotypes show substantial genetic diversity due to structural variations in the form of copy number variants (CNVs) and presence/absence variants (PAVs) (Saxena et al. 2014). Therefore, the likelihood of finding a specific resistance gene in a single reference genome is low. For example, Chinese Spring (CS), the cultivar used to establish the reference sequence of bread wheat, has not been widely used in breeding due to its susceptibility to biotic and abiotic stresses (Sears and Miller 1985). Moreover, a recent chromosome-scale comparative analysis between chromosomes 2D of CS and the wheat genotype “CH Campala Lr22a”, which carries the adult plant leaf rust resistance gene Lr22a, revealed large structural variations and four haploblocks with significantly increased single nucleotide polymorphism (SNPs). Three of those haploblocks showed CNV for NLR genes (Thind et al. 2018). This type of studies is of utmost relevance because SNPs and SVs are major contributors to phenotypic variation (Saxena et al. 2014).

Therefore, it would be desirable to have a complete genomic composition of a species, the so-called pangenome. Accordingly, the SHAPE project is laying the foundation stone of the barley pangenome with de novo whole-genome shotgun sequencing of two haplotypes profoundly distant at a genomic level from Morex, the cultivar used for the barley reference genome (SHAPE 2018). Likewise, a global wheat partnership is on the road toward the first wheat pangenome by sequencing ten wheat genomes from different origins around the world (Wheat Initiative 2018, Moreover, highly continuous genome assemblies for the wheat diploid donors of the A genome (T. urartu) (Ling et al. 2018) and D genome (Ae. tauschii) (Luo et al. 2017) and its allotetraploid progenitor, wild emmer (T. turgidum ssp. dicoccoides) (Avni et al. 2017) have been recently generated. These genomic resources will open up new avenues for implementing genomics-assisted resistance breeding in different ways (Bevan et al. 2017) (Fig. 1). Firstly, a pangenome will reveal genomic diversity between genotypes and allow the identification of novel alleles and genes not present in single reference genomes. Secondly, the incorporation of additional reference sequences will improve the SNP calling between multiples genotypes that until now have relied mainly on single reference genomes. Therefore, SNP-based arrays or exome capture designs will be implemented simplifying map- or GWAS-based isolation of genes by anchoring markers to smaller physical interval targets or discovering rare alleles (Zhao et al. 2018). Thirdly, a pangenome formed by a given domesticated crop and its wild relatives provides a unique genomic structure to which all known variations can be anchored. This reduces the risk of missing out R genes present in wild relatives but absent in domesticated forms. Moreover, pangenomes including wild relatives will allow the identification of orthologues, an aspect of particular relevance in resistance breeding where resistance gene suppression of wild relatives-derived genes by their orthologues present in domesticated forms is a common phenomenon (e.g., Pm8 rye-derived gene is suppressed by its wheat orthologue Pm3CS (Hurni et al. 2014). There, it was shown that the putative suppressor action of these orthologues can be tested in the N. benthamiana system. This might be generally true and suppressors could be eliminated by genome editing if necessary. Finally, thanks to bioinformatic-driven advances in NLR annotation, hundreds of NLR-encoding genes are predicted in cereal genomes (Steuernagel et al. 2018) serving as information on potential resistance genes, which can greatly assist the functional validation of candidates genes resulting from map- or GWAS-based approaches. Therefore, high-quality cereal genome sequences have enormous value and potential for gene cloning, and, a rapidly increasing number of molecularly isolated resistance genes can be expected.
Fig. 1

Diagram of the wheat pangenome. a An example of a genomic region in Chinese Spring (CS) that consists of four R genes, the gold colored one is a pseudogene. b and c Two genomic regions in two elite cultivars. The incorporation of additional high-quality reference genomes results in the discovery of new resistance genes (light blue) absent in CS, or the complete cds of others (gold). d Genomic region of a wild relative displaying shared R genes with some of the elite varieties (orange, gold and dark blue) and a cluster of new R genes. e Resulting pangenome after the integration of genomic annotation from different elite varieties and wild relatives (color figure online)

An alternative genotyping approach to SNP arrays is exome capture (King et al. 2015), a genome complexity reduction strategy focused on sequencing the exome, which has allowed the discovery of markers in cultivated and wild relatives of barley (Wendler et al. 2014) and wheat (Allen et al. 2013). However, exome capture designs are biased by incorporating gene annotation of reference genomes. The sequencing of multiple reference genomes in the future will certainly assist in improved designs of exome capture arrays, but a small number of varieties cover only a small part of the genetic reservoir of resistance genes. Therefore, they are of limited use particularly for the highly diverse resistance genes. In contrast to exome capture arrays, genotyping-by-sequencing (GBS) (Elshire et al. 2011) represents an alternative genotyping approach that does not rely on a fixed set of SNPs and is reference-free. GBS involves targeted genome complexity reduction followed by restriction enzyme-based sequencing allowing marker discovery that is genome-wide and population specific. When GBS-based marker discovery is combined with association mapping, novel R genes/QTLs specific and unique to a selected population can be uncovered (Mgonja et al. 2017). GBS-based tagging of genomic regions harboring resistance genes in PGR collections is of particular importance because it would enable the discovery of resistance genes absent in reference genomes. SNP-based arrays or exome capture designs relying on reference genomes will miss out PGR-specific resistance genes. This makes GBS an excellent de novo marker platform for genomics-assisted breeding.

PGR collections for gene identification are most useful if they represent a maximal biological diversity of the trait of interest and there are efficient, high-quality phenotyping methods available. The overwhelming number of GR makes it virtually impossible to genotype all accessions. Therefore, efficient criteria to define useful genetic variation for resistance breeding are needed. In this regard, “Focused Identification of Germplasm Strategy” (FIGS) can act as a proxy to select PGR with potential resistance genes (Mackay and Street 2004). FIGS assumes that adaptive traits can be linked to eco-geographic parameters, and, therefore, PGR from agro-ecological regions with disease-conductive conditions would represent reservoirs of resistant genes to diseases of those regions. Using FIGS, sources of resistance to barley net blotch (Endresen et al. 2012), rice blast (Vasudevan et al. 2014) or wheat powdery mildew (Kaur et al. 2008; Bhullar et al. 2009) have been found. FIGS might be further complemented by GS. Using a training population (a fraction of the whole PGR collection) for which both phenotypic and genotypic data are available, GEBVs are calculated following different GS models. The further selection of genotypes supposed to have beneficial alleles within the PGR collection are selected based on their GEBVs. GS has proven to facilitate the selection of superior accessions from big PGR collections in soybean (de Azevedo et al. 2017), maize (Pace et al. 2015) or in wheat for adult plant resistance to stripe rust (Meuwissen et al. 2001).

On the other hand, tremendous advances in genomics have shifted the research bottleneck in plant breeding from genotyping to phenotyping (Furbank and Tester 2011), which prevents us from translating genomic variants into desired resistance phenotypes (Araus et al. 2018). Therefore, there is an urgent need to develop phenotyping tools that a) increase the throughput of resistance screening under controlled and/or field conditions and b) reliably identify the genetic variants underlying phenotypes (Mahlein 2016). Fortunately, phenomics has progressed rapidly in recent years developing field-based, high-resolution and high-throughput sensor-based phenotyping tools (Simko et al. 2017; Shakoor et al. 2017) that deliver an improved, reproducible and accurate disease phenotyping at large scale. The future improvement of these techniques and their use in evaluating PGR collections for disease resistance would allow us to identify more efficiently genomic variants responsible for desired resistance phenotypes.

High-throughput isolation of gene family-specific resistance genes: fishing out NLRs

NLRs share distinct protein motifs that guide the design of specific probes for NLR-encoding sequences (Jupe et al. 2013), which can be used for gene isolation based on exome capture (Arora et al. 2018). Wild relatives are reservoirs of novel NLR genes, exhibiting extensive diversity (Jones et al. 2016). In a recent study, Arora et al. (2018) performed association genetics with NLR gene enrichment sequencing (AgRenSeq) in a panel of Ae. tauschii (donor of D genome of bread wheat) accessions to exploit the naturally occurring variation of NLRs. The phenotypic evaluation assessing race-specific resistance against stem rust with just seven isolates allowed the rapid molecular identification of four genuinely new NLR in a cost-effective manner and bypassed the need to generate mutant or recombinant populations. Moreover, AgRenSeq can be applied to distant wild relatives of domesticated crops that often show sexual incompatibility. Finally, AgRenSeq is reference-free, which is particularly attractive for the isolation of resistance genes, as these exhibit extreme accessional diversity (Noël et al. 1999). AgRenSeq and similar approaches promise to greatly speed up the discovery of new functional NLR genes, which can be introgressed into elite varieties either through MAS based on gene-specific markers or transgenic approaches (“Resistance gene introgression” section). Finally, when the current, most pathogenic strains are used, AgRenSeq provides a tailor-made resistance breeding strategy counteracting pathogen’s virulence profile.

Rapid approaches for resistance gene isolation in non-reference cultivars

Until very recently, most disease resistance genes have been identified through high-resolution mapping and positional cloning, a burdensome and lengthy procedure taking several years. However, the combination of recent advances in NGS, bioinformatics and genome complexity reduction technologies resulted in several very rapid and efficient gene isolation approaches in cereals (reviewed in Periyannan 2018). A gene map-based approach named TACCA (targeted chromosome-based cloning via long-range assembly), based on high-quality sequences of flow-sorted chromosome carrying the resistance gene, identifies resistance genes using molecular marker information and ethyl methanesulfonate (EMS) mutants (Thind et al. 2017). Other approaches have moved away from map-based cloning and focused on mutagenesis and sequencing of a portion of the genome/transcriptome to identify resistance genes through mutational mapping [MutMap, (Abe et al. 2012)], mutant chromosome sequencing [MutChromSeq, (Sánchez-Martín et al. 2016)] or gene enrichment and sequencing [MutRenSeq, (Steuernagel et al. 2016)]. With these gene isolation techniques, it is now possible to isolate quickly each of the hundreds of known disease resistance genes described in major crops. Therefore, it is timely to isolate them for breeding applications. Besides, the research community has developed over many years a large series of NILs carrying R genes. They represent excellent material for the application of some of the above-mentioned gene isolation approaches.

In the two previous sections, we have described how to isolate R genes quickly. However, identifying candidate genes very rapidly does not make functional validation easier. Given the pace at which candidate genes can now be discovered, high-throughput functional genomic approaches are urgently needed to determine if genetic variants are indeed functional in resistance (Box 2).

Box 2: Scaling up functional genomics—the relevance of improved and new functional genomics approaches

Transgenesis, the primordial functional validation approach used so far, is time-consuming and restricted to a narrow set of transformable genotypes. Moreover, transgene insertion and expression are uncontrolled and pleiotropic effects on plant growth and development may occur. Some of these drawbacks can now be overcome with improved transformation protocols or precise DNA insertion facilitated by genome editing technologies. Moreover, the latter can serve as a high-throughput functional genomic approach (Khatodia et al. 2016 and references therein). Nevertheless, novel and faster, complementary functional genomics approaches are urgently needed.

In this regard, it would be worth exploring possibilities offered by “effectoromics,” a powerful tool to identify host resistance genes and matching pathogenic avirulence genes in plant-oomycetes pathosystems based on transient expression Agrobacterium-based functional assays (Vleeshouwers and Oliver 2015). Although still nascent in cereal-fungi pathosystems, we believe that this approach can greatly assist in the functional validation of candidate R genes. For example, the validation of the set of R candidates derived from an AgRenSeq study could be supported by the agro-co-infiltration with core pathogenic effectors obtained from field samples in Nicotiana benthamiana checking for R gene-specific hypersensitive response. Consideration, however, needs to be given to genetic interactions exhibited by some R genes (Resistance gene deployment and management: a view to the future), which can make R validation through effectoromics challenging. Besides, effectoromics assumes a direct interaction between the R-AVR pair of proteins, which is not always the case. Therefore, independent and complementary functional assays are required to confirm the resistance action of candidate genes, like Virus Induced Gene Silencing (VIGS) (Lee et al. 2012) or reverse genetic approaches like TILLING (Targeting Induced Local Lesions IN Genomes) (Krasileva et al. 2017).

Isolation of genes underlying quantitative disease resistance

Very few QDR loci have been cloned to date, including two wheat leaf rust resistance genes: Lr34 (Krattinger et al. 2009) and Lr67 (Moore et al. 2015b); a wheat yellow rust resistance gene: Yr36 (Fu et al. 2009); and a recessive rice blast resistance gene pi21 (Fukuoka et al. 2009). The main limitation identifying genes underlying QDR resistance stems from the difficulty of obtaining reliable genotype–phenotype associations. QDR resistance is controlled by many genetic loci, which individually have small effects, thereby hampering an appropriate disease scoring. The question is “how can we adequately select genes—in particular, those with small effects- underlying QDR?” Choice of the population, reproducible and accurate phenotyping and identification of mutants are the critical elements in ensuring a proper selection of genetic determinants of QDR.

Population choice: nested association mapping (NAM)

The use of recombinant inbred lines based on multiparent cross-designs, such as nested association mapping (NAM) populations, has been proposed for fine-scale mapping and marker-trait associations of complex traits by combining the advantages of association mapping (high mapping resolution) and linkage mapping (power of QTL detection) (Yu et al. 2008; Buckler et al. 2009). A NAM population encompasses a wide range of genetic diversity derived from multiple recombinant inbred line (RIL) families connected by a common cultivar that acts as a recurrent parent. The genetic diversity is generated by a) the shuffling of parental alleles over generations through segregation and genetic recombination, and (2) the historical recombination of haplotypes present in the donor parents. The resulting genetic diversity encapsulates all allele variants whose frequencies are balanced and with rare variants enriched, thereby increasing the odds of discovering rare alleles. Moreover, it has been reported that the genetic background may influence QTL effects due to epistasis and pleiotropy (Holland 2007; Mackay et al. 2009). NAM populations, as multi parental populations, are able to unravel such effects that would go unnoticed in simple genotypes such as NILs. First promising steps in the use of NAM populations for the genetic dissection of QDR are being taken for stem rust resistance in wheat (Bajgain et al. 2016) or net blotch resistance in barley (Vatter et al. 2017). Decades of fieldwork in nurseries gives us access to rich phenotypic data on cultivars proven to have durable and stable resistance over long periods across large areas without resistance breakdown. These cultivars must now be identified and used as recurrent parents in the development of NAM populations. The advance of HTGT technologies facilitates genome-wide genotyping of NAM populations. Having said that, new phenotyping approaches are needed to achieve the genetic dissection of QDR.

Accurate, reliable and reproducible phenotyping

Visual scoring is admittedly subjective and error-prone (Poland and Nelson 2011). While this might not be critical when evaluating R gene-mediated resistance, in the case of QDR an accurate and reproducible evaluation is essential. Sophisticated technologies based on hyperspectral and thermal imagining (Mutka and Bart 2014), chlorophyll fluorescence imagining (Rousseau et al. 2013) or microphenomics (Douchkov et al. 2013) can greatly assist identifying genetic variants controlling QDR as these techniques quantitatively measure disease incidence, as recently demonstrated in the Septoria tritici blotch–wheat pathosystem (Karisto et al. 2018). Moreover, the environment can significantly influence QTL expression (Gutiérrez et al. 2015), which requires a valuation at contrasting locations. Therefore, we suggest that future QDR studies would employ regionally adapted varieties with proven durable and stable resistance as the recurrent parent of NAM populations, which would be evaluated at multiple sites using non-invasive and automated phenotyping platforms that can quantitatively assess resistance under near-field conditions during disease development (Thomas et al. 2018).

The role of mutants in the genetic dissection of QDR

EMS-derived mutants have been proven to support the identification of QDR loci. For example, the multipathogen “pleiotropic” resistance wheat QTLs, Lr34/Yr18/Sr57/Pm38 (Krattinger et al. 2009) and Lr67/Yr46/Sr55/Pm46 (Moore et al. 2015a) were isolated by positional cloning, assisted by EMS-induced mutants. This was possible because of their strong phenotypes. Therefore, for those major QTLs for which mutants can be identified, gene isolation techniques such as MutChromSeq (Sánchez-Martín et al. 2016) certainly can speed up the QTL isolation process. In the case of minor QTLs, although the identification of EMS mutants is unlikely, we believe that mutants can still play a substantial role. Nested genome-wide association (GWA) mapping has finely mapped regions associated with QDR in Arabidopsis (Huard-Chauveau et al. 2013; Debieu et al. 2016) and in wheat (Bajgain et al. 2016), resulting in a small number of candidates genes. Further detailed phenotypic evaluation of such limited numbers of candidates genes can then be done with mutants generated from resistant parent-derived TILLING populations (Hurni et al. 2015) or insertional mutants (T-DNA) (Debieu et al. 2016). Therefore, the development of EMS-derived and TILLING mutants in genotypes of interest seems to be promising to assist in the validation of major- and minor-effect QTLs, respectively.

The emerging importance of pathogenomics in resistance breeding

Classical work on the diversity of wheat fungal pathogen populations has characterized field isolates based on their virulence on near-isogenic lines (NILs) carrying known R genes. This approach results in a limited understanding of pathogen diversity as it only describes virulence or avirulence to a few, known genes Thus, NIL-based characterization only provides a partial understanding of the pathogen’s biology and population structure. Nowadays, NGS and HTGT technologies offer an unbiased characterization of pathogen populations, both at the organismal and molecular levels. Genomic and transcriptomic data from field samples, so-called field pathogenomics, can rapidly define the population structure and genetic diversity of pathogens (Möller and Stukenbrock 2017), which, if spatially and temporally monitored, can predict emerging epidemics by uncovering diversity changes and population structure shifts (Hubbard et al. 2015) and pathogen surveillance activities. Moreover, this data allows to rapidly define lineage-specific repertoires of effectors (Zhong et al. 2018). When compared with each other, a core of effectors can be defined (Dangl et al. 2013). Together with the increasing number of effectors characterized at the molecular level allowing the design of specific molecular markers (reviewed in Bourras et al. 2018), this pathogen-derived information opens up new avenues to improve resistance through so-called effector-assisted breeding. For example, it might guide identification of R genes, redirecting efforts toward genes matching the pathogen’s virulence pattern (“Resistance gene deployment and management: a view to the future” section), or predict the durability of to-be-deployed R genes (Vera Cruz et al. 2000). Furthermore, resistance breeding would benefit from releasing R genes that recognize the most conserved effectors among pathogenic strains. Genomic-level studies can reveal pathogen evolution processes that result in newly emerged pathogens like the wheat blast pathogen (Inoue et al. 2017) or novel powdery mildew forms able to infect rye–wheat amphiploid species (Menardo et al. 2016). This type of information must be considered for successful resistance introgression. Thus, pathogen-informed strategies will play an increasing role in resistance breeding in the future. After the long decline of the once popular virulence studies in fungal populations, the molecular understanding of pathogens is currently contributing to a revival of integration of pathogen information to the breeding process. Although R gene-based resistance breeding will mostly profit from effector-assisted breeding, pathogenomics will possibly also develop into an important tool for the work with QTL for resistance. Recently, transcriptome analysis showed that mildew and rust pathogens growing on host plants carrying the broad-spectrum resistance QTL Lr34 from wheat do not show alterations in gene expression (Sucher et al. 2018). Thus, there is no physiological response of the pathogens to the presence of this resistance gene, possibly explaining why pathogens have not adapted to Lr34 despite the intensive use of this gene in last more than 100 years. It is tempting to speculate that this absence of transcriptional response could be the hallmark of durable resistance QTL, a hypothesis that remains to be tested in NILs containing known QTL for resistance.

Resistance gene introgression

With the gene isolation techniques mentioned in “Rapid approaches for resistance gene isolation in non-reference cultivars” section, it is now possible to isolate quickly each of the hundreds genetically described disease resistance genes in barley, rice and wheat. It is time to set the goal to isolate all of these genes with potential value for resistance breeding and to increase crop disease resistance within elite varieties. The question that arises is “how can we incorporate into elite varieties all those genes?” The answer largely depends on the species source of the resistance gene.

Introgression from sexually compatible relatives

If the donor parent belongs to the primary gene pool or the loci of interest are present in a homologous chromosome of a species belonging to the secondary gene pool, resistance introgression is moderately feasible via marker-assisted backcrossing (MABC): sexual crossing, backcrossing and selection. The isolation of the gene of interest following one of the approaches explained in “Genebanks: treasures of genetic diversity waiting to improve resistance breeding” through “Isolation of genes underlying quantitative disease resistance” sections provides the “perfect marker,” a gene/allele-specific marker that greatly assists the selection of the loci of interest while recovering the recurrent parent genome almost entirely in a few backcrossing generations. Besides, MABC can be underpinned by novel breeding-based approaches to speed up resistance introgression by shortening plant generation times. For instance, Ruengphayak et al. (2015) proposed a pseudo-backcrossing design for rapid introgression of traits in rice. Other approaches combine embryo culture with specific growth conditions to generate multiple generations of barley and wheat per annum (Zheng et al. 2013; Hickey et al. 2017). Finally, Watson et al. (2018) using supplemental lighting under controlled conditions prominently accelerated the traditional single seed descent approach to generate multiple generations per year, allowing at the same time phenotyping on-the-go, a clear advantage compared to double haploid technology. This approach, called speed breeding, holds great promise for rapid resistance introgression into domesticated crops from sexually compatible relatives in the context of “traditional” resistance breeding, speeding up the development of disease-resistant varieties tremendously.

Introgression from distant relatives

If the donor species belongs to the tertiary gene pool or the locus of interest is present in a non-homologous chromosome from a species from the secondary gene pool, resistance introgression is much more challenging. If hybrids are possible, interspecific hybridization can be performed by sexual hybridization supported by cytological techniques. Alternatively, in the presence of sexual incompatibility, chromosome-engineering approaches mediated by asymmetric somatic hybridization are required. The resulting introgression lines (ILs) are pre-breeding lines with introgressed segments of highly variable size harboring the gene of interest. They can then be backcrossed to recover the recurrent parent genome. Although widely recognized as rich sources of resistance (Hajjar and Hodgkin 2007), ILs barely have been involved in the development of elite varieties due to the lack of appropriate high-throughput technologies to screen, identify and select small introgressions with the resistance gene (to eliminate linkage drag) and reduced interspecific recombination frequencies in the introgressed regions (Kilian et al. 2011). Nowadays, the detection efficiency of alien introgressions, their characterization in term of size and their tracking through breeding programs is greatly assisted NGS technologies. The availability of SNP-based arrays derived from hexaploid wheat and its secondary and tertiary gene pool (Winfield et al. 2016) allows to track introgressions precisely through the process of backcrossing and selfing, while selecting the smallest introgression in interspecific wheat/wild relatives F1 hybrids (King et al. 2017). Likewise, GBS and exome capture designs derived from species of the secondary gene pool (Wendler et al. 2014) enable a precise genetic characterization of barley/wild relative introgression lines (Wendler et al. 2015). These two examples highlight the relevance and practical use in resistance breeding of extending HTGH and NGS technologies to the secondary and tertiary gene pools of domesticated crops allowing more efficient use of ILs in resistance breeding by reducing the size of the fragments and then reduce linkage drag. However, one cannot ignore the fact that deleterious genes will be introgressed along with the gene of interest even when the alien introgression are selected for small size. In such cases, resistance introgression needs to be done by transgenesis.

Transgenesis-based introgression of resistance

A faster way to introgress resistance circumventing sexual incompatibilities, linkage drag and genes linked in repulsion hampering their selection (e.g., Sr31, Sr33, Sr50 (Ellis et al. 2014) is through genetic transformation of elite varieties with resistance genes (Rodriguez-Moreno et al. 2017). Transgenesis-based resistance has been successfully used in rice (Kumari et al. 2017) or wheat (Brunner et al. 2011, 2012b; Koller et al. 2018) via Agrobacterium-mediated or particle bombardment methods. Interestingly, the overexpression of the transgene might lead to resistance in the field even though the resistance gene has been overcome in natural cultivars (Koller et al. 2018). Transgenesis, unable to control where the transgene is inserted, is not free of silencing by the genetic background surrounding the insertion site (Matzke and Matzke 1998). Consequently, technologies that allow a controlled insertion of the transgene, such as CRISPR/Cas9 (reviewed in Khatodia et al. 2016), when combined with gene cassettes (explained below), would be a very promising approach in resistance breeding.

Clusters of different R genes cloned into the same vector and jointly transformed into a preferential variety, so-called genes cassettes, have been proposed to overcome the limitations mentioned above (Wulff and Moscou 2014). Moreover, the genetic basis of R genes used in gene cassettes can be broadened by different means. Firstly, naturally occurring allelic variants with extended or different resistance spectra can replace alleles with reduced resistance specificity and secondly through engineering new R-mediated resistance specificities (Zhang and Coaker 2017; Cesari 2018; De la Concepcion et al. 2018). There are the first successful reports to engineering new resistance specificities based on information derived from allele-mining studies. For example, in the work of Stirnweis et al. (2014), the substitution of two amino acids in Pm3f resulted in a broader resistance specificity, very similar to the one conferred by the Pm3a protein. These results highlight the relevance of allele mining and its practical use in resistance breeding.

Initially conceived as a package of R genes, mainly NLR-encoding genes, these gene cassettes can be now complemented with the inclusion of genes governing non-host resistance and QDR, and thus enhancing disease resistance. For instance, the rice PRR XA21 confers resistance bacterial blight of rice by recognizing a conserved sulfated peptide from Xanthomonas oryzae pv. oryzae (Pruitt et al. 2015). Moreover, including QDR genes that are affecting different stages of the pathogen infection process is particularly attractive. For example, Li et al. (2001) proved that pyramiding genes with a different mode of action on the pathogen could extend the durability of pyramid-based resistance. This kind of gene pyramids would represent a nearly insurmountable barrier for the pathogens but requires a very detailed physiological understanding of the action of each individual QDR.

A further much-discussed approach, not yet adequately tested, is host-induced gene silencing (HIGS) of small interfering RNAs that may silence essential genes in the pathogen (reviewed in Nunes and Dean 2012). If promising results obtained to date in transient gene expression assays were further confirmed in transgenic plants, we would get confidence that HIGS is a viable technology for resistance breeding.

Nevertheless, transgenesis and genome editing approaches are hampered by relatively low transformation efficiencies and limited to a narrow range of transformable varieties, especially in wheat and barley. Despite recent improvement in transformation protocols and thereby widening the genotype range for transgenics (Wang et al. 2017), their practical use in resistance breeding is still very far from reaching a massive scale level. Besides, legal barriers resulting from the negative public perception of transgenic plants in certain areas, especially in Europe, discourage the use of these approaches by many breeders. Consequently, we advocate for “a more realistic and implementable” resistance breeding strategy based on a multiresistance gene by pyramiding as described below. Nevertheless, we consider it to be important to emphasize in public discussions related to future changes in regulatory procedures that transgenic approaches have an enormous potential to provide strong and efficient resistance (Wulff and Dhugga 2018; Koller et al. 2018), allowing to reduce fungicide use, another important goal of our societies.

Resistance gene deployment and management: a view to the future

Rotations in space and time of multilines or mixtures of agronomically very similar varieties differing in resistance genes (Mundt 2002) prevent pathogen spread by increasing host variety and barrier effects (Pink 2002). This strategy, adopted in barley (Wolfe 1992) and rice agro-systems (Zhu et al. 2000), has faced deployment difficulties due to the lack of uniformity among varieties, hampering its use in other agro-systems, such as wheat (Pink 2002). Nevertheless, transgenic multilines have proven to be effective to counter wheat powdery mildew (Brunner et al. 2012b). However, given the operational difficulties at farming level, we advocate using regionally adapted cultivars with pyramided resistance genes.

The combination of R genes into a single cultivar—gene pyramiding—(Hsam and Zeller 2002; McDonald and Linde 2002), has been used to lengthen the durability of the R-mediated resistance under the pretext that pathogens would require changes in multiples Avr genes to bypass host recognition (Hsam and Zeller 2002; McDonald and Linde 2002). Many reports have proven the value of R pyramiding to counter, for example, wheat powdery mildew (Liu et al. 2000) or rice blast (Hittalmani et al. 2000). Although close markers to the gene of interest have been proven useful in MABC schemes, we have advocated the isolation of R genes due to two reasons. First, gene/allele-specific markers are highly diagnostic and can be used in any genetic background (Miedaner and Korzun 2012). Second, the isolation of genes might allow checking for eventual compatibility and suppression effects. Some R genes act additively [e.g., the wheat stripe rust genes Sr24 and Sr26 (Roelfs 1988), or the Lr10-mediated resistance to leaf rust in wheat (Loutre et al. 2009)]. Other introgressed genes are suppressed by their homologues, as the Pm8 rye-derived gene is by its wheat homologue Pm3 (Hurni et al. 2014). Finally, R genes can interact with APR genes like the wheat Lr34 enhances the effectiveness of some R genes (German and Kolmer 1992; Vanegas et al. 2008). These aspects represent limiting factors in R pyramidization and should be identified before starting the backcrossing process (Sánchez-Martin et al. 2018). In this regard, transient assays can assist in the rapid detection of suppressive interactions between resistance genes (e.g., the Pm3Pm8 case).

The overarching prerequisite for the success of any breeding strategy for durable resistance depends on deployment strategies that exert less selective pressures on the pathogen (Burdon et al. 2016). To do so, we propose the implementation of resistance breeding strategies that have two main features (Fig. 2). Firstly, it has a multigene nature, including R and QDR genes (cloned as described in “Genebanks: treasures of genetic diversity waiting to improve resistance breeding” through “Isolation of genes underlying quantitative disease resistance” sections) that desirably act in different aspects of the pathogen life cycle. The introgression of the resistance is achieved following a MABC strategy under speed breeding conditions as presented in “Introgression from sexually compatible relatives” and “Introgression from distant relatives” sections. Alternatively, where legally possible and commercial varieties are amenable to transformation, the gene cassette strategy could be implemented. This multigene resistance, guided by pathogen-derived information, is deployed following a mosaic design at the local or regional level to match pathogen’s virulence profile. By doing so, breeders will broaden the genetic diversity at the gene (different resistance components) and plant level (different hosts), thereby mimicking as much as possible the genetic diversity present in PGR populations in natural environments, and then reducing evolutionary pressure on pathogen populations. Secondly, the resistance is deployed dynamically over time, incorporating new resistance components to counter new pathogenic strains. Given the declining sequencing costs, genomics-informed, real-time, global pathogen surveillance protocols will be commonplace soon, enabling the monitoring of allelic frequencies of Avr genes and potential changes in pathogen populations. All this will guide rational resistance deployment strategies matching current and predicted pathogen’s virulence at a regional level. If so, breeders will break up the adaptative landscape, and thus relieve the evolutionary pressure on pathogen populations to ultimately lengthen the durability of the resistance (McDonald and Stukenbrock 2016).
Fig. 2

Diagram of a possible resistance breeding strategy for the geographic region colored in turquoise. a Cloning of described resistance genes using approaches described in “Rapid approaches for resistance gene isolation in non-reference cultivars” section. b AgRenSeq on a wild relative diversity panel and field pathogenomics (“High-throughput isolation of gene family-specific resistance genes: fishing out NLRs” section). c Co-infiltration in N. benthamiana of resistance genes (colored bars) and core pathogenic effectors (gray colored circles). Specific resistance responses represent candidate R-Avr pairs, depicted as yellow circles in leaves (green squares). dN. benthamiana-based co-infiltration assay checking for incompatibilities between different R genes when co-infiltrated with Avr effectors. The co-infiltration of the red and green R genes does not result in HR, due to, for example, a suppression phenomenon: the wild-derived red gene is suppressed by its wheat orthologue (light green), similar to the Pm3Pm8 case. Consequently, red and blue R genes are the selected genes to be introgressed into an elite variety. e Introgression resistance strategies, considering a wild relative with the red R gene and an elite variety with the blue R gene following a marker-assisted backcrossing approach (MABC) supplemented with speed breeding (SP), trangenesis or genome editing approaches. Finally, the figure considers the case of introgression by means of a gene cassette strategy into another elite variety (light green). The strategy would be implemented across time, selecting R genes to be introgressed based on the pathogen-derived information. Finally, the strategy would be implemented in regions colored in red and in green light in a similar way. Note that the colored geographic regions correspond to hypothetical scenarios where it is assumed the presence of the same pathogenic race (color figure online)

Unresolved questions and future challenges

Genome editing: the Trojan horse to introgress resistance

There is a growing interest to silence (modify or suppress) plant genes that act as “Trojan horses” to the plant resistance machinery, the susceptibility (S) genes: host targets manipulated by pathogen effectors that lead to the plant immune system disarmament (Pavan et al. 2010). By selecting against an S gene, an essential component for pathogen replication or effector target is eliminated and plants become recessively resistant. There are some rare, but very relevant and successful cases of loss-of-function S genes used in resistance breeding. For example, the well-known naturally occurring mutation of the Mlo gene in barley confers broad-spectrum resistance against the barley powdery mildew (Jorgensen 1992), or the pi21 recessive mutation associated with durable resistance to rice blast (Fukuoka et al. 2009). With the advent of genome editing technologies able to manipulate precisely specific genomic sequences susceptibility genes can be “switched off” (Zaidi et al. 2018). One of these genome editing technologies, CRISPR/Cas9, excels above the rest since it allows simultaneous multisite editing (Cong et al. 2013), which is particularly attractive for polyploid species, such as wheat, allowing simultaneous targeting of the homoeologous gene copies in all the subgenomes. However, precautions should be taken in this regard, as multifunctionality and pleiotropic effects might limit the use of S genes in resistance breeding. For example, although mlo mutants confer resistance to the barley powdery mildew, the resistance against necrotrophic and hemibiotrophic pathogens is compromised (McGrann et al. 2014). Likewise, the disease-susceptibility gene Xa13/Os8N3/OsSWEET11 conferring resistance to rice bacterial blight is required for reproductive development (Chu et al. 2006). Besides, genome editing is genotype specific. Therefore, the disruption of S genes would require a one-by-one evaluation of their agronomic viability.

Identification of non-canonical R genes: beyond NLRs

In most of the known gene-for-gene relationships NLRs recognize Avr effectors. However, the wheat Stb6 gene encodes a conserved wall-associated receptor kinase (WAK)-like protein (Saintenac et al. 2018). It recognizes an apoplastic effector (Zhong et al. 2017) and suggests that resistance genes not encoding NLRs might play a critical and currently underestimated role in race-specific resistance. Furthermore, WAK genes have been found to play an important role in disease resistance in rice and maize (Hurni et al. 2015). Therefore, given the widespread presence of WAK genes in cereal genomes (Vogel et al. 2010; IWGSC 2018), it is of utmost importance to further study WAK genes as new players in race-specific resistance in cereals. One could consider the possibility to “capture” all WAK genes present in a PGR collection and test for resistance in PGR similarly to what has been done by Arora et al. (2018).

Summary points

  • Genebanks form the basis for studies on the genetic diversity of resistance and provide the raw material for resistance breeding. Our success translating genomic variants into desired resistant phenotypes will largely depend on our capability to wisely select GRP collections by, for example, allele-mining tools such as FIGS. These populations can be now accurately genotyped and phenotyped.

  • There is a wide range of rapid and accurate approaches for the isolation of major resistance genes. These approaches must be used to isolate all useful, genetically described resistance genes. Furthermore, new R and QDR genes need to be identified to further introgress them by MACB, genetic transformation or genome editing.

  • Resistance breeding should be multigenic in nature. Its genetic components should be tested beforehand to avoid, for example, incompatibilities as exemplified by the Pm3Pm8 suppression case. If genes are cloned, transient expression assays like the N. benthamiana give rapid answers.

  • R gene diversity is not limited to NLR-encoding genes, but encompasses other players, such as WAKs. The identification of all type of genetic components involved in resistance is of utmost importance to improve crop disease resistance.

  • Transcriptomics has shown as useful tool for the genetic dissection of both R and QDR genes and for studying pathogen reaction as well. The effective incorporation of transcriptomics in resistance gene isolation will be decisive for the future isolation of many resistance components.

  • Eliminating susceptibility genes by genome editing is conditional upon agronomic viability, which requires a one-by-one evaluation.

  • The establishment of pangenomes will expedite resistance gene cloning. This requires large international collaborations.

  • The isolation of the corresponding avirulence genes for each disease resistance gene will greatly help to get a better molecular understanding of disease resistance. The information derived will further our knowledge in Avr/R gene biology that could guide long-term durable resistance breeding strategies.

  • The (re-)emerging research area of field pathogenomics will play a substantial role in resistance breeding. Constant spatial–temporal pathogen population surveillance programs for major crop diseases need to be implemented to study pathogen diversity and diversity and guide resistance deployment strategies as well.

  • The development of robust phenotyping tools is urgently needed to close the current phenotyping bottleneck that prevents us from translating the vast and now accessible genetic information to desired resistant phenotypes.

Author contribution statement

JSM and BK conceived the ideas, compiled the literature sources and wrote the manuscript.



We apologize to the authors whose work was not mentioned here due to the space constraints. JSM is partially supported by the University Research Priority Programs (URPP) of the University of Zurich. We would like to thank Victoria Widrig for support with the preparation of Fig. 2. Finally, we would like to thanks to the anonymous reviewer for many valuable comments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Abe A, Kosugi S, Yoshida K et al (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 30:174–178. Google Scholar
  2. Allen AM, Barker GLA, Wilkinson P et al (2013) Discovery and development of exome-based, co-dominant single nucleotide polymorphism markers in hexaploid wheat (Triticum aestivum L.). Plant Biotechnol J 11:279–295. Google Scholar
  3. Allen AM, Winfield MO, Burridge AJ et al (2017) Characterization of a Wheat Breeders’ Array suitable for high-throughput SNP genotyping of global accessions of hexaploid bread wheat (Triticum aestivum). Plant Biotechnol J 15:390–401. Google Scholar
  4. Araus JL, Kefauver SC, Zaman-Allah M et al (2018) Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23:451–466. Google Scholar
  5. Arora S, Steuernagel B, Johnson R et al (2018) Resistance gene discovery and cloning by sequence capture and association genetics. bioRxiv 1–12.
  6. Atienza SG, Jafary H, Niks RE (2004) Accumulation of genes for susceptibility to rust fungi for which barley is nearly a nonhost results in two barley lines with extreme multiple susceptibility. Planta 220:71–79. Google Scholar
  7. Avni R, Nave M, Barad O et al (2017) Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science (80-) 1:80. Google Scholar
  8. Ayliffe M, Devilla R, Mago R et al (2011) Nonhost resistance of rice to rust pathogens. Mol Plant Microbe Interact 24:1143–1155. Google Scholar
  9. Bajgain P, Rouse MN, Tsilo TJ et al (2016) Nested association mapping of stem rust resistance in wheat using genotyping by sequencing. PLoS ONE 11:e0155760. Google Scholar
  10. Bauer E, Schmutzer T, Barilar I et al (2017) Towards a whole-genome sequence for rye (Secale cereale L.). Plant J 89:853–869. Google Scholar
  11. Bayer MM, Rapazote-Flores P, Ganal M et al (2017) Development and evaluation of a barley 50k iSelect SNP array. Front Plant Sci 8:1792. Google Scholar
  12. Bettgenhaeuser J, Gilbert B, Ayliffe M, Moscou MJ (2014) Nonhost resistance to rust pathogens—a continuation of continua. Front Plant Sci. 5:664Google Scholar
  13. Bevan MW, Uauy C, Wulff BBH et al (2017) Genomic innovation for crop improvement. Nature 543:346–354. Google Scholar
  14. Bhullar NK, Street K, Mackay M et al (2009) Unlocking wheat genetic resources for the molecular identification of previously undescribed functional alleles at the Pm3 resistance locus. Proc Natl Acad Sci USA 106:9519–9524. Google Scholar
  15. Bhullar NK, Mackay M, Keller B (2010a) Genetic diversity of the pm3 powdery mildew resistance alleles in wheat gene bank accessions as assessed by molecular markers. Diversity 2:768–786. Google Scholar
  16. Bhullar NK, Zhang Z, Wicker T, Keller B (2010b) Wheat gene bank accessions as a source of new alleles of the powdery mildew resistance gene Pm3: a large scale allele mining project. BMC Plant Biol 10:88. Google Scholar
  17. Bourras S, Praz CR, Spanu PD, Keller B (2018) Cereal powdery mildew effectors: a complex toolbox for an obligate pathogen. Curr Opin Microbiol 46:26–33. Google Scholar
  18. Brown JKM (2015) Durable resistance of crops to disease: a Darwinian perspective. Annu Rev Phytopathol 53:513–539. Google Scholar
  19. Brunner S, Hurni S, Streckeisen P et al (2010) Intragenic allele pyramiding combines different specificities of wheat Pm3 resistance alleles. Plant J 64:433–445. Google Scholar
  20. Brunner S, Hurni S, Herren G et al (2011) Transgenic Pm3b wheat lines show resistance to powdery mildew in the field. Plant Biotechnol J 9:897–910. Google Scholar
  21. Brunner S, Stirnweis D, Diaz Quijano C et al (2012) Transgenic Pm3 multilines of wheat show increased powdery mildew resistance in the field. Plant Biotechnol J 10:398–409. Google Scholar
  22. Buckler ES, Holland JB, Bradbury PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718. Google Scholar
  23. Burdon JJ, Zhan J, Barrett LG et al (2016) Addressing the challenges of pathogen evolution on the world’s arable crops. Phytopathology 106:1117–1127. Google Scholar
  24. Büschges R, Hollricher K, Panstruga R et al (1997) The barley <em> Mlo </em> gene: a novel control element of plant pathogen resistance. Cell 88:695–705. Google Scholar
  25. Cesari S (2018) Multiple strategies for pathogen perception by plant immune receptors. New Phytol 219:17–24. Google Scholar
  26. Chakraborty S, Newton AC (2011) Climate change, plant diseases and food security: an overview. Plant Pathol 60:2–14. Google Scholar
  27. Chen H, Xie W, He H et al (2014) A high-density SNP genotyping array for rice biology and molecular breeding. Mol Plant 7:541–553. Google Scholar
  28. Chu Z, Yuan M, Yao J et al (2006) Promoter mutations of an essential gene for pollen development result in disease resistance in rice. Genes Dev 20:1250–1255. Google Scholar
  29. Cong L, Ran FA, Cox D et al (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339:819–823. Google Scholar
  30. Cook DE, Mesarich CH, Thomma BPHJ (2015) Understanding plant immunity as a surveillance system to detect invasion. Annu Rev Phytopathol 53:541–563. Google Scholar
  31. Cooper J, Dobson H (2007) The benefits of pesticides to mankind and the environment. Crop Prot 26:1337–1348Google Scholar
  32. Corwin JA, Kliebenstein DJ (2017) Quantitative resistance: more than just perception of a pathogen. Plant Cell 29:655–665. Google Scholar
  33. Couto D, Zipfel C (2016) Regulation of pattern recognition receptor signalling in plants. Nat Rev Immunol 16:537Google Scholar
  34. Crop Trust (2018) Ex situ conservation strategies. Accessed 1 Aug 2018
  35. Dangl JL, Horvath DM, Staskawicz BJ (2013) Pivoting the plant immune system from dissection to deployment. Science (80-) 341:746–751. Google Scholar
  36. Das A, Soubam D, Singh PK et al (2012) A novel blast resistance gene, Pi54rh cloned from wild species of rice, Oryza rhizomatis confers broad spectrum resistance to Magnaporthe oryzae. Funct Integr Genom 12:215–228. Google Scholar
  37. de Azevedo Peixoto L, Moellers TC, Zhang J et al (2017) Leveraging genomic prediction to scan germplasm collection for crop improvement. PLoS ONE 12:e0179191. Google Scholar
  38. De la Concepcion JC, Franceschetti M, Maqbool A et al (2018) Polymorphic residues in rice NLRs expand binding and response to effectors of the blast pathogen. Nat Plants 4:576–585. Google Scholar
  39. Debieu M, Huard-Chauveau C, Genissel A et al (2016) Quantitative disease resistance to the bacterial pathogen X anthomonas campestris involves an Arabidopsis immune receptor pair and a gene of unknown function. Mol Plant Pathol 17:510–520. Google Scholar
  40. Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet 3:22Google Scholar
  41. Devanna NB, Vijayan J, Sharma TR (2014) The blast resistance gene Pi54of cloned from Oryza officinalis interacts with Avr-Pi54 through its novel non-LRR domains. PLoS ONE 9:e104840. Google Scholar
  42. Dodds PN, Rathjen JP (2010) Plant immunity: towards an integrated view of plant–pathogen interactions. Nat Rev Genet 11:539–548. Google Scholar
  43. Dormann CF, Schweiger O, Augenstein I et al (2007) Effects of landscape structure and land-use intensity on similarity of plant and animal communities. Glob Ecol Biogeogr. Google Scholar
  44. Douchkov D, Baum T, Ihlow A, Schweizer P, Seiffert U (2013) Microphenomics for interaction of barley with fungal pathogens. In: Tuberosa R, Graner A, Frison E (eds) Genomics of plant genetic resources. Springer, Dordrecht, The Netherlands, pp 123–148Google Scholar
  45. Ellis JG, Lagudah ES, Spielmeyer W, Dodds PN (2014) The past, present and future of breeding rust resistant wheat. Front Plant Sci 5:641. Google Scholar
  46. Elmore JM, Perovic D, Ordon F, Schweizer P, Wise RP (2018) A genomic view of biotic stress resistance. In: Stein N, Muehlbauer J (eds) The barley genome. Springer, Switzerland, pp 233–257. Google Scholar
  47. Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379. Google Scholar
  48. Endresen DTF, Street K, Mackay M et al (2012) Sources of resistance to stem rust (Ug99) in bread wheat and durum wheat identified using focused identification of germplasm strategy. Crop Sci 52:764. Google Scholar
  49. Enserink M, Hines PJ, Vignieri SN et al (2013) The pesticide paradox. Science (80-) 341:728–729. Google Scholar
  50. Fu D, Uauy C, Distelfeld A et al (2009) A kinase-START gene confers temperature-dependent resistance to wheat stripe rust. Science (80-) 323:1357–1360. Google Scholar
  51. Fukuoka S, Saka N, Koga H et al (2009) Loss of function of a proline-containing protein confers durable disease resistance in rice. Science (80-) 325:998–1001. Google Scholar
  52. Fukuoka S, Yamamoto S-I, Mizobuchi R et al (2014) Multiple functional polymorphisms in a single disease resistance gene in rice enhance durable resistance to blast. Sci Rep 4:4550Google Scholar
  53. Fukuoka S, Saka N, Mizukami Y et al (2015a) Gene pyramiding enhances durable blast disease resistance in rice. Sci Rep 5:7773. Google Scholar
  54. Fukuoka S, Yamamoto S-I, Mizobuchi R et al (2015b) Multiple functional polymorphisms in a single disease resistance gene in rice enhance durable resistance to blast. Sci Rep 4:4550. Google Scholar
  55. Furbank RT, Tester M (2011) Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644. Google Scholar
  56. German SE, Kolmer JA (1992) Effect of gene Lr34 in the enhancement of resistance to leaf rust of wheat. Theor Appl Genet 84:97–105. Google Scholar
  57. Gutiérrez L, Germán S, Pereyra S et al (2015) Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America. Theor Appl Genet 128:501–516. Google Scholar
  58. Hajjar R, Hodgkin T (2007) The use of wild relatives in crop improvement: a survey of developments over the last 20 years. Euphytica 156:1–13. Google Scholar
  59. Heath MC (2000) Nonhost resistance and nonspecific plant defenses. Curr Opin Plant Biol 3:315–319. Google Scholar
  60. Hickey LT, Germán SE, Pereyra SA et al (2017) Speed breeding for multiple disease resistance in barley. Euphytica 213:64. Google Scholar
  61. Hittalmani S, Parco A, Mew TV et al (2000) Fine mapping and DNA marker-assisted pyramiding of the three major genes for blast resistance in rice. TAG Theor Appl Genet 100:1121–1128. Google Scholar
  62. Hobbs PR, Sayre K, Gupta R (2008) The role of conservation agriculture in sustainable agriculture. Philos Trans R Soc Lond B Biol Sci 363:543–555. Google Scholar
  63. Holland J (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161. Google Scholar
  64. Hsam SLK, Zeller FJ (2002) Breeding for powdery mildew resistance in common wheat. In: Bélanger R, Bushnell W, Dik A, Carver TLW (eds) The powdery mildews: a comprehensive treatise. American Phytopathological Society (APS Press), St. Paul, p 292Google Scholar
  65. Huang L, Sela H, Feng L et al (2016) Distribution and haplotype diversity of WKS resistance genes in wild emmer wheat natural populations. Theor Appl Genet 129:921–934. Google Scholar
  66. Huard-Chauveau C, Perchepied L, Debieu M et al (2013) An atypical kinase under balancing selection confers broad-spectrum disease resistance in arabidopsis. PLoS Genet 9:e1003766. Google Scholar
  67. Hubbard A, Lewis CM, Yoshida K et al (2015) Field pathogenomics reveals the emergence of a diverse wheat yellow rust population. Genome Biol 16:23. Google Scholar
  68. Hurni S, Brunner S, Stirnweis D et al (2014) The powdery mildew resistance gene Pm8 derived from rye is suppressed by its wheat ortholog Pm3. Plant J 79:904–913. Google Scholar
  69. Hurni S, Scheuermann D, Krattinger SG et al (2015) The maize disease resistance gene Htn1 against northern corn leaf blight encodes a wall-associated receptor-like kinase. Proc Natl Acad Sci USA 112:8780–8785. Google Scholar
  70. Inoue Y, Vy TTP, Yoshida K et al (2017) Evolution of the wheat blast fungus through functional losses in a host specificity determinant. Science 357:80–83. Google Scholar
  71. International Rice Genome Sequencing Project (2005) The map-based sequence of the rice genome. Nature. Google Scholar
  72. International Wheat Genome Sequencing Consortium (IWGSC) (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361.
  73. Johnston PA, Niks RE, Meiyalaghan V et al (2013) Rph22: mapping of a novel leaf rust resistance gene introgressed from the non-host Hordeum bulbosum L. into cultivated barley (Hordeum vulgare L.). Theor Appl Genet 126:1613–1625. Google Scholar
  74. Jones JDG, Dangl JL (2006) The plant immune system. Nature 444:323–329. Google Scholar
  75. Jones JDG, Vance RE, Dangl JL (2016) Intracellular innate immune surveillance devices in plants and animals. Science 354:aaf6395. Google Scholar
  76. Jorgensen JH (1992) Discovery, characterization and exploitation of Mlo powdery mildew resistance in barley. Euphytica 63:141–152. Google Scholar
  77. Jørgensen LN, Hovmøller MS, Hansen JG et al (2014) IPM strategies and their dilemmas including an introduction to J Integr Agric 13:265–281. Google Scholar
  78. Jupe F, Witek K, Verweij W et al (2013) Resistance gene enrichment sequencing (RenSeq) enables reannotation of the NB-LRR gene family from sequenced plant genomes and rapid mapping of resistance loci in segregating populations. Plant J. Google Scholar
  79. Karisto P, Hund A, Yu K et al (2018) Ranking quantitative resistance to septoria tritici blotch in elite wheat cultivars using automated image analysis. Phytopathology 108:568–581. Google Scholar
  80. Kaur N, Street K, Mackay M et al (2008) Molecular approaches for characterization and use of natural disease resistance in wheat. In: Collinge DB, Munk L, Cooke BM (eds) Sustainable disease management in a European context. Springer, Amsterdam, pp 387–397Google Scholar
  81. Keller B, Wicker T, Krattinger SG (2018) Advances in wheat and pathogen genomics: implications for disease control. Annu Rev Phytopathol 56:67–87Google Scholar
  82. Khatodia S, Bhatotia K, Passricha N et al (2016) The CRISPR/Cas genome-editing tool: application in improvement of crops. Front Plant Sci 7:506. Google Scholar
  83. Kilian B, Mammen K, Millet E et al (2011) Aegilops. Wild crop relatives: genomic and breeding resources. Springer, Berlin, pp 1–76Google Scholar
  84. King R, Bird N, Ramirez-Gonzalez R et al (2015) Mutation scanning in wheat by exon capture and next-generation sequencing. PLoS ONE 10:e0137549. Google Scholar
  85. King J, Grewal S, Yang C-Y et al (2017) A step change in the transfer of interspecific variation into wheat from Amblyopyrum muticum. Plant Biotechnol J 15:217–226. Google Scholar
  86. Koller T, Brunner S, Herren G et al (2018) Pyramiding of transgenic Pm3 alleles in wheat results in improved powdery mildew resistance in the field. Theor Appl Genet 131:861–871. Google Scholar
  87. Krasileva KV, Vasquez-Gross HA, Howell T et al (2017) Uncovering hidden variation in polyploid wheat. Proc Natl Acad Sci USA 114:E913–E921. Google Scholar
  88. Krattinger SG, Lagudah ES, Spielmeyer W et al (2009) A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science (80-) 323:1360–1363. Google Scholar
  89. Kumari M, Rai AK, Devanna BN et al (2017) Co-transformation mediated stacking of blast resistance genes Pi54 and Pi54rh in rice provides broad spectrum resistance against Magnaporthe oryzae. Plant Cell Rep 36:1747–1755. Google Scholar
  90. Lacombe S, Rougon-Cardoso A, Sherwood E et al (2010) Interfamily transfer of a plant pattern-recognition receptor confers broad-spectrum bacterial resistance. Nat Biotechnol 28:365Google Scholar
  91. Lagudah ES (2011) Molecular genetics of race non-specific rust resistance in wheat. Euphytica 179:81–91. Google Scholar
  92. Lamichhane JR, Dachbrodt-Saaydeh S, Kudsk P, Messéan A (2015) Toward a reduced reliance on conventional pesticides in European agriculture. Plant Dis 100:10–24. Google Scholar
  93. Lee W-S, Hammond-Kosack KE, Kanyuka K (2012) Barley stripe mosaic virus-mediated tools for investigating gene function in cereal plants and their pathogens: virus-induced gene silencing, host-mediated gene silencing, and virus-mediated overexpression of heterologous protein. Plant Physiol 160:582–590. Google Scholar
  94. Lee S, Whitaker VM, Hutton SF (2016) Mini review: potential applications of non-host resistance for crop improvement. Front Plant Sci 7:1–6. Google Scholar
  95. Li ZK, Sanchez A, Angeles E et al (2001) Are the dominant and recessive plant disease resistance genes similar? A case study of rice R genes and Xanthomonas oryzae pv. oryzae races. Genetics 159:757–765Google Scholar
  96. Ling HQ, Ma B, Shi X et al (2018) Genome sequence of the progenitor of wheat A subgenome Triticum urartu. Nature. Google Scholar
  97. Liu J, Liu D, Tao W et al (2000) Molecular marker-facilitated pyramiding of different genes for powdery mildew resistance in wheat. Plant Breed 119:21–24. Google Scholar
  98. Loutre C, Wicker T, Travella S et al (2009) Two different CC-NBS-LRR genes are required for Lr10-mediated leaf rust resistance in tetraploid and hexaploid wheat. Plant J 60:1043–1054. Google Scholar
  99. Lucas JA, Hawkins NJ, Fraaije BA (2015) The evolution of fungicide resistance. Adv Appl Microbiol 90:29–92. Google Scholar
  100. Luo MC, Gu YQ, Puiu D et al (2017) Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature. Google Scholar
  101. Macho AP, Zipfel C (2014) Plant PRRs and the activation of innate immune signaling. Mol Cell 54:263–272. Google Scholar
  102. Mackay M, Street K (2004) Focused identification of germplasm strategy-FIGS. In: Cereal Chemestry Division RACI (RACI) (ed) Proceedings of the 54th Australian cereal chemistry conference and the 11th wheat breeders’ Assembly. Melbourne, Victoria, Australia, pp 138–141Google Scholar
  103. Mackay TFC, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10:565–577. Google Scholar
  104. Mahlein A-K (2016) Plant disease detection by imaging sensors—parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100:241–251. Google Scholar
  105. Marasas CN, Smale M, Singh RP (2004) The economic impact in developing countries of leaf rust resistance breeding in CIMMYT-RELATED spring bread wheat. Mexic, D.F.Google Scholar
  106. Mascher M, Gundlach H, Himmelbach A et al (2017) A chromosome conformation capture ordered sequence of the barley genome. Nature 544:427–433. Google Scholar
  107. Matzke AJM, Matzke MA (1998) Position effects and epigenetic silencing of plant transgenes. Curr Opin Plant Biol 1:142–148. Google Scholar
  108. McCouch S, Baute GJ, Bradeen J et al (2013) Agriculture: feeding the future. Nature 499(7456):23–24Google Scholar
  109. McCouch SR, Wright MH, Tung C-W et al (2016) Open access resources for genome-wide association mapping in rice. Nat Commun 7:10532. Google Scholar
  110. McDonald BA, Linde C (2002) The population genetics of plant pathogens and breeding strategies for durable resistance. Euphytica 124:163–180. Google Scholar
  111. McDonald BA, Stukenbrock EH (2016) Rapid emergence of pathogens in agro-ecosystems: global threats to agricultural sustainability and food security. Philos Trans R Soc Lond B Biol Sci. Google Scholar
  112. McDowell JM, Woffenden BJ (2003) Plant disease resistance genes: recent insights and potential applications. Trends Biotechnol 21:178–183. Google Scholar
  113. McGrann GRD, Stavrinides A, Russell J et al (2014) A trade off between mlo resistance to powdery mildew and increased susceptibility of barley to a newly important disease, Ramularia leaf spot. J Exp Bot 65:1025–1037. Google Scholar
  114. Menardo F, Praz CR, Wyder S et al (2016) Hybridization of powdery mildew strains gives rise to pathogens on novel agricultural crop species. Nat Genet 48:201–205. Google Scholar
  115. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829Google Scholar
  116. Mgonja EM, Park CH, Kang H et al (2017) Genotyping-by-sequencing-based genetic analysis of african rice cultivars and association mapping of blast resistance genes against Magnaporthe oryzae populations in Africa. Phytopathology 107:1039–1046. Google Scholar
  117. Miedaner T, Korzun V (2012) Marker-assisted selection for disease resistance in wheat and barley breeding. Phytopathology 102:560–566Google Scholar
  118. Möller M, Stukenbrock EH (2017) Evolution and genome architecture in fungal plant pathogens. Nat Rev Microbiol 15:756–771Google Scholar
  119. Monaghan J, Zipfel C (2012) Plant pattern recognition receptor complexes at the plasma membrane. Curr Opin Plant Biol 15:349–357. Google Scholar
  120. Moore JW, Herrera-Foessel S, Lan C et al (2015) A recently evolved hexose transporter variant confers resistance to multiple pathogens in wheat. Nat Genet 47:1494–1498. Google Scholar
  121. Müller T, Schierscher-Viret B, Fossati D et al (2018) Unlocking the diversity of genebanks: whole-genome marker analysis of Swiss bread wheat and spelt. Theor Appl Genet 131:407–416. Google Scholar
  122. Mundt CC (2002) Use of multiline cultivars and cultivar mixtures for disease management. Annu Rev Phytopathol 40:381–410. Google Scholar
  123. Mutka AM, Bart RS (2014) Image-based phenotyping of plant disease symptoms. Front Plant Sci 5:734. Google Scholar
  124. Niks RE, Marcel TC (2009) Nonhost and basal resistance: how to explain specificity? New Phytol 182:817–828. Google Scholar
  125. Niks RE, Qi X, Marcel TC (2015) Quantitative resistance to biotrophic filamentous plant pathogens: concepts, misconceptions, and mechanisms. Annu Rev Phytopathol 53:445–470. Google Scholar
  126. Noël L, Moores TL, van der Biezen EA et al (1999) Pronounced intraspecific haplotype divergence at the RPP5 complex disease resistance locus of arabidopsis. Plant Cell 11:2099–2111Google Scholar
  127. Nunes CC, Dean RA (2012) Host-induced gene silencing: a tool for understanding fungal host interaction and for developing novel disease control strategies. Mol Plant Pathol 13:519–529. Google Scholar
  128. Oerke EC (2006) Crop losses to pests. J Agric, SciGoogle Scholar
  129. Pace J, Yu X, Lübberstedt T (2015) Genomic prediction of seedling root length in maize (Zea mays L.). Plant J 83:903–912. Google Scholar
  130. Parlevliet JE (1992) Selecting components of partial resistance. In: Stalker H, Murphy J (eds) Plant breeding in the 1990s. CAB Institute, Wallingford, pp 281–302Google Scholar
  131. Parlevliet JE (2002) Durability of resistance against fungal, bacterial and viral pathogens; present situation. Euphytica 124:147–156Google Scholar
  132. Parlevliet JE, Kuiper HJ (1985) Accumulating polygenes for partial resistance in barley to barley leaf rust, Puccinia hordei. I. Selection for increased latent periods. Euphytica 34:7–13. Google Scholar
  133. Parlevliet JE, van Ommeren A (1988) Accumulation of partial resistance in barley to barley leaf rust and powdery mildew through recurrent selection against susceptibility. Euphytica 37:261–274. Google Scholar
  134. Parlevliet JE, Leijn M, Van Ommeren A (1985) Accumulating polygenes for partial resistance in barley to barley leaf rust, Puccinia hordei. II. Field evaluation. Euphytica 34:15–20. Google Scholar
  135. Pavan S, Jacobsen E, Visser RGF, Bai Y (2010) Loss of susceptibility as a novel breeding strategy for durable and broad-spectrum resistance. Mol Breed 25:1–12Google Scholar
  136. Periyannan S (2018) Sustaining global agriculture through rapid detection and deployment of genetic resistance to deadly crop diseases. New Phytol 219:45–51. Google Scholar
  137. Pink DAC (2002) Strategies using genes for non-durable disease resistance. In: Euphytica, pp 227–236Google Scholar
  138. Poland JA, Nelson RJ (2011) In the eye of the beholder: the effect of rater variability and different rating scales on QTL mapping. Phytopathology 101:290–298Google Scholar
  139. Poland J, Rutkoski J (2016) Advances and challenges in genomic selection for disease resistance. Annu Rev Phytopathol 54:79–98. Google Scholar
  140. Poland JA, Balint-Kurti PJ, Wisser RJ et al (2009) Shades of gray: the world of quantitative disease resistance. Trends Plant Sci 14:21–29. Google Scholar
  141. Pruitt RN, Schwessinger B, Joe A et al (2015) The rice immune receptor XA21 recognizes a tyrosine-sulfated protein from a Gram-negative bacterium. Sci Adv 1:e1500245. Google Scholar
  142. Raboin L-M, Ballini E, Tharreau D et al (2016) Association mapping of resistance to rice blast in upland field conditions. Rice 9:59. Google Scholar
  143. Rieseberg LH, Archer MA, Wayne RK (1999) Transgressive segregation, adaptation and speciation. Heredity (Edinb) 83:363Google Scholar
  144. Risk JM, Selter LL, Chauhan H et al (2013) The wheat Lr34 gene provides resistance against multiple fungal pathogens in barley. Plant Biotechnol J 11:847–854. Google Scholar
  145. Rodrigues P, Garrood JM, Shen Q-H et al (2004) The genetics of non-host disease resistance in wheat to barley yellow rust. Theor Appl Genet 109:425–432. Google Scholar
  146. Rodriguez-Moreno L, Song Y, Thomma BP (2017) Transfer and engineering of immune receptors to improve recognition capacities in crops. Curr Opin Plant Biol 38:42–49. Google Scholar
  147. Roelfs AP (1988) Genetic control of phenotypes in wheat stem rust*. Annu Rev Phytopathol 26:351–367. Google Scholar
  148. Rousseau C, Belin E, Bove E et al (2013) High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis. Plant Methods 9:17. Google Scholar
  149. Roux F, Voisin D, Badet T et al (2014) Resistance to phytopathogens e tutti quanti: placing plant quantitative disease resistance on the map. Mol Plant Pathol 15:427–432. Google Scholar
  150. Ruengphayak S, Chaichumpoo E, Phromphan S et al (2015) Pseudo-backcrossing design for rapidly pyramiding multiple traits into a preferential rice variety. Rice (N Y) 8:7. Google Scholar
  151. Saintenac C, Lee WS, Cambon F et al (2018) Wheat receptor-kinase-like protein Stb6 controls gene-for-gene resistance to fungal pathogen Zymoseptoria tritici. Nat Genet. Google Scholar
  152. Sánchez-Martín J, Bourras S, Keller B (2018) Diseases affecting wheat and barley: powdery mildew. In: Oliver R (ed) Integrated disease management of wheat and barley. Burleigh Dodds Science Publishing Limited, Cambridge, UK, pp 69–93Google Scholar
  153. Sánchez-Martín J, Steuernagel B, Ghosh S et al (2016) Rapid gene isolation in barley and wheat by mutant chromosome sequencing. Genome Biol. Google Scholar
  154. Saxena RK, Edwards D, Varshney RK (2014) Structural variations in plant genomes. Brief Funct Genom 13:296–307. Google Scholar
  155. Schulze-lefert P, Panstruga R (2011) A molecular evolutionary concept connecting nonhost resistance, pathogen host range, and pathogen speciation. Trends Plant Sci 16:117–125. Google Scholar
  156. Sears ER, Miller TE (1985) The history of Chinese Spring wheat. Cereal Res Commun 13:261–263Google Scholar
  157. Seeholzer S, Tsuchimatsu T, Jordan T et al (2010) Diversity at the Mla powdery mildew resistance locus from cultivated barley reveals sites of positive selection. Mol Plant Microbe Interact 23:497–509. Google Scholar
  158. Shakoor N, Lee S, Mockler TC (2017) High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr Opin Plant Biol 38:184–192. Google Scholar
  159. SHAPE (2018) Structural genome variation, haplotype diversity and the barley pan-genome—exploring structural genome diversity for barley breeding. Accessed 15 Jul 2018
  160. Shiferaw B, Smale M, Braun HJ et al (2013) Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Secur 5:291–317. Google Scholar
  161. Shtaya MJY, Sillero JC, Flath K et al (2007) The resistance to leaf rust and powdery mildew of recombinant lines of barley (Hordeum vulgare L.) derived from H. vulgare x H. bulbosum crosses. Plant Breed 126(3):259–267Google Scholar
  162. Simko I, Jimenez-Berni JA, Sirault XRR (2017) Phenomic approaches and tools for phytopathologists. Phytopathology 107:6–17. Google Scholar
  163. Singh RP, Trethowan R (2007) Breeding spring bread wheat for irrigated and rainfed production systems of the developing world. In: Kang MS, Priyadarshan PM (eds) Breeding major food staples. BlacWell Publishing, Iowa, pp 107–140Google Scholar
  164. Singh RP, Herrera-Foessel S, Huerta-Espino J et al (2014) Progress towards genetics and breeding for minor genes based resistance to Ug99 and other rusts in CIMMYT high-yielding spring wheat. J Integr Agric 13:255–261. Google Scholar
  165. Song WY, Wang GL, Chen LL et al (1995) A receptor kinase-like protein encoded by the rice disease resistance gene, Xa21. Science (80-). Google Scholar
  166. Srichumpa P, Brunner S, Keller B, Yahiaoui N (2005) Allelic series of four powdery mildew resistance genes at the Pm3 locus in hexaploid bread wheat. Plant Physiol 139:885–895. Google Scholar
  167. St.Clair DA (2010) Quantitative disease resistance and quantitative resistance loci in breeding. Annu Rev Phytopathol 48:247–268. Google Scholar
  168. Steuernagel B, Periyannan SK, Hernández-Pinzón I et al (2016) Rapid cloning of disease-resistance genes in plants using mutagenesis and sequence capture. Nat Biotechnol 34:652–655. Google Scholar
  169. Steuernagel B, Witek K, Krattinger SG et al (2018) Physical and transcriptional organisation of the bread wheat intracellular immune receptor repertoire. bioRxiv 339424.
  170. Stirnweis D, Milani SD, Jordan T et al (2014) Substitutions of two amino acids in the nucleotide-binding site domain of a resistance protein enhance the hypersensitive response and enlarge the PM3F resistance spectrum in wheat. MPMI 27:265–276. Google Scholar
  171. Sucher J, Menardo F, Praz CR et al (2018) Transcriptional profiling reveals no response of fungal pathogens to the durable, quantitative Lr34 disease resistance gene of wheat. Plant Pathol 67:792–798. Google Scholar
  172. The 3000 Rice Genomes Project (2014) The 3,000 rice genomes project. Gigascience 3:7. Google Scholar
  173. Thind AK, Wicker T, Simkova H et al (2017) Rapid cloning of genes in hexaploid wheat using cultivar-specific long-range chromosome assembly. Nat Biotechnol. Google Scholar
  174. Thind AK, Wicker T, Müller T et al (2018) Chromosome-scale comparative sequence analysis unravels molecular mechanisms of genome dynamics between two wheat cultivars. Genome Biol 19:104. Google Scholar
  175. Thomas S, Behmann J, Steier A et al (2018) Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods 14:45. Google Scholar
  176. Thomma BPHJ, Nürnberger T, Joosten MHAJ (2011) Of PAMPs and effectors: the blurred PTI-ETI dichotomy. Plant Cell 23:4 LP-15Google Scholar
  177. Togninalli M, Seren Ü, Meng D et al (2018) The AraGWAS catalog: a curated and standardized Arabidopsis thaliana GWAS catalog. Nucleic Acids Res 46:D1150–D1156. Google Scholar
  178. Tsuda K, Katagiri F (2010) Comparing signaling mechanisms engaged in pattern-triggered and effector-triggered immunity. Curr Opin Plant Biol 13:459–465. Google Scholar
  179. Vanegas CDG, Garvin DF, Kolmer JA (2008) Genetics of stem rust resistance in the spring wheat cultivar Thatcher and the enhancement of stem rust resistance by Lr34. Euphytica 159:391–401. Google Scholar
  180. Vasudevan K, Vera Cruz CM, Gruissem W, Bhullar NK (2014) Large scale germplasm screening for identification of novel rice blast resistance sources. Front Plant Sci 5:505. Google Scholar
  181. Vasudevan K, Gruissem W, Bhullar NK (2015) Identification of novel alleles of the rice blast resistance gene Pi54. Sci Rep. Google Scholar
  182. Vatter T, Maurer A, Kopahnke D et al (2017) A nested association mapping population identifies multiple small effect QTL conferring resistance against net blotch (Pyrenophora teres f. teres) in wild barley. PLoS ONE 12:e0186803. Google Scholar
  183. Velásquez AC, Castroverde CDM, He SY (2018) Plant-pathogen warfare under changing climate conditions. Curr Biol 28:R619–R634. Google Scholar
  184. Vera Cruz CM, Bai J, Ona I et al (2000) Predicting durability of a disease resistance gene based on an assessment of the fitness loss and epidemiological consequences of avirulence gene mutation. Proc Natl Acad Sci USA 97:13500–13505. Google Scholar
  185. Vleeshouwers VGAA, Oliver RP (2015) Effectors as tools in disease resistance breeding against biotrophic, hemibiotrophic, and necrotrophic plant pathogens. Mol Plant Microbe Interact. Google Scholar
  186. Vogel JP, Garvin DF, Mockler TC et al (2010) Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature 463:763–768. Google Scholar
  187. Wallwork H, Johnson R (1984) Transgressive segregation for resistance to yellow rust in wheat. Euphytica 33:123–132. Google Scholar
  188. Wang Q, Liu Y, He J et al (2014a) STV11 encodes a sulphotransferase and confers durable resistance to rice stripe virus. Nat Commun 5:4768. Google Scholar
  189. Wang S, Wong D, Forrest K et al (2014b) Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnol J 12:787–796. Google Scholar
  190. Wang K, Liu H, Du L, Ye X (2017) Generation of marker-free transgenic hexaploid wheat via an Agrobacterium -mediated co-transformation strategy in commercial Chinese wheat varieties. Plant Biotechnol J 15:614–623. Google Scholar
  191. Watson A, Ghosh S, Williams MJ et al (2018) Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants 4:23–29. Google Scholar
  192. Wendler N, Mascher M, Nöh C et al (2014) Unlocking the secondary gene-pool of barley with next-generation sequencing. Plant Biotechnol J 12:1122–1131. Google Scholar
  193. Wendler N, Mascher M, Himmelbach A et al (2015) Bulbosum to go: a toolbox to utilize Hordeum vulgare/bulbosum introgressions for breeding and beyond. Mol Plant 8:1507–1519. Google Scholar
  194. Wheat Initiative (2018) 10 WHEAT GENOMES PROJECT. Accessed 15 Jul 2018
  195. Winfield MO, Allen AM, Burridge AJ et al (2016) High-density SNP genotyping array for hexaploid wheat and its secondary and tertiary gene pool. Plant Biotechnol J 14:1195–1206. Google Scholar
  196. Wolfe MS (1992) Maintaining the value of our varieties. In: Munk J (ed) Barley genetics VI. Munksgaard International Publishers, Copenhagen, pp 1055–1067Google Scholar
  197. Wulff BBH, Dhugga SK (2018) Wheat—the cereal abandoned by GM. Science (80-) 361:451–452Google Scholar
  198. Wulff BBH, Moscou MJ (2014) Strategies for transferring resistance into wheat: from wide crosses to GM cassettes. Front Plant Sci 5:692. Google Scholar
  199. Wulff BBH, Horvath DM, Ward ER (2011) Improving immunity in crops: new tactics in an old game. Curr Opin Plant Biol 14:468–476. Google Scholar
  200. Yahiaoui N, Srichumpa P, Dudler R, Keller B (2004) Genome analysis at different ploidy levels allows cloning of the powdery mildew resistance gene Pm3b from hexaploid wheat. Plant J 37:528–538. Google Scholar
  201. Yahiaoui N, Brunner S, Keller B (2006) Rapid generation of new powdery mildew resistance genes after wheat domestication. Plant J 47:85–98. Google Scholar
  202. Yahiaoui N, Kaur N, Keller B (2009) Independent evolution of functional Pm3 resistance genes in wild tetraploid wheat and domesticated bread wheat. Plant J 57:846–856. Google Scholar
  203. Yan J, Warburton M, Crouch J (2011) Association mapping for enhancing maize (Zea mays L.) genetic improvement. Crop Sci 51:433. Google Scholar
  204. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551. Google Scholar
  205. Zaidi SS-A, Mukhtar MS, Mansoor S (2018) Genome editing: targeting susceptibility genes for plant disease resistance. Trends Biotechnol 36:898–906. Google Scholar
  206. Zhang M, Coaker G (2017) Harnessing effector-triggered immunity for durable disease resistance. Phytopathology 107:912–919. Google Scholar
  207. Zhao B, Lin X, Poland J et al (2005) A maize resistance gene functions against bacterial streak disease in rice. Proc Natl Acad Sci 102:15383–15388. Google Scholar
  208. Zhao Q, Feng Q, Lu H et al (2018) Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice. Nat Genet 50:278–284. Google Scholar
  209. Zheng Z, Wang HB, Chen GD et al (2013) A procedure allowing up to eight generations of wheat and nine generations of barley per annum. Euphytica 191:311–316. Google Scholar
  210. Zhong Z, Marcel TC, Hartmann FE et al (2017) A small secreted protein in Zymoseptoria tritici is responsible for avirulence on wheat cultivars carrying the Stb6 resistance gene. New Phytol 214:619–631. Google Scholar
  211. Zhong Z, Chen M, Lin L et al (2018) Population genomic analysis of the rice blast fungus reveals specific events associated with expansion of three main clades. ISME J. Google Scholar
  212. Zhu Y, Chen H, Fan J et al (2000) Genetic diversity and disease control in rice. Nature 406:718–722. Google Scholar
  213. Zipfel C (2014) Plant pattern-recognition receptors. Trends Immunol 35:345–351. Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Plant and Microbial BiologyUniversity of ZürichZurichSwitzerland

Personalised recommendations