# Genome-wide association studies in plant pathosystems: toward an ecological genomics approach.

Fabrice Roux, Claudia Bartoli

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Fabrice Roux, Claudia Bartoli. Genome-wide association studies in plant pathosystems: toward an ecological genomics approach. Frontiers in Plant Science, 2017, 8, 10.3389/fpls.2017.00763. hal-01606059

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# Genome-Wide Association Studies In Plant Pathosystems: Toward an Ecological Genomics Approach

Claudia Bartoli and Fabrice Roux *

LIPM, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université de Toulouse, Castanet-Tolosan, France

The emergence and re-emergence of plant pathogenic microorganisms are processes that imply perturbations in both host and pathogen ecological niches. Global change is largely assumed to drive the emergence of new etiological agents by altering the equilibrium of the ecological habitats which in turn places hosts more in contact with pathogen reservoirs. In this context, the number of epidemics is expected to increase dramatically in the next coming decades both in wild and crop plants. Under these considerations, the identification of the genetic variants underlying natural variation of resistance is a pre-requisite to estimate the adaptive potential of wild plant populations and to develop new breeding resistant cultivars. On the other hand, the prediction of pathogen's genetic determinants underlying disease emergence can help to identify plant resistance alleles. In the genomic era, whole genome sequencing combined with the development of statistical methods led to the emergence of Genome Wide Association (GWA) mapping, a powerful tool for detecting genomic regions associated with natural variation of disease resistance in both wild and cultivated plants. However, GWA mapping has been less employed for the detection of genetic variants associated with pathogenicity in microbes. Here, we reviewed GWA studies performed either in plants or in pathogenic microorganisms (bacteria, fungi and oomycetes). In addition, we highlighted the benefits and caveats of the emerging joint GWA mapping approach that allows for the simultaneous identification of genes interacting between genomes of both partners. Finally, based on co-evolutionary processes in wild populations, we highlighted a phenotyping-free joint GWA mapping approach as a promising tool for describing the molecular landscape underlying plant - microbe interactions.

**Keywords:** genome-wide association mapping, disease resistance, pathogenicity, crops, microbiota, pathobiota, genome-to-genome analysis, co-evolution

## INTRODUCTION

In the last decade the World Health Organization (WHO) reported more than 300 newly infectious diseases that have emerged as threat for human (Jones et al., 2008). In the same trend, a conspicuous burst of plant diseases has been reported since the beginning of this century (Bartoli et al., 2016). Newly pathogenic microbes can spill over form reservoirs or newly variants of a pre-existing pathogen (Elena et al., 2011). Three main driving factors for Emerging Diseases (ED) occurrences are currently considered in both human and plant pathology: (i) genetic and biological factors

## OPEN ACCESS

**Edited by:**

Mahmut Tör,
University of Worcester,
United Kingdom

**Reviewed by:**

Weixing Shan,
Northwest A&F University, China
Guus Bakkeren,
Agriculture & Agri-Food Canada,
Canada

**\*Correspondence:**

Fabrice Roux
fabrice roux@inra.fr

**Specialty section:**

This article was submitted to
Plant Microbe Interactions,
a section of the journal
Frontiers in Plant Science

**Received:** 22 February 2017

**Accepted:** 24 April 2017

**Published:** 23 May 2017

**Citation:**

Bartoli C and Roux F (2017)
Genome-Wide Association Studies In
Plant Pathosystems: Toward an
Ecological Genomics Approach.
Front. Plant Sci. 8:763.
doi: 10.3389/fpls.2017.00763
both acting on the host and the etiological agent (i.e., changes in host susceptibility and microbial adaptation via genomic rearrangements), (ii) ecological and environmental factors (i.e., climate change, perturbations of the ecological niches of the pathogens and changes in host demography), (iii) social, political and economic factors (i.e., land use and international travel; Smolinski et al., 2003). Whatever the factor we consider the common line in ED is the evolutionary potential of microbes that through genetic changes can bypass the host defense system by spreading over new host populations. However, the evolutionary genetic change of the pathogen is not sufficient for the occurrence of ED and ecological perturbations such as habitat modifications are necessary for the newly pathogen to encounter its host. For example, agricultural practices can drastically alter the environment by offering routes for transmission of pathogens (Institute of Medicine, 2003).

As proposed by (Engering et al., 2013), ED caused by microbial agents can be divided in three major groups. The first disease emergence category concerns those pathogens that emerge in a novel host, a common process that is also called host jump. Pathogenic lines with flexible genomes (or high evolvability behavior) and with high environmental survival are much more prone for host jumps (Engering et al., 2013), although interspecies contact rate—that is burst by the ongoing growth in human population and consumption of animal products—is one of the main drivers for host jump occurrence. For example, the oomycete species *Phytophthora infestans* responsible for the Irish potato famine, experienced several host jumps because of the high rate of genomic rearrangements in non-coding genomic regions (Raffaele et al., 2010). A second type of disease emergence involves mutant pathogenic lines that caused more severe diseases after the acquisition of novel genetic traits. Novel pathogenicity traits can be the result of mutations or horizontal gene transfer and both processes increase pathogen's genetic variability (Bartoli et al., 2016). In light of this, the barley powdery mildew, *Blumeria graminis*, has increased its pathogenicity and has acquired an obligate biotrophy life-style via retrotransposon proliferation and gene loss (Spanu et al., 2010). However, the environment where the pathogenic populations evolve is of fundamental importance for triggering the evolution of new traits increasing aggressiveness in a pathogen. For example, the intensification of antibiotic utilization in agriculture increased antibiotic resistance gene acquisition as well as virulence in several bacterial pathogens (Jones et al., 2008). Examples are the *Escherichia coli* O157:H7 in which its aggressiveness increased after the acquisition of the Shiga toxin plasmid, or the rice fungal pathogen *Cochiliobolus miyabeanus* responsible of Great Bengal Famine of 1943 that gaining non-host specific toxins raised virulence on the rice host (Bruyne et al., 2016). However, in some cases, resistance to antibiotics can also naturally occur in pathogens without the selective pressure of the molecule. For example, in *Pseudomonas viridiflava* hypermutable variants, resistant to several antibiotics, spontaneously occur in synthetic media, as well in planta, in absence of antibiotics (Bartoli et al., 2015). Also, mutations CYP51 conferring azole resistance in different fungal pathogens is not always related to the utilization of fungicides as demonstrated in *Fusarium* spp (Parker et al.,

2014). Lastly, the geographic expansion (or geographic jump) of a pathogen is also a form of disease emergence that can rapidly lead to disease epidemics (Engering et al., 2013). As already mentioned for host jumps, pathogens with more flexible genomes are obviously more prone to expand their geographic range because they can rapidly respond to the environmental conditions of a new habitat. However, human practices are also responsible for the expansion of pathogens' geographic area. In particular, multiple crop epidemics were caused by multiple introductions pathogen populations from restricted geographic regions, as reported for the worldwide spread of kiwifruit bacterial canker caused by the bacterium *Pseudomonas syringae* pv. *actinidiae* originating from China (Kim et al., 2016) and for *P. infestans* (Kamoun et al., 2015).

The challenge of understanding and predicting ED occurrences is even more relevant in the climate change context which is likely to favor conditions for pathogens' development and dispersal (Bergot et al., 2004; Garrett et al., 2006; Tylianakis et al., 2008). Consequently, climate change scenarios predict an increase in the number of epidemics in the next coming decades (Bergot et al., 2004; Evans et al., 2008). However, the relationships between climate change and ED occurrences is also related to the life histories and the infection processes of the pathogens. For example, pathogens living in highly fluctuating environments (such as epiphytes) should be more favored under a climatic change scenario than pathogens strictly adapted to more constant habitats (such as obligate endophytes). Taking into account the environmental drivers of the pathogen/host variability that is the central theme for ED occurrence, there is an increasing need to better identify and understand the genetic and molecular mechanisms underlying pathogen virulence and plant resistance in the ecological conditions where both pathogens and hosts evolve. To our opinion, this ecological genomics approach can favor the identification of novel, durable, and sustainable means to prevent crop diseases.

To date, traditional linkage mapping based on genetic map, has been achieved in the identification of the genetic basis underlying phenotypic variation in both plants and pathogenic microbes. Traditional linkage mapping refers to a diversity of experimental populations ranging from F2 populations to the more recently developed multiparent advanced generation intercross (MAGIC) lines (Kover et al., 2009). Because Recombinant Inbred Lines (RILs) are almost completely homozygous, they can be replicated within an experiment and/or among several environmental conditions, thereby making RIL families the most popular experiment populations for traditional linkage mapping, at least in plants (Bergelson and Roux, 2010). While few genetic markers are required for a complete genome scan, traditional linkage mapping presents several drawbacks including (i) coarse mapping, (ii) genetic diversity that is limited to the parental lines of the segregating populations, and (iii) the impossibility to distinguish between pleiotropic and physically close genes (Bergelson and Roux, 2010). To address those problems, the method of genome-wide association (GWA) mapping emerged through the recent development of next-generation sequencing (NGS) technologies. Taking advantage of recombination events
that have accumulated over thousands of generations (Mitchell-
Olds and Schmitt, 2006; Nordborg and Weigel, 2010), GWA
mapping uses natural linkage disequilibrium (LD) to identify
polymorphisms that are associated with phenotypic variation.
Although its power is reduced to detect rare alleles or weak-
effect alleles in GWA mapping populations, this issue is
counterbalanced by the greater advantage of fine mapping (down
to the gene level) and common alleles associated with phenotypic
variation at the species level (Bergelson and Roux, 2010).

The aim of the present review is to summarize recent
progresses on the identification of QTLs underlying plant-
pathogen interactions, through the use of GWA mapping. We
discussed the main experimental and methodological difficulties
that need to be addressed in the future to obtain a thorough
overview of the genetic architecture underlying plant-pathogen
interactions. We also introduced the emerging joint GWA
mapping approach that allows the simultaneous identification of
intergenomic epistatic QTLs underlying the molecular landscape
of plant-pathogen interactions. In this context, we highlighted
the benefits of the joint GWA mapping approach, in particular
when performed in absence of phenotyping.

GWA STUDIES OF PATHOGEN
RESISTANCE IN PLANTS

In this review, we only considered GWAS based on a substantial
number of genetic markers covering the whole plant species
genome, thereby allowing to obtain an unbiased picture of
the genetic architecture driving disease resistance. Also, we
focused on studies where plants were directly challenged
with a pathogen species. We therefore not considered GWAS
of autoactive hypersensitive response (Olukolu et al., 2014).
Based on these criteria, we identified a total of 35 studies
reporting the identification of genomic regions associated with
natural variation of plant response to pathogen infection in 34
pathosystems (Table 1). Despite the limited number of GWAS
of disease resistance, important observations can be still drawn.
Firstly, although the first GWAS of disease resistance—on
*Arabidopsis thaliana* interacting with the bacterial pathogen *P.
syringae*—was published in 2005 (Aranzana et al., 2005), almost
half of the GWAS (~48.6%) have been published in the last
2 years. The recent burst of GWAS is directly linked to the
development of the NGS technologies that occurred in the
last decade. NGS technologies provide sufficient numbers of
genetic markers to fine-map genes underlying natural variation
of complex traits (Bergelson and Roux, 2010). The 35 GWAS
identified here used Single Nucleotide Polymorphisms (SNPs) as
genetic markers for mapping, because of their high frequency
across the genomes and the development of SNP-tilling arrays
containing probe sets for tens (even hundreds) of thousands of
SNPs. While SNP markers remain highly popular, they represent
only a fraction of the available genetic polymorphisms. The
access to structural variants such as copy number variation
(CNV) and insertions-deletions (indels) is already facilitated
by Single Molecule Real-Time (SMRT) sequencing technologies
such as the Pacific Biosciences (PacBio) and Oxford Nanopore

systems (Goodwin et al., 2016; Lee et al., 2016). In addition,
because epigenetic variation can account for a non-negligible
fraction (up to 30%) of the variation in complex traits (Roux
et al., 2011), epigenome characterization at a single-base-pair
resolution for hundreds of plant lines has already started for
a limited number of plant species. For example, a high-quality
single-base resolution genome-wide methylome was recently
reported for 1,107 natural *A. thaliana* accessions (Kawakatsu
et al., 2016). While combining different types of genomic markers
with epigenomic diversity should help the access to causal
variations and to tease apart the relative effect of genetic variants
from epigenetic variants, the inclusion of additional information
ever lead to an increase of false-positive associations between
phenotype and polymorphic markers due to the problem of
large dimensionality. The reduction of false-positive rate requests
the development of statistical methods taking into account the
diversity of polymorphic markers and the inherent kinships
among plant lines, potentially reflecting different aspects of the
demographic history of the plant species considered.

Secondly, the 35 plant GWAS are not evenly distributed
across the three classes of pathogenic organisms considered in
this review (Table 1). Two thirds of the GWAS reported the
mapping of resistance QTLs to fungal pathogens, whereas the
remaining GWAS were split between bacterial pathogens (n =
9) and oomycete pathogens (n = 6). The greater number of
GWAS designed to identify genes decreasing the detrimental
effect of fungal infection on plants is in line with fungal
pathogens being the most widespread and rapidly spreading
crop pathogens, despite their restricted host range in comparison
with bacterial and oomycete pathogens (Bebber et al., 2014).
Although plants are simultaneously and/or sequentially attacked
by a range of pathogens, whether in natural environments or
in crop fields (Kniskern et al., 2007; Davila Olivas et al., 2016;
Roux and Bergelson, 2016), GWAS reporting genomic regions
associated with plant responses to multiple pathogen attacks
remain however scarce. In a first attempt, by measuring resistance
to three fungal leaf diseases (i.e., southern leaf blight caused by
*Cochliobolus heterostrophus*, gray leaf spot caused by *Cercospora
zeae-maydis* (Czm) and northern leaf blight caused by *Cercospora
zeina*) on 253 maize inbred lines genotyped for only 858 SNPs
(i.e., 0.0015% SNP sites in maize), Wisser et al. (2011) performed
a multivariate analysis and identified a glutathione S-transferase
(GST) associated with modest levels of resistance to all three
diseases. The recent development of statistical tools to perform
multi-trait GWA mapping would undoubtedly facilitate the
identification of the pleiotropic genetic determinants underlying
multi-pathogen response (Korte et al., 2012; Thoen et al.,
2016). In the era of metagenomics, describing multiple pathogen
infections can be facilitated by the description of the pathobiota
(defined as the complex of microorganisms with the potential to
cause disease on the plant host; Kamada et al., 2013) based on
high throughput sequencing of amplicons of housekeeping genes
with deep taxonomic resolution (such as *gyrB* gene marker for
bacteria), allowing to distinguish pathogenic from commensal
Operational Taxonomic Units (OTUs) within a given microbe
genus (Barret et al., 2015). A metagenomic approach can help to
estimate the relative fraction of microbial pathogens and can also
<table><caption>TABLE 1 | Genome-wide association studies in plant pathosystems.</caption><thead><tr><th>Plant species name</th><th>Number of lines<sup>a</sup></th><th>Polymorphisms<sup>b</sup></th><th>Pathogen species name</th><th>Phenotypic trait<sup>c</sup></th><th>Growth conditions<sup>d</sup></th><th>Gen. Arch.<sup>e</sup></th><th>Functional validation<sup>f</sup></th><th>References</th></tr></thead><tbody><tr><td rowspan="10">Bacteria</td><td rowspan="10">Arabidopsis thaliana</td><td>84 NA</td><td>&gt;17 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrRpm1</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPM1</td><td>Aranzana et al., 2005</td></tr><tr><td>87 NA</td><td>&gt;17 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrB</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPM1</td><td>Aranzana et al., 2005</td></tr><tr><td>90 NA</td><td>&gt;17 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrPphB</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPS5</td><td>Aranzana et al., 2005</td></tr><tr><td>89 NA</td><td>&gt;17 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrRpt2</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPS2</td><td>Aranzana et al., 2005</td></tr><tr><td>84 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrRpm1</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPM1</td><td>Atwell et al., 2010</td></tr><tr><td>87 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrB</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPM1</td><td>Atwell et al., 2010</td></tr><tr><td>90 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrPphB</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPS5</td><td>Atwell et al., 2010</td></tr><tr><td>89 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas syringae-Pst DC3000::avrRpt2</td><td>B Hypersensitive response</td><td>G</td><td>M</td><td>RPS2</td><td>Atwell et al., 2010</td></tr><tr><td>95 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas syringae-Pst DC3000</td><td>Q Bacterial growth</td><td>G</td><td>P</td><td>No</td><td>Atwell et al., 2010</td></tr><tr><td>96 NA</td><td>&gt;205 k SNPs</td><td>Pseudomonas syringae-Pst DC3000</td><td>Q Bacterial growth</td><td>G</td><td>P</td><td>ALABCG36</td><td>Ji et al., 2014</td></tr><tr><td rowspan="10">Bacteria</td><td rowspan="10">Arabidopsis thaliana</td><td>64 NA</td><td>4,004,754 SNPs</td><td>Pseudomonas syringae-Pst DC3000D28E (virulence factor HopAM1)</td><td>Q Di (time point that cell death was first observed)</td><td>G</td><td>P</td><td>No</td><td>Iakovidis et al., 2016</td></tr><tr><td>75 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas syringae-natural strain PNA29.1a</td><td>B Chlorotic rosette phenotype</td><td>G</td><td>P</td><td>No</td><td>Iakovidis et al., 2016</td></tr><tr><td>175 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas viridiflava-natural strain LP23.1a</td><td>Q Hypersensitive response</td><td>G</td><td>M</td><td>RPS5</td><td>Karasov et al., 2014</td></tr><tr><td>175 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas viridiflava-natural strain RMX23.1a</td><td>Q Di (chlorosis) and bacterial growth</td><td>G</td><td>P</td><td>No</td><td>Atwell et al., 2010</td></tr><tr><td>175 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas viridiflava-natural strain RMX3.1b</td><td>Q Di (chlorosis) and bacterial growth</td><td>G</td><td>P</td><td>No</td><td>Atwell et al., 2010</td></tr><tr><td>175 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas viridiflava-natural strain PNA3.3a</td><td>Q Di (chlorosis) and bacterial growth</td><td>G</td><td>P</td><td>No</td><td>Atwell et al., 2010</td></tr><tr><td>175 NA</td><td>&gt;216 k SNPs</td><td>Pseudomonas viridiflava-natural strain ME3.1b</td><td>Q Di (chlorosis) and bacterial growth</td><td>G</td><td>P</td><td>No</td><td>Atwell et al., 2010</td></tr><tr><td>163 NA</td><td>&gt;214 k SNPs</td><td>Ralstonia solanacearum-crop strain GMI1000</td><td>Q Di (wilting)</td><td>G</td><td>P</td><td>RRS1/RPS4 &amp; SSL4</td><td>Aoun et al., in review</td></tr><tr><td>384 NA</td><td>&gt;214 k SNPs</td><td>Xanthomonas campestris pv. campestris-crop strain Xcc568</td><td>Q Di (chlorosis)and bacterial growth</td><td>G</td><td>P</td><td>RKS1</td><td>Huard-chaureau et al., 2013</td></tr></tbody></table>

(Continued)
<table><caption>TABLE 1 | Continued</caption><thead><tr><th></th><th>Plant species name</th><th>Number of lines<sup>a</sup></th><th>Polymorphisms<sup>b</sup></th><th>Pathogen species name</th><th>Phenotypic trait<sup>c</sup></th><th>Growth conditions<sup>d</sup></th><th>Gen. Arch.<sup>e</sup></th><th>Functional validation<sup>f</sup></th><th>References</th></tr></thead><tbody><tr><td rowspan="3">Arabidopsis thaliana</td><td>384 NA</td><td></td><td>&gt;214 k SNPs</td><td>Xanthomonas campestris pv. campestris - crop strain Xcc12824</td><td>Q</td><td>DI (chlorosis)</td><td>G</td><td>P</td><td>AT5G22540</td><td>Debieu et al., 2016</td></tr><tr><td>176 NA</td><td></td><td>&gt;214 k SNPs</td><td>Xanthomonas campestris pv. campestris - crop strain XccCFBP6943</td><td>Q</td><td>DI (chlorosis)</td><td>G</td><td>P</td><td>RPS1/RPS4</td><td>Debieu et al., 2016</td></tr><tr><td>3,173 Accessions</td><td></td><td>37,659 SNPs</td><td>Xanthomonas axonopodis pv. glycines</td><td>Q</td><td>DI (percentage of lesions)</td><td>F (NE)</td><td>P</td><td>No</td><td>Chang et al., 2016</td></tr><tr><td rowspan="10">Fungi</td><td>96 NA</td><td>115,301 SNPs</td><td>Botrytis cinerea - strain Apple 517</td><td>Q</td><td>Lesion area + camalexin production</td><td>G</td><td>P</td><td>Yes</td><td>Corwin et al., 2016</td></tr><tr><td>96 NA</td><td>115,301 SNPs</td><td>Botrytis cinerea - strain B05.10</td><td>Q</td><td>Lesion area + camalexin production</td><td>G</td><td>P</td><td>Yes</td><td>Corwin et al., 2016</td></tr><tr><td>96 NA</td><td>115,301 SNPs</td><td>Botrytis cinerea - strain Supersteak</td><td>Q</td><td>Lesion area + camalexin production</td><td>G</td><td>P</td><td>Yes</td><td>Corwin et al., 2016</td></tr><tr><td>96 NA</td><td>115,301 SNPs</td><td>Botrytis cinerea - strain UKRazz</td><td>Q</td><td>Lesion area + camalexin production</td><td>G</td><td>P</td><td>Yes</td><td>Corwin et al., 2016</td></tr><tr><td>350 NA</td><td>&gt;214 k SNPs</td><td>Botrytis cinerea</td><td>Q</td><td>Percentage of leaves with spreading lesions</td><td>G</td><td>P</td><td>No</td><td>Thoen et al., 2016</td></tr><tr><td>350 NA</td><td>&gt;214 k SNPs</td><td>Botrytis cinerea - strain B05.10</td><td>Q</td><td>rosette fresh weight</td><td>G</td><td>P</td><td>No</td><td>Davila Olivas et al., 2016</td></tr><tr><td>179 Diverse accessions</td><td>18,804 SNPs</td><td>Leptosphaeria maculans - 12 single spore isolates (04MGPS021, 06MGPP041, D8, D9, IBCN13, IBCN15, IBCN16, IBCN17, IBCN18, IBCN75, IBCN76 and PHW1223)</td><td>Q</td><td>DI (lesion size)</td><td>G</td><td>P</td><td>No</td><td>Raman et al., 2016</td></tr><tr><td>179 Diverse accessions</td><td>18,804 SNPs</td><td>Leptosphaeria maculans - mix of pathotypes from four canola stubble sources</td><td>Q</td><td>Percentage of internal canker infection</td><td>G</td><td>P</td><td>No</td><td>Raman et al., 2016</td></tr><tr><td>116 Varieties</td><td>4,239 SNS</td><td>Leptosphaeria maculans</td><td>Q</td><td>DI (area of necrosis)</td><td>F (NE)</td><td>P</td><td>No</td><td>Fopa Fomeju et al., 2014</td></tr><tr><td rowspan="5">Glycine max</td><td>392 Diverse cultivars + 300 advanced breeding lines</td><td>52,041 SNPs + 5,361 SNPs</td><td>Fusarium virguliforme</td><td>Q</td><td>DI (disease incidence * disease severity)</td><td>F (NE)</td><td>P</td><td>No</td><td>Wen et al., 2014</td></tr><tr><td>214 Germplasm accessions</td><td>31,914 SNPs</td><td>Fusarium virguliforme - isolate Mont-1</td><td>Q</td><td>DI (percentage of foliage affected) + disease progressive curve</td><td>G</td><td>P</td><td>No</td><td>Zhang et al., 2015</td></tr><tr><td>4,771 Accessions</td><td>37,991 SNPs</td><td>Fusarium virguliforme - isolate Mont-1</td><td>Q</td><td>DI (percentage of foliage affected)</td><td>G</td><td>P</td><td>No</td><td>Chang et al., 2016</td></tr><tr><td>130 Lines</td><td>7,864 SNPs</td><td>Sclerotinia sclerotiorum - strain NB5</td><td>Q</td><td>Lesion length</td><td>G</td><td>P</td><td>No</td><td>Bastien et al., 2014</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(Continued)
<table><caption>TABLE 1 | Continued</caption><thead><tr><th>Plant species name</th><th>Number of lines<sup>a</sup></th><th>Polymorphisms<sup>b</sup></th><th>Pathogen species name</th><th>Phenotypic trait<sup>c</sup></th><th>Growth conditions<sup>d</sup></th><th>Gen. Arch.<sup>e</sup></th><th>Functional validation<sup>f</sup></th><th>References</th></tr></thead><tbody><tr><td rowspan="12">Fungi</td><td>Glycine max</td><td>2773<br>Accessions<br>540<br>Accessions</td><td>33,240 SNPs<br>33,486 SNPs</td><td>Cadophora gregata</td><td>B<br>Combination of foliar and stem observations</td><td>F (NE)</td><td>P</td><td>No</td><td>Rincker et al., 2016</td></tr><tr><td rowspan="11">Medicago sativa</td><td>825<br>Accessions<br>606<br>Accessions<br>608<br>Accessions<br>1,426<br>Accessions<br>112<br>Accessions<br>2,385<br>Accessions<br>92<br>Commercial cultivars</td><td>32,150 SNPs<br>29,815 SNPs<br>34,424 SNPs<br>33,549 SNPs<br>34,210 SNPs<br>38,608 SNPs</td><td>Cadophora gregata—strain Oh2<br>Cadophora gregata—strain Oh2<br>Cadophora gregata</td><td>Q<br>Percentage of plants exhibiting symptoms + proportion of nodes exhibiting brown pith<br>Q<br>Proportion of plants exhibiting foliar symptoms<br>Q<br>Proportion of nodes exhibiting brown pith<br>Q<br>Four classes of phenotypes based on foliar and stem symptoms<br>Q<br>DI (length of the visible lesions + proportion of dead plants)<br>Q<br>DI (percentage of infected/dead plants)</td><td>G<br>G + F (NE)</td><td>P</td><td>No<br>No<br>P<br>No</td><td>Rincker et al., 2016<br>Rincker et al., 2016<br>Chang et al., 2016<br>Chang et al., 2016</td></tr><tr><td>Hordeum vulgare</td><td>6,970 SNPs</td><td>Diaporthe phaseolorum var. caulivora</td><td>Q<br>DI (symptom and lesion development) + type of lesion</td><td>G<br>G</td><td>P<br>P</td><td>No<br>No</td><td>Chang et al., 2016<br>Chang et al., 2016</td></tr><tr><td>Medicago sativa</td><td>179 Elite breeding lines</td><td>Diaporthe phaseolorum var. meridionalis<br>Phakopsora pachyrhizi—mixture of four isolates</td><td>Q<br>DI (uredinia size, chlorosis and necrosis)</td><td>G<br>G</td><td>P<br>P</td><td>No<br>No</td><td>Turuspekov et al., 2016</td></tr><tr><td>Oryza sativa</td><td>413 Cultivars</td><td>Puccinia graminis—mixture of isolates representative of the most prevailing races of the pathogen in Central Asia</td><td>Q<br>DI (severity of disease symptoms)</td><td>G<br>G</td><td>P<br>P</td><td>No<br>No</td><td>Yu et al., 2016</td></tr><tr><td>362 cultivars</td><td>700 k SNPs</td><td>Verticillium alfaiae—mix of six strains (Berg, Sevick, Freitag, Kunderts, VP6 and VP11)</td><td>Q<br>DI (lesions)</td><td>F (Al)</td><td>P</td><td>No</td><td>Zhao et al., 2011</td></tr><tr><td>331 Cultivars</td><td>700 k SNPs</td><td>Pyricularia oryzae—mix of US races IB-49, IC-17 and IE-1K</td><td>Q<br>DI (lesion area)</td><td>G<br>G</td><td>P<br>P5 locus</td><td>Kang et al., 2016<br>Kang et al., 2016</td><td></td></tr><tr><td>312 Cultivars</td><td>700 k SNPs</td><td>Magnaporthe oryzae—isolate RB22<br>Magnaporthe oryzae—isolate 75-1-127</td><td>Q<br>DI (lesion area)</td><td>G<br>G</td><td>P<br>P5 locus</td><td>Kang et al., 2016<br>Kang et al., 2016</td><td></td></tr><tr><td>335 Cultivars</td><td>700 k SNPs</td><td>Magnaporthe oryzae—isolate 0-249</td><td>Q<br>DI (lesion area)</td><td>G<br>G</td><td>P<br>P5 locus</td><td>Kang et al., 2016<br>Kang et al., 2016</td><td></td></tr><tr><td>333 Cultivars</td><td>700 k SNPs</td><td>Magnaporthe oryzae—isolate P06-6</td><td>Q<br>DI (lesion area)</td><td>G<br>G</td><td>P<br>P5 locus</td><td>Kang et al., 2016<br>Jia et al., 2013</td><td></td></tr><tr><td>Setaria italica</td><td>916 Varieties (traditional landraces + modern cultivars)</td><td>Magnaporthe grisea</td><td>Q<br>not available</td><td>F (NE)</td><td>P<br>P</td><td>No<br>No</td><td>Jia et al., 2013<br>Jia et al., 2013</td><td></td></tr><tr><td></td><td>Rhizoctonia solani</td><td></td><td>Q<br>not available</td><td>F (NE)</td><td>P<br>P</td><td>No<br>No</td><td></td><td></td></tr><tr><td></td><td>Uromyces setariae-italicae</td><td></td><td>Q<br>not available</td><td>F (NE)</td><td>P<br>P</td><td>No<br>No</td><td></td><td></td></tr></tbody></table>

(Continued)
<table><caption>TABLE 1 | Continued</caption><thead><tr><th>Plant species name</th><th>Number of lines<sup>a</sup></th><th>Polymorphisms<sup>b</sup></th><th>Pathogen species name</th><th>Phenotypic trait<sup>c</sup></th><th>Growth conditions<sup>d</sup></th><th>Gen. Arch.<sup>e</sup></th><th>Functional validation<sup>f</sup></th><th>References</th></tr></thead><tbody><tr><td rowspan="2">Sorghum bicolor</td><td rowspan="2">300 Genotypes (251 tropical sorghums + 49 breeding lines)</td><td rowspan="2">79,132 SNPs</td><td rowspan="2">Macrophomina phaesolina</td><td rowspan="2">Q</td><td rowspan="2">Length of the visible lesions</td><td>F (AI)</td><td>P</td><td>No</td><td>Adeyanju et al., 2015</td></tr><tr><td>Fusarium thapsinum</td><td>F (AI)</td><td>P</td><td>No</td><td>Adeyanju et al., 2015</td></tr><tr><td rowspan="7">Zea mays</td><td>4,699 RILs (NAM population)</td><td>1.6 M SNPs</td><td>Cochilobus heterostrophus</td><td>Q</td><td>Length of the visible lesions</td><td>F (NE + AI)</td><td>P</td><td>No</td><td>Kump et al., 2011</td></tr><tr><td>4,413 RILs (NAM population)</td><td>28.5 M genomic variants (SNPs + read-depth variants)</td><td>Cochilobus heterostrophus</td><td>Q</td><td>DI (number and area of lesions)</td><td>F (NE + AI)</td><td>P</td><td>No</td><td>Bian et al., 2014</td></tr><tr><td>4,630 RILs (NAM population)</td><td>1.6 M SNPs</td><td>Setosphaeria turcica—race 1</td><td>Q</td><td>Percentage of diseased leaf area</td><td>F (AI)</td><td>P</td><td>No</td><td>Poland et al., 2011</td></tr><tr><td>3,678 RILs (NAM population)</td><td>1.6 M SNPs</td><td>Cercospora zeae-maydis + Cercospora zeina</td><td>Q</td><td>DI (dimension of each lesion + number of lesions)</td><td>F (NE)</td><td>P</td><td>No</td><td>Benson et al., 2015</td></tr><tr><td>1487 European inbred lines</td><td>8,244 SNPs</td><td>Setosphaeria turcica</td><td>Q</td><td>DI (Percentage of diseased leaf area)</td><td>F (NE + AI)</td><td>P</td><td>No</td><td>Van Inghelandt et al., 2012</td></tr><tr><td>144 Inbred lines</td><td>45,868 SNPs</td><td>Sphacelotheca reiliana—spores collected from the field in the previous growing season</td><td>Q</td><td>Percentage of infected plants</td><td>F (AI)</td><td>P</td><td>No</td><td>Wang et al., 2012</td></tr><tr><td>267 Inbred lines</td><td>47,445 SNPs</td><td>Fusarium verticilliodes—local isolates</td><td>Q</td><td>Percentage of the ear presenting disease symptoms + DI (percentage of kernels exhibiting visual symptoms of infection)</td><td>F (AI)</td><td>P</td><td>No</td><td>Zila et al., 2013</td></tr><tr><td rowspan="3">Oomycete</td><td rowspan="3">Arabidopsis thaliana</td><td>274 Inbred lines</td><td>246,497 SNPs</td><td>Puccina sorghi—mix of races of spores collected during the previous season</td><td>Q</td><td>DI (scale of rust infection on the plant)</td><td>F (AI)</td><td>P</td><td>No</td><td>Olikolu et al., 2016</td></tr><tr><td>86 NA</td><td>>216 k SNPs</td><td>Hyaloperonospora arabidopsidis—natural strain Emco5</td><td>B</td><td>Presence/absence of sporangiophores</td><td>G</td><td>P</td><td>RPP13, RPP5</td><td>Atwell et al., 2010; Nemri et al., 2010</td></tr><tr><td>85 NA</td><td>>216 k SNPs</td><td>Hyaloperonospora arabidopsidis—natural strain Emwa1</td><td>B</td><td>Presence/absence of sporangiophores</td><td>G</td><td>P</td><td>No</td><td>Atwell et al., 2010; Nemri et al., 2010</td></tr><tr><td></td><td>76 NA</td><td>> 216k SNPs</td><td>Hyaloperonospora arabidopsidis—natural strain Emoy2</td><td>B</td><td>Presence/absence of sporangiophores</td><td>G</td><td>P</td><td>RPP13</td><td>Atwell et al., 2010; Nemri et al., 2010</td></tr></tbody></table>

(Continued)
<table><caption>TABLE 1 | Continued</caption><thead><tr><th>Plant species name</th><th>Number of lines<sup>a</sup></th><th>Polymorphisms<sup>b</sup></th><th>Pathogen species name</th><th>Phenotypic trait<sup>c</sup></th><th>Growth conditions<sup>d</sup></th><th>Gen. Arch.<sup>e</sup></th><th>Functional validation<sup>f</sup></th><th>References</th></tr></thead><tbody><tr><td>Arabidopsis thaliana</td><td>84 NA</td><td>&gt;216 k SNPs</td><td>Hyaloferonospora arabidopsidis—natural strain Hiks1</td><td>B</td><td>G</td><td>P</td><td>RPP7</td><td>Atwell et al., 2010; Nemri et al., 2010</td></tr><tr><td></td><td>87 NA</td><td>&gt;216 k SNPs</td><td>Hyaloferonospora arabidopsidis—natural strain Noco2</td><td>B</td><td>G</td><td>P</td><td>RPP5, RPP7</td><td>Atwell et al., 2010; Nemri et al., 2010</td></tr><tr><td>Glycine max</td><td>~879 Accessions (range from 44 to 7,431)</td><td>~33,709 SNPs (range from 24,490 to 41,911)</td><td>Phytophthora sojae—19 individual races</td><td>Q</td><td>G</td><td>P</td><td>No</td><td>Chang et al., 2016</td></tr><tr><td>Medicago truncatula</td><td>179 NA</td><td>~5.1 M SNPs</td><td>Aphanomyces euteiches—strain ATCC 201684</td><td>Q</td><td>G</td><td>P</td><td>No</td><td>Bonhomme et al., 2014</td></tr><tr><td>Pisum sativum</td><td>175 Lines (cultivars, breeding lines + germplasm lines)</td><td>13,204 SNPs</td><td>Aphanomyces euteiches—French strain RB84</td><td>Q</td><td>G</td><td>P</td><td>No</td><td>Desgroux et al., 2016</td></tr><tr><td></td><td></td><td>13,204 SNPs</td><td>Aphanomyces euteiches—American strain Ae109</td><td>Q</td><td>G</td><td>P</td><td>No</td><td>Desgroux et al., 2016</td></tr><tr><td></td><td></td><td>13,204 SNPs</td><td>Aphanomyces euteiches</td><td>Q</td><td>F (NE)</td><td>P</td><td>No</td><td>Desgroux et al., 2016</td></tr><tr><td>Setaria italica</td><td>916 Varieties (traditional landraces + modern cultivars)</td><td>845,787 SNPs</td><td>Sclerospora graminicola</td><td>Q</td><td>F (NE)</td><td>P</td><td>No</td><td>Jia et al., 2013</td></tr></tbody></table>

<footnote><sup>a</sup>Number of genetic lines that have been phenotyped for pathogen response, NA: natural accessions. <sup>b</sup>SNPs: Single Nucleotide Polymorphisms. <sup>c</sup> “B” and “Q” stand for binary and quantitative traits, respectively. <sup>d</sup>DI: disease index (generally based on a scale from 0 to 9). <sup>e</sup>A:G, greenhouse conditions; F: field conditions; NE: natural exposition to the pathogen; Al: artificial inoculation. <sup>f</sup>Genetic architecture: M, monogenic; P, polygenic; NA, not available. <sup>g</sup>Name of the genes underlying QTLs that have been functionally validated. Genes in red indicate genes that have been functionally validated after QTL detection by GWA mapping.</footnote>
have the advantage of describing multiple pathogen infections at very early disease stages. Under these considerations, the next challenge in tracing the plant genomic regions associated with disease occurrence concerns the developed of GWAS on the whole pathobiota. While a recent GWA study reported the identification of genomic regions associated with descriptors (i.e., species richness, α-diversity, β-diversity, presence/absence of OTUs) of leaf microbial community in *A. thaliana* (Horton et al., 2014), to our knowledge, no GWAS on descriptors of pathobiota communities have been published so far. Altogether, mapping genomic regions associated with plant response to multiple pathogen infections can help to better elucidate the pathways that ultimately enable a plant to fine-tune its defense against different aggressors, thereby shedding some light on downstream components of the complex signaling network leading to resistance that cannot be revealed by classical monopathogen infection approaches.

Thirdly, the 35 GWAS involved only 11 plant species distributed across three botanical families, i.e., Brassicaceae (*A. thaliana* and *Brassica napus*), Fabaceae (*Glycine max*, *Medicago sativa*, *Medicago truncatula*, *Pisum sativum*) and Poaceae (*Hordeum vulgare*, *Oryza sativa*, *Setaria italica*, *Sorghum bicolor*, and *Zea mays*; **Table 1**). Because of the economic and environmental cost of crop pathogens, the majority of GWAS (~62.8%) was conducted on crop species to speed up the identification of new sources of disease resistance. The remaining GWAS were conducted on the two model plant species *A. thaliana* (~33%) and *M. truncatula* (~3%). Conducting GWAS on wild plant species can be a successful starting point to identify homologous genes in other species belonging or not to the same botanical family (Huard-chauveau et al., 2013). In addition, studying the spatial-temporal evolutionary dynamics of an adaptive resistance gene in an ecologically realistic context can help drawing new strategies for disease management in crops in an agro-ecological context (Bergelson et al., 2001; Barrett et al., 2009; Burdon et al., 2014; Karasov et al., 2014; Roux et al., 2014a).

Fourthly, a large variety of genetic lines, from natural accessions to elite breeding lines, were used for scoring disease resistance (Table 1). Noteworthy is the use of the combined advantages of both traditional QTL mapping and GWA mapping in a Nested Association Mapping (NAM) population in maize (for a total of ~4,000 RILs resulting from 25 crosses between diverse inbred lines and the reference line B73; Buckler et al., 2009) in four GWAS (Kump et al., 2011; Poland et al., 2011; Bian et al., 2014; Benson et al., 2015). Across the 35 GWAS, the number of genetic lines scored for disease resistance follows an L-shaped distribution with a median of ~196 lines (mean number ~817 lines, minimum number ~44 lines, maximum number ~7,431 lines; Table 1). Increasing genetic diversity remains the major goal in the design of a GWA mapping population and is mainly achieved by assembling lines collected over the entire geographic range of a plant species. Such panels may however increase the effect of population demographic history on the rate of false-positive phenotype-genotype associations (Zhao et al., 2007). Statistical methods controlling for population structure can drastically reduce the inflation of false-positive associations (Price et al., 2006, 2010; Kang et al., 2010; Zhang et al.,

2010), but with the cost of increasing the rate of false-negative associations (i.e., when causative variants are lost after applying a correction for the effects of population structure; Brachi et al., 2010). In addition, because the same phenotype can be achieved by different combinations of genes, a higher genetic diversity increases the probability of genetic and/or allelic heterogeneity which may in turn limit the detection of polymorphic markers linked to phenotypic variation (Bergelson and Roux, 2010). Controlling the effects of genetic and allelic heterogeneity may be achieved by building panels of lines belonging to the same genetic cluster that is often geographically restricted. While of smaller size, those panels can lead to a higher power and resolution to fine-map loci associated with phenotypic variation. For example, more significant and neat association peaks for phenological variation in *A. thaliana* were found in set of 121 natural accessions collected in the region of Burgundy (France) than in a set a 167 worldwide natural accessions (Brachi et al., 2013). It remains to be tested whether a similar pattern is also observed for disease resistance.

Fifthly, the environmental conditions in which phenotyping of disease resistance was performed are well balanced between laboratory (greenhouse/growth chamber) controlled conditions (60%) and field conditions (40%) (Table 1), with only three GWAS performed both in controlled and/or field conditions (Chang et al., 2016; Desgroux et al., 2016; Rincker et al., 2016). Controlled and field conditions are complementary. In the field, plants are exposed to greater temporal abiotic fluctuations that may affect plant responses to pathogen invasions than are typically encountered in laboratory conditions. To limit the effects of those fluctuations, field experiments are repeated over several years. Despite recurrent observations of pathogen infections in natural populations, it is interesting to note that no GWAS was performed in the model plant species *A. thaliana* and *M. truncatula* in their local habitats. Adding ecology to the studies of “disease resistance—genotype” association can however help to better understand the evolutionary trajectories of a given adaptive resistance gene (Karasov et al., 2014; Roux and Bergelson, 2016). On the other hand, performing experiments in controlled conditions can help to test the effect of a specific abiotic stress on plant resistance to pathogen infection. This is especially relevant in the context of climate change where (i) the severity of epidemics is predicted to increase due to the shift and broadening of geographic distributions of pathogen species (Evans et al., 2008; Bebber et al., 2014), and (ii) a permanent increase in temperature was demonstrated to alleviate major known defense mechanisms in plants (Suzuki et al., 2014). In addressing this issue, Aoun and colleagues reported a GWAS performed on *A. thaliana* challenged with the bacterial pathogen *Ralstonia solanacearum* at 27 and 30°C (Aoun et al., in review). Based on traditional QTL mapping performed on one RIL family, the immune receptor pair of TIR-NBS-LRR proteins RESISTANT TO P. SYRINGAE 4 (RPS4)/RESISTANT TO R. SOLANACEARUM 1 (RRS1) has been map-based cloned and identified as the major genetic determinant conferring resistance to the *R. solanacearum* GMI1000 strain at 27°C (Deslandes et al., 2002). GWA mapping performed on 176 worldwide accessions of *A. thaliana* phenotyped at 27°C revealed a strong and unique
association peak with the most associated SNP located within *RPS4*, suggesting that natural variation for resistance to the strain GMI1000 is also caused by *RPS4/RRS1* at the species level (Aoun et al., in review). At 30°C, GWA mapping performed on the same set of accessions revealed multiple and smaller association peaks not located in the vicinity of *RPS4/RRS1*. Based on the phenotyping of T-DNA knockout mutants, the authors identified an enzyme encoding a strictosidine synthase (*STRICTOSIDINE SYNTHASE-LIKE 4*, *SLL4*) as underlying one of these small QTLs (Aoun et al., in review).

Sixthly, the first GWAS of disease resistance were performed in *A. thaliana* on natural variation of the hypersensitive response (HR), i.e., a highly effective local resistance response that is often associated with hypersensitive cell death (Table 1). This binary trait was useful to demonstrate the power of GWA mapping in plants because of previously known resistance genes underlying HR (Aranzana et al., 2005; Atwell et al., 2010). However, quantitative resistance (continuum of symptoms) is much more prevalent than qualitative resistance (presence/absence of symptoms) in crops and natural plant populations (Young, 1996; Poland et al., 2008; Roux et al., 2014a). Accordingly, recent GWAS performed in *A. thaliana* and *M. truncatula* and all GWAS performed in crops were based on a quantitative scoring of plant response to pathogen infections (but see Rincker et al., 2016; Table 1). The quantitative genetic architecture was highly diverse among pathosystems, ranging from the identification of few medium-effect QTLs to the identification of up to hundreds (and even thousands) of small-effect QTLs (Corwin et al., 2016). We should however be cautious on the complexity of the quantitative genetic architecture described in most GWAS because the number of QTLs identified might be highly dependent on the number of genetic lines used, the number of polymorphic markers genotyped and the accuracy in scoring the disease symptoms (Table 1). Because the development of NGS technologies will speed up the accumulation of genomic resources, the next frontier is high-throughput phenotyping of precise quantitative disease symptoms. This challenge can be achieved by the combination of the development of automated platforms (such as the Toulouse Plant Microbe Phenotyping Platform) updated by the International Plant Phenomics Network (IPPN) with the development of image-based quantification of disease symptoms (Laflamme et al., 2016).

Finally, the major goal of performing GWA mapping in crops is to accelerate the identification of genetic markers that can be subsequently used for Marker-Assisted Selection (MAS) in breeding programs. In this context, GWAS are rarely followed up by studies aiming at functionally validating the causative genes. To date, after identification of genomic regions linked to disease resistance by GWA mapping, genes responsible for the QTL of interest were functionally validated in only six GWAS (Table 1). Unsurprisingly, most functional validations were performed in *A. thaliana* due to the impressive genetic tools available in this species, such as the availability of several public collections of T-DNA mutants, quantitative complementation (i.e., introducing alternative alleles in genetic lines lacking the candidate gene) or quantitative knockdown (i.e., gene silencing by amiRNA; Bergelson and Roux, 2010). Functional validation helped (i) to

identify gene functions that have never been related to defense against aggressors, as illustrated by the atypical kinase *RKS1* gene and a gene of unknown function, both conferring quantitative resistance to the bacterial pathogen *Xanthomonas campestris* (Huard-chauveau et al., 2013; Roux et al., 2014b; Debieu et al., 2016), and (ii) to establish the selective forces acting on the causative gene (Huard-chauveau et al., 2013; Karasov et al., 2014). In a further step, both analyzing the transcriptional and/or post-transcriptional regulation of the causative gene and searching for proteins directly interacting with the causative gene can lead to the identification of the downstream signaling pathways, thereby providing an additional list of candidate genes for breeding programs.

## WHY SO FEW GWAS OF PATHOGENICITY IN BACTERIA, FUNGI AND OOMYCETES?

Similarly to plants, we only considered in this study GWAS based on a substantial number of genetic markers covering the whole genome of the pathogen species. As described above, GWA mapping started to be extensively used to fine map resistance genes in plants. By contrast, since the publication of the first plant pathogenic bacterium genome (*Xylella fastidiosa*) in 2000 (Simpson et al., 2000), the first plant pathogenic fungal species *Magnaporthe grisea* in 2005 (Dean et al., 2005) and the first oomycete species *Phytophthora ramorum* in 2006 (Tyler et al., 2006), as well as the establishment of the Fungal Genome Initiative (FGI) (Galagan et al., 2005), only five studies employed GWA mapping to identify candidate pathogenic genetic determinants in bacterial (Monteil et al., 2017) and fungal pathogens (Dalman et al., 2013; Gao et al., 2016; Talas et al., 2016, Wu et al., 2017) and to our knowledge, no GWAS was reported yet on a phytopathogenic oomycete. Several explanations can be advanced for this paucity of studies. Firstly, comparative genomics has been proved as a very efficient tool to identify important pathogenic genetic determinants and to elucidate the mechanisms that microbial phytopathogens employ during pathogenesis (Klosterman et al., 2016). As recently summarized by Sundin et al. (2016), comparative genomics performed on an ever-growing number of phytopathogenic sequenced genomes was extremely useful for the discovery of type III secretion system (T3SS) effectors and transcription activator-like effector nuclease (TAL-effector) of the three most important studied plant pathogenic bacteria *P. syringae*, *X. campestris* and *R. solanacearum* (Mansfield et al., 2012). Though powerful, comparative genomics has also several limitations. Comparative genomics is a powerful tool to compare closely related epidemic strains showing few genomic differences but it can be hardly applied to "reservoir populations" carrying a high degree of diversity among them. In addition, comparative genomics is mainly employed to detect phenotypic differences that are based on the presence/absence of genes, thereby strongly limiting the potential for identifying SNPs associated with natural variation of virulence/aggressiveness among microbial pathogenic populations.
Secondly, despite the presence of several molecular mechanisms that can generate genetic variation in microbes (i.e., frequent mutation events, homologous recombination, and horizontal gene transfer; Bartoli et al., 2016), fine mapping in GWAS can be limited by the long LD observed in many pathogen species (Read and Massey, 2014). In human pathogenic microbes, long LD is mainly observed in pathogen species with a clonal reproduction system and/or in sets of epidemics strains with limited genetic diversity (Chen and Shapiro, 2015; Read and Massey, 2014). While to our knowledge no LD extent has been estimated in phytopathogenic bacteria and oomycetes, a rapid LD decay was observed in two phytopathogenic fungi, i.e., *Fusarium graminearum* (mean LD ~1 kb) and *Parastagonospora nodorum* (mean LD ~5–10 kb; Gao et al., 2016; Talas et al., 2016), thereby allowing fine mapping of pathogenic determinants (see below).

Thirdly, as previously observed in plants, genomic diversity of pathogenic microbes can be strongly shaped by population stratification (Power et al., 2017). Such population stratification is particularly encountered in haploid and asexual bacteria and/or pathogenic microbes with limited dispersal (Chen and Shapiro, 2015). Because population structure can impede the identification of genomic regions associated with virulence/aggressiveness in pathogenic microbes, various methods have been recently developed to limit the rate of false positives (Sheppard et al., 2013; Earle et al., 2016). When applied in the field of human pathology, these methods allowed the identification of genes related to host specificity in the *Campylobacter* human pathogen (Sheppard et al., 2013) and genes associated with antibiotic resistance in both *Mycobacterium tuberculosis* and *Streptococcus* bacterial species (Read and Massey, 2014).

To limit the negative effects of long LD and population structure on the identification of pathogenic genetic determinants, we advise the use of a local/ regional set of microbial strains collected on wild plant species. Such a strategy should increase the level of genetic diversity that is available in the natural plant reservoirs, while limiting the problem of population stratification.

If the last 4 years are documented by a speed in the increasing of studies employing GWAS to detect genes important for pathogenicity in human pathogens (Power et al., 2017), GWA mapping is still poorly used in plant pathology to identify genes related to microbial pathogenicity phenotypes. To our knowledge, the study from Monteil et al. (2017) is the only one that attempted to apply GWA mapping to a phytopathogenic bacterium. By using a GWA mapping method previously developed for human bacterial pathogens and that takes into account both core and pan-genome while controlling for population structure (Sheppard et al., 2013; Pascoe et al., 2015; Yahara et al., 2017), the authors found that the T3SS effectors *hopQ1* and *hopD1* have probably shaped the adaptation of the ubiquitous plant pathogenic bacterium *P. syringae* to crops. In plant-fungus pathosystems, we identified four studies reporting the identification of genetic determinants associated with virulence/aggressiveness. Dalman et al. (2013) adopted a GWA mapping approach to identify the genetic components

underlying virulence in the fungal necrotrophic pathogen *Heterobasidion annosum sensu stricto* that is responsible of severe damages in forest conifers. Based on 23 haploid whole-genome sequenced *H. annosum* isolates collected in different geographic European countries, the authors used 33,018 non-singleton SNPs to run GWA mapping on virulence scored on both Scots pine and Norway spruce in controlled conditions. Although the size of the mapping population was limited, 12 SNPs were found to be significantly associated with virulence on both host plants. In the study of Talas et al. (2016), 119 isolates of the fungal pathogen *F. graminearum* collected in Germany were phenotyped for aggressiveness on wheat under field conditions in two locations over 2 years (Talas et al., 2016). Based on ~29,000 SNPs and a short LD of <1 kb, the authors finely mapped 50 SNPs significantly associated with aggressiveness. Interestingly, highly significant interactions between the isolates and the field phenotyping conditions suggested an environment-dependent genetic architecture of *F. graminearum*. In the study of Gao et al. (2016), 191 isolates of the fungal necrotrophic wheat pathogen *P. nodorum* were phenotyped for virulence on two wheat lines and genotyped for ~3,000 SNPs distributed across the genome as well as genetic markers in candidate genes. The identification of the two previous cloned effector genes *SnToxA* and *SnTox3* confirmed the power of GWA mapping to fine map virulence factors in *P. nodorum*. In a recent study, Wu et al. (2017) used a combined method between comparative genomics and GWA mapping by using 20 newly sequenced isolates of *Puccina triticina* from Australia. Based on 306,474 SNPs, the authors identified a polygenic architecture corresponding to 302 genes harboring at least one SNP associated with leaf rust virulence on wheat.

All the studies mentioned above were performed in controlled conditions or in common gardens with conditions that strongly differ from the habitats where the pathogenic microbes co-evolved with their natural hosts. Therefore, one of the next challenges is to perform phenotyping of the pathogenic trait of interest in more ecologically realistic conditions. In addition, because phytopathogenic microbes can be controlled by several members of the microbiota (Roux and Bergelson, 2016), it would be worthwhile to identify the genetic determinants in the pathogen species that are involved in the arms races with microbiota. Finally, as previously advised for plants, functional validation remains the gold standard to test whether the candidate genes identified by GWA mapping truly confer the aggressive/virulent phenotype to the microbial pathogen under study.

## A JOINT GWA MAPPING APPROACH TO DRAW A PICTURE OF THE NETWORK OF GENETIC INTERACTIONS BETWEEN PLANTS AND PATHOGENS

Plant GWAS have been demonstrated to be successful in identifying genomic regions associated with disease resistance (Table 1), whereas microbial GWAS reporting genomic regions associated with pathogenicity are still in the starting blocks. Despite this increasing number of GWAS, it is interesting
to note that GWA mapping has never been performed
on the two counterparts of the plant pathosystem, either
separately or jointly. Characterizing the molecular landscape

of plant-pathogen interactions can considerably increase our
knowledge on the co-evolutionary processes driving adaptive
dynamics of plant species in plant communities (Allen et al.,

<figure><img src="image_4.png" /><figcaption>FIGURE 1 | Hypothetical 3D-Manhattan plot of joint GWA mapping between the genome of a host plant and a pathogen species.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Illustration of the four steps of the free-phenotyping joint GWA mapping approach. Step 1: paired sampling of plants and microbiota in wild populations. Each color corresponds to a different plant population. Step 2: isolation of putative pathogenic strains. The green circle corresponds to the putative pathogenic strain whereas gray circles correspond to other members of the microbiota. Steps 3 and 4: whole-genome sequencing of both plants and microbial strains and genome-to-genome statistical analysis.</figcaption></figure>
2004; Karasov et al., 2014; Roux and Bergelson, 2016),
thereby increasing our understanding and predictions on ED
(Lambrechts, 2010; Roux et al., 2014a).

To study co-evolutionary quantitative genetics, the first
step will require (i) estimating the respective contributions
of plant and pathogen genetic variation to disease variation
and (ii) estimating the heritability of plant × pathogen
interactions ($H_{G×G}^2$) that can be calculated by dividing the
phenotypic variance associated with “plant genotype—by—
pathogen genotype” interactions by the total phenotypic variance
across genotypes of both biotic partners (Roux and Bergelson,
2016). Because extensive phenotypic data sets are required
for estimating the joint genetic effect of the plant and the
pathogen, studies reporting heritability estimates of plant ×
pathogen interactions remains scarce in the scientific literature.
This observation reinforces the need for the development of
automated high-throughput phenotyping platforms.

The next step requires the characterization of the genetic
architecture of plant-pathogen interactions; that is the number
of intergenomic epistatic QTLs, their physical locations in their
respective genomes and their corresponding effect (Figure 1).
Quantitative genetic methods have been developed to identify
host-by-pathogen QTL interactions (Wang et al., 2006; Yang
et al., 2008). These traditional QTL mapping based methods may
however not be adapted to genome scans on both interacting
species. New statistical methods must be developed with the
challenge of performing joint GWA mapping by taking into
account simultaneously the information provided by the genome
sequences of the plant and the pathogen (Roux and Bergelson,
2016). An additional challenge relies on the correction for the
effects of population structure of both interacting partners, by
including separately the additive polygenic random effects of the
plant species and the pathogen species.

While technically challenging, identifying in both biotic
partners the genes (and more precisely the causative variants)
that confer the quantitative phenotypic variants likely retained
by natural selection will help to understand and predict co-
evolutionary dynamics between a host and its pathogen either in
natural populations or in crop fields.

AN ECOLOGICAL GENOMICS APPROACH
TO IDENTIFY NATURAL GENETIC
VARIANTS DRIVING THE INTERACTIONS
BETWEEN PLANTS AND MICROBIAL
PATHOGENS: A FREE-PHENOTYPING
JOINT GWA MAPPING APPROACH

Based on coevolution signatures in host and pathogen genomes, an innovative free-phenotyping strategy was recently developed for global genome-to-genome analysis and employed in the human-HIV pathosystem (Bartha et al., 2013). Using paired human and viral data from 1,071 individuals, HIV-1 sequence variants were used as “phenotypic” traits to finely map human

genetic variants in interaction with viral genetic variants. The
authors demonstrated that using HIV-1 sequence variants was
much more powerful than viral load to finely map human SNPs
in the major histocompatibility complex (MHC) region that
was already reported as implicated in human-HIV coevolution.
By adopting an ecological genomics approach, this strategy can
also be applied in plant pathosystems. Here, we propose four
steps to detect highly significant associations between plant DNA
polymorphisms and pathogen sequence variation, without the
need to obtain large phenotypic data sets (Figure 2). Firstly,
plants are sampled across a given geographic area in conjunction
with the corresponding microbiota. If the demographic history
of the plant species has been reported, the sampling should
be limited to genetically homogeneous subgroups, allowing a
reduction of false positives due to population structure during
the genome-to-genome statistical analysis. Secondly, our method
requires the isolation of one representative strain (putative
representative pathogen strain) on each plant individual.
Traditional microbiological methods for strain isolation and
identification can be time- and material-consuming. However,
the systematic identification of a conspicuous number of
microbial strains can be strongly facilitated by combining
community-based culture collections (CBC), housekeeping gene
amplicon pooling (16S or *gyrB* for bacteria, 18S for fungi, ITS
for fungi and oomycetes) and NGS technologies (Armanhi et al.,
2016). With ever-cheaper genome-sequencing SMRT methods,
the third step consists in generating paired plant and pathogen
genomic data. The fourth step consists in performing joint
association analysis using both host and pathogen genomes,
with the lofty goal of identifying genetic polymorphisms in
strong LD across the two genomes. Similarly to joint GWA
mapping approaches based on phenotypic data, new statistical
methods must be developed for testing gene-gene interaction
while accounting for population structure including interactions
between the genetic backgrounds of the two organisms.

Based on co-evolutionary processes, combining paired plant,
and pathogen genomic information represents therefore an
exciting opportunity, especially in wild species, to describe the
molecular landscape underlying plant-pathogen interactions.

AUTHOR CONTRIBUTIONS

FR supervised the project. CB and FR wrote the manuscript.

FUNDING

This work was funded by the Région Midi-Pyrénées (CLIMARES
project) and the LABEX TULIP (ANR-10-LABX-41, ANR-11-
IDEX-0002-02).

ACKNOWLEDGMENTS

We are grateful to colleagues from the Laboratory of Plant-
Microbes Interactions for their helpful discussions.
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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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