# New resistance to bacterial wilt in heat-stressed tomato is revealed by two-reference Genome Wide Association

Adrien Belny, Henri Desaint, Mylène Rigal, Sébastien Carrère, Jérôme Gouzy, Gregori Bonnet, Céline Labourey, Elise Albert, Laurent Grivet, Fabrice Roux, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Adrien Belny, Henri Desaint, Mylène Rigal, Sébastien Carrère, Jérôme Gouzy, et al. New resistance to bacterial wilt in heat-stressed tomato is revealed by two-reference Genome Wide Association. 2025. [https://hal.inrae.fr/hal-05292858v1](https://hal.inrae.fr/hal-05292858v1)

HAL Id: hal-05292858

[https://hal.inrae.fr/hal-05292858v1](https://hal.inrae.fr/hal-05292858v1)

Preprint submitted on 2 Oct 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 **Title**

2 **New resistance to bacterial wilt in heat-stressed tomato is revealed**

3 **by two-reference Genome Wide Association**

4 **Authors**

5 Adrien Belny ¹, ² *, Henri Desaint ¹, ³*, Mylène Rigal¹, Sébastien Carrère¹, Jérôme Gouzy¹,

6 Gregori Bonnet³, Céline Labourey³, Elise Albert², Laurent Grivet², Fabrice Roux¹, Fabienne

7 Vailleau¹, Tristan Mary-Huard ⁴, ⁵ and Richard Berthomé¹+.

8 **Affiliations**

9 1 : LIPMe, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan 31320, France;

10 2 : SYNGENTA Seeds, Saint-Sauveur 31790, France

11 3 : SYNGENTA France SAS, 346 Route des Pasquiers, 84260 Sarrians, France

12 4: Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.

14 5: Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France.

16 * : co-first authors

17 + : corresponding author

18

19

20

21

22

23
24 Abstract

Bacterial wilt, caused by bacterial strains of the *Ralstonia solanacearum* species complex, is one of the most harmful diseases striking many crops including tomato. Its spread is dependent upon temperature and humidity, which are expected to fluctuate strongly due to climate change.

Previous results have highlighted that temperature elevation led to an increase in disease severity in commercial cultivars, whose resistance is quantitative and mostly relies on the Quantitative Trait Loci (QTL) *bwr-6* and *bwr-12*. In this study, we focused on temperature-dependent quantitative disease resistance (QDR) to bacterial wilt with the aim to unravel new resistance mechanisms that remain efficient at higher temperatures. For this purpose, a new panel of 189 accessions composed of tomato wild relatives, was assembled and sequenced thus creating a unique genomic resource. Its response to the *Ralstonia pseudosolanacearum* strain GMI1000 from three- to ten-days post-inoculation at 28°C and 32°C was explored. To discover the genetic basis underlying the responses of the panel, Genome-Wide Association (GWA) studies were conducted using the disease symptom scores recorded daily and monitored throughout the kinetics of the infection. To improve *QTL* detection, we have proposed a new approach using two reference genomes from within the panel. By correcting part of a single reference genome, especially when the only reference genome is a cultivar, this approach may be considered an alternative to pangenomic studies. As panel sequencing was highly resolutive, *QTL* positions allowed the identification of 44 candidate genes, which seemed to follow a temporal dynamic of activation after pathogen inoculation. Interestingly, no candidate genes were found to be common between the two phenotyping temperatures, highlighting the importance of the experimental design in addressing this type of question.

Most of our quantitative disease resistance candidate genes belong to gene families described as being involved in immunity. Moreover, a significant proportion appears to be expressed in roots where bacterial infection occurs. Among them, two candidates are closely linked to the
49 genomic positions of the *bwr-6* and *bwr-12* *QTLs*, the main *QTLs* of bacterial wilt Quantitative
50 Disease Resistance (QDR) studied whose mechanisms of action are still unknown.

51 Author summary

52 Bacterial wilt is a plant disease that affects more than 200 crop species (including tomato,
53 potato and banana) leading to high yield losses. The most efficient way to deal with this disease
54 is still the use of genetic resistance. However, previous studies have shown that numerous
55 sources of resistance are negatively affected when plants face heat stress, which is alarming in
56 a context of global warming. In this study, we have developed a new approach for discovering
57 candidate genes in tomato capable of conferring thermostable resistance to bacterial wilt.
58 Forty-four genes were sequentially detected over time, reflecting different temporal dynamics
59 of induction after inoculation. Even if none were found to be common between the two
60 temperatures, available information on their transcriptional regulation in roots and their
61 involvement in immune processes confirms their relevance. Finally, we provide a short list of
62 the candidate genes identified, some of which are currently undergoing functional validation
63 and will be used in breeding programs to help overcome epidemics in future years.
64

65

65 Introduction

66 Bacteria belonging to the *Ralstonia solanacearum* species complex (RSSC) are telluric
67 phytopathogens that infect plant roots, colonizing and multiplying in the xylem, clogging
68 vessels and leading to characteristic symptoms of plant wilting (1). The RSSC strains originate
69 from tropical and sub-tropical regions and are organized into four major genetic clusters,
70 called phylotypes. Interestingly, the geographical origins of the strains are correlated with their
71 phylotype (2). Strains of the RSSC are able to infect a wide range of plant species from 53
72 botanical families and are responsible for various bacterial diseases that can lead to
considerable economic losses in major crops (tomato, potato, eggplant and other Solanaceae species), crops of local importance (banana, peanuts, etc), and ornamentals (pelargonium, rose) (3, 2).

To limit the occurrence of bacterial wilt and overcome disease outbreaks, integrated pest management combines multiple strategies (4). Promising developments in control methods are based on, or use, living organisms (5,6) and can complement traditional cultivation practices, such as crop rotation (7) and pesticide use (4). However, multiple studies have shown that genetic solutions remain the most efficient, cost-effective and eco-friendly means of tackling these diseases (8, 9).

The genetic determinism of plant resistance to pathogens has been extensively studied for many years, providing an interesting framework for improved understanding of plant-pathogen interactions and qualitative plant resistance, also known as gene-for-gene resistance. It has also resulted in a highly concise definition of plant immunity, reduced to the induction of a two-level response described in the well-known zig-zag model (10). Its mechanisms are based on the type of host receptors involved: PAMP-triggered immunity (PTI) or effector-triggered immunity (ETI). Results from the last two decades have brought to light the fact that mechanisms involved in plant immunity appear to share interconnected actors (11). Indeed, knowledge acquired from Quantitative Disease Resistance (QDR) studies (12, 13) revealed their polygenic nature. Moreover, QDR were sometimes found to confer resistance to multiple pathogens and to be modulated spatio-temporally. Thus, the simple dichotomic description of PTI versus ETI is no longer relevant. In contrast, knowledge of the genetic basis of plant defense responses to RSSC remain sparse. The best characterized resistance to *Ralstonia solacearum*, identified in the model plant *Arabidopsis thaliana*, is mediated by the nucleotide-binding, leucine-rich repeat (NLR) immune receptor pair RPS4 / RRS1 (14, 15, 16). Initially defined as a qualitative type of resistance, subsequent work demonstrated that this
98 immunoreceptor pair conferred resistance to different strains of R. solanacearum (17, 14) as
99 well as to other pathogenic species, such as Colletotrichum higginsianum or Pseudomonas
100 syringae pv tomato DC3000 (18). Interestingly, two studies also demonstrated the role of
101 RPS4 / RRS1 in QDR against Xanthomonas campestris pv. campestris strain CFBP6943 and
102 R. solanacearum strain GMI1000 at 27°C (19, 20). Indeed, resistance to RSSC appears to be
103 quantitative in most species studied. Evidence comes from studies of the repertoire of type III
104 effectors, which are major determinants of the pathogenicity of RSSC strains, and genetic
105 analyses of host resistance. These studies corroborate the fact that plant resistance to RSSC is
106 strain-specific and mostly polygenic (8, 9). Overall, more than forty QTLs underlying plant
107 defense responses to different strains of RSSC have been identified in A. thaliana, tobacco,
108 potato, peanut, pepper, eggplant and tomato (21, 9), mainly by traditional QTL mapping
109 approaches or, more recently, by exploring species genetic diversity and GWAS. However,
110 the mechanisms involved remain unknown in most cases; except in few recent examples in A.
111 thaliana in which candidate genes have been functionally validated. These candidates
112 participate in resistance or susceptibility to the wild-type R. solanacearum strain GMI1000 or
113 to four T3E mutants. (20, 22, 23).

114 Tomato (Solanum lycopersicum) constitutes one of the main Solanaceous species
115 cultivated worldwide and serves as a model for fruit-bearing crops. As natural hosts of R.
116 solanacearum, most tomato cultivars have been described to be susceptible to a wide range of
117 strains belonging to the four phylotypes of the RSSC (24). Interestingly, Solanum
118 pimpinellifolium, a wild relative of cultivated tomato, has contributed to the identification of
119 resistance to over 45 different diseases (25, 26) including, for instance, resistance to
120 Phytophthora infestans (27, 28), Fusarium oxysporum (I2 gene) (29), gray leaf spot disease
121 (Sm gene) (30) and bacterial speck (Pto gene) (31). For bacterial wilt, the S. lycopersicum
122 'Hawaii 7996' cultivar is still the most tolerant to several strains of R. solanacearum (32) and
123 is currently used in many breeding programs. Interestingly, the tolerance of this cultivar
124 originates from S. pimpinellifolium accession PI127805A and is associated with at least seven
125 QTLs, of which bwr-6 and bwr-12 are well studied (33). However, none of the genes
126 underlying these QTL have been cloned and the genetic architecture of this QDR appears to
127 be complex. Indeed, bwr-6 and bwr-12 are made of several smaller QTLs: four for bwr-6 and
128 three for bwr-12 (34).

129 A growing number of studies have assessed various combinations of plant-pathogen
130 interactions under abiotic stress conditions (cold, high temperature, low nitrogen, drought) (35,
131 36), showing different plant defense responses to be strongly and negatively modulated by
132 abiotic constraints. However the underlying mechanisms remain poorly understood, most
133 studies being descriptive and supported by phenotypic data and transcriptomic data when
134 analyzed (35, 37). For temperature increases, one of the climatic parameters predicted to
135 fluctuate the most by the end of the century, a meta-analysis showed that 55% of a total of 145
136 genetic sources of resistance are negatively modulated by heat stress, which leads to increased
137 disease severity (37). For RSSC, heat stress unveils the thermosensitivity of the A. thaliana
138 RPS4/RRS1-R mediated resistance (20). This is also the case in Solanaceous species (38, 39).
139 Indeed, increased soil temperatures can impair the bacterial wilt QDR for some tomato
140 varieties (40). In Taiwan and other parts of South-East Asia, varieties with bwr-12-based QDR
141 tend to be more susceptible to the bacteria at high temperatures. When phenotyped at elevated
142 temperature, the ‘Hawaii 7996’ cultivar presented a wilting rate of between 28 % and 52 %
143 depending on the trial (32). To date the Hawaii 7996 cultivar QDR remains the most reliable.
144 However, new QTL conferring resilience to resistances to bacterial wilt in fluctuating
145 environments have yet to be identified and characterized.

146 The natural diversity in the response of plant species to pathogens and genome-wide
147 association studies (GWAS) has demonstrated their effectiveness in detecting QTL underlying
148 plant defense responses (41, 42). However, the development of such strategies using
149 accessions challenged by combined stresses is still scarce. Tomato wild-relatives originated
150 from tropical and equatorial regions and evolved under more extreme climates, probably in
151 contact with R. solanacearum species (2). Optimal growth conditions for tomato vary between
152 13 and 33°C depending on the variety studied (37), while the optimum growth temperature for
153 the R. solanacearum GMI1000 strain is 28°C (43). Therefore, we hypothesized that panel
154 accessions might have developed defense-related mechanisms specific to their biomes. So, by
155 using a specific tomato wild relative collection of accessions infected at two contrasting
156 temperatures, to mimic temperature elevation under controlled conditions, we aimed to
157 uncover thermostable QDR to R. solanacearum. To this end, a panel of 189 tomato wild
158 relatives was selected, sequenced and mapped onto two reference genomes, generated from
159 accessions of two tomato wild relatives representative of the genetic structure of the panel and
160 originating from contrasting climates. Analysis of the population structure of the panel
161 revealed its significant genetic diversity and a subdivision into five clusters. Furthermore,
162 inoculation with the GMI1000 strain at 28°C and 32°C revealed higher response variability
163 and more severe disease at the higher temperature. GWAS conducted following good practices
164 (44), with both reference genomes, at both temperatures, using phenotypic data acquired from
165 a symptom scoring time-course experiment, enabled detection of 28 biologically relevant
166 QTLs underlying 44 genes. Our study also highlights the temporal dynamic of plant responses
167 at both temperatures. Most of these QTLs were found using both reference genomes - except
168 when genomic structural variations impaired QTL detection, however none was common to
169 both temperatures. Interestingly, two QTLs are localized near two previously detected major
170 QTLs: bwr-6 and bwr-12, for which genes have not been identified.

171
173 **Biological material**

174 *Tomato panel composition and R. solanacearum strains*

175 Initially, a panel of 199 accessions was constituted, corresponding to a collection of
176 plants available in the Tomato Genetic Ressource Center (University of California, Davis
177 campus, USA) provided by SYNGENTA for the BURNED / Rethink project (INRA
178 n°15000441) and used in phenotyping experiments. Of the original panel, 189 accessions were
179 selected for GWAS. The list of accessions and passport data of the panel are available in [S1].
180 Passport data, when available, include geopositioning (168 accessions), altitude (125
181 accessions), country of origin, sampling province and species.

182 The whole panel was inoculated using the wild type *R. solanacearum* GMI1000 strain
183 classified by Hong et al. (45) as a phylotype I, race 1, biovar 3 strain. It originates from South
184 America (French Guiana) (46) and was isolated in tomato (47).

185

186 *Plant phenotyping conditions*

187 For each accession, seeds were surface sterilized. Five to 25 seeds were immersed for
188 10 min in a 15 mL FalconTM tube filled with 5 mL of a 20 % bleach solution mixed with 200
189 µL of TWEEN 20. The seeds were then washed four times with the same volume of sterilized
190 water, for 48 h at 4°C for stratification. Seeds were sown on rich BG solid medium (48) in
191 upside down petri dishes for 48 h at 28°C in the dark for germination. A maximum of two
192 germinated seeds per accession were transplanted into pots (7x7x6.4 cm) filled with loam
193 (Proveen potting soil Semi bouturage 2 with perlite, Gergy Fourniture, France). The accessions
194 were grown for two weeks at the Toulouse Plant Microbe Phenotyping plateform (INRAE-
195 LIPME, Toulouse, France) under controlled conditions in a growth chamber [25°C day / 24°C
196 night, 75% RH, 12 h light, LED lights (intensity in µmol m⁻² s⁻¹ per range of wavelengths
197 respectively at 100% Aria: 176; 400-700 / 1; 380-399 / 61; 400-499 / 20; 500-599 / 95; 600-
198 699/ 4; 700-780 / blue, green and peak at 444, 597 and 661 nm)]. Plants were randomized at
199 the beginning of the second week. Five days prior to inoculation, they were moved to a growth
200 chamber in the quarantine laboratory for acclimatization under controlled conditions (28°C
201 day / 27°C night, 75 % RH, 12 h light, 100µmol m⁻² s⁻¹). Inoculations were carried out
202 according to Morel et al. (49), with 40 mL of a solution at 5.10⁷ Colony-forming unit
203 (CFU)/mL/pot. Temperature treatments were applied simultaneously with the R.
204 solanacearum inoculation, plants being transferred to growth chambers with the following
205 controlled conditions: 28°C day / 27°C night, 75 % RH, 12 h light, 50 µmol m⁻² s⁻¹ or 32°C
206 day / 31°C night, 75 % RH, 12 h light, 50 µmol m⁻² s⁻¹. The wilting symptoms were scored
207 on an established zero-to-four disease scale as previously described (49) and were monitored
208 from two to ten days after inoculation (Dai).

209

210 **Tomato genomic resource creation and analysis**

211 *Panel sequencing and genome assembly*

212 The panel accessions were sequenced using Illumina chemistry by
213 SYNGENTA (GENEWIZ, US) using Illumina chemistry, with the aim of obtaining a raw
214 sequencing depth between 15X to 25X per accession. The LA1246 and LA1670 accessions,
215 representative of the two genetic groups previously identified in Blanca et al (50), were
216 selected to assemble new reference genomes and to fulfill this purpose were sequenced with a
217 PacBio Sequel Sequencer (Pacific Biosciences, Menlo Park, CA, USA) to reach a minimum
218 depth of 50X. A meta-assembly strategy similar to the one used to assemble the Rosa genome
219 (51) was applied. Very briefly, initial contigs were built using CANU 1.8 (52) and secondary
220 contigs were scaffolded using Bionano optical maps (DLE-1 enzyme, Saphyr system,
221 hybridScaffold.pl software). Details comprising protocols of DNA sample preparation,
222 sequencing and data processing after sequencing are available in [S2, S3].

223

224 Gene prediction & functional annotation

225 For both reference genomes, protein and non-protein coding gene models were
226 predicted using the integrative EuGene pipeline release 1.5 (53),
227 http://eugene.toulouse.inra.fr/Downloads/egnep-Linux-x86_64.1.5.tar.gz) and five protein
228 databases were aligned with NCBI-BLASTx to detect translated regions. Protein coding genes
229 were annotated by integrating the five sources of information. Results were successively
230 integrated depending on the expected accuracy of the source of information. Priority was given
231 to BLAST-P results from the "Solanum" database [S2, S3].

232

233 Variant calling and selection of Single Nucleotide Polymorphisms (SNP) for downstream
234 analysis

235 Once genome assembly was complete, variant calling was carried out using FreeBayes
236 (54) on the two genomes that will hereafter be referred to as reference genomes. Several filters
237 were applied to SNP matrices to remove SNPs unsuitable for further analyses [Table 1]. Filters
238 A, B, C & D were applied using PLINK1.07 (55). A MAF thresholding of 0.032 was applied
239 according to previously described formula (56). Since preliminary tests showed an over-
240 representation of heterozygous SNPs at extreme GWAS p-values, the filter E was added to
241 avoid disequilibrium between classes of allelic state. It led to the retention of 3,728,946 SNPs
242 (LA1670) and 3,464,481 SNPs (LA1246) for the GWAS SNP matrices [Table 1].

243

244 Table 1: Filters applied to the SNP matrix and the corresponding number of remaining
245 SNPs at each step. Filter A corresponds to FreeBayes output (54) and Filter E was added to
246 avoid a disequilibrium in the representation of each allelic state (56). Identifiers of the filters
247 used and correspondence; A: raw output from genome assembly; B: vcftools filters to remove
248 low quality SNPs (minQ40, biallelic, max missing 0.95); C: minor allele frequency (MAF) at
249 0.032; D: LD<0.2; E: Threshold used to ensure the presence, for each SNP, of six to seven
250 individuals for each allelic state.

<table><thead><tr><th rowspan="2">Filter used</th><th colspan="2">Reference Genome (number of SNP)</th><th rowspan="2">Analyses</th></tr><tr><th>LA1670</th><th>LA1246</th></tr></thead><tbody><tr><td>A</td><td>58 353 015</td><td>58 606 561</td><td></td></tr><tr><td>A + biallelic</td><td>47 430 644</td><td>47 486 684</td><td></td></tr><tr><td>A + B</td><td>25 622 125</td><td>25 929 267</td><td></td></tr><tr><td>A + B + C</td><td>13 541 872</td><td>14 551 270</td><td>PCA</td></tr><tr><td>A + B + C + D</td><td>1 836 148</td><td>2 567 085</td><td>Kinship</td></tr><tr><td>A + B + C + E</td><td>3 728 946</td><td>3 646 481</td><td>GWAS SNPs matrix</td></tr></tbody></table>

251

252 Kinship estimation and population structure analyses

253 The kinship matrix was computed using the Balding-Nichols method (57)
254 implemented in EMMAX software (Kang et al. 2010), using a filtered matrix (filters
255 A+B+C+D).

256 Population structure was investigated using Principal Component Analysis (PCA) with
257 R package gdsfmt (58) and its function snpgdsPCA(). Results are available in [S4].
258 Additionally, hierarchical clustering was performed on kinship matrices of both reference
259 genomes. Five clusters were chosen for representation. The fixation index (59) between these
260 clusters was then computed with vcftools using the GWAS SNP matrix [Table 1] over
261 windows of 10 kbp.

262

263 Extent of linkage disequilibrium (LD) analysis
The extent of LD ($r^2$ measure) was investigated in two ways. Firstly, LD half-decay was computed in a three-step procedure: GWAS matrices were sub-sampled (10% of markers for each chromosome), then $r^2$ between pairs of markers from 1 bp to 1 Mbp in distance were computed; finally the mean LD between pairs of markers within 1 kbp windows was computed. Secondly, to visualize LD patterns along chromosomes, a kinship-corrected LD (60) was computed using a two-step procedure: 1,000 SNPs were firstly sampled from GWAS matrices for each chromosome, then a kinship-corrected $r^2$ was computed for all SNP pairs on each chromosome using R package LDcorSV (61).

272

## 273 Natural variation of QDR in the tomato panel

### 274 Experimental design

Five blocks of 450 plants were constructed using a random complete block design with two technical repetitions for each block. Each repetition was composed of nine trays of 25 positions corresponding to 225 plants with one plant per accession ($n = 198$ accessions) and the tolerant control accession 'Hawaii 7996' (62) placed at the same three positions (top-left corner, center, bottom-right corner) within each tray ($n = 9$ trays x 3 replicates per tray=27).

280

### 281 Correction for environmental effects and timepoint selection

For each combination of temperature and Dai, referred to as a condition hereafter, phenotypic data were adjusted using the following mixed model (1):

284 $Y_{ibr} = \mu + G_i + B_b + I_{ib} + \epsilon_{ibr}$ (1)

285 $G_i \hookrightarrow N(0, \sigma_G^2)$ independent and identically distributed (iid)

286 $B_b \hookrightarrow N(0, \sigma_B^2)$ iid

287 $I_{ib} \hookrightarrow N(0, \sigma_I^2)$ iid
288 $\epsilon_{ibr} \to N(0, \sigma_{\epsilon}^2)$ iid

289 $G \perp B \perp I \perp \epsilon$

290 where $Y_{ibr}$ denotes the phenotype of the $r^{th}$ repeat of the $i^{th}$ genotype in the $b^{th}$ block,

291 $\mu$ is the intercept, $G$ is the genotypic random effect, $B$ is the block random effect, $I$ is the block

292 x genotype interaction random effect and $\epsilon$ is the vector of errors. $\perp$ means independence

293 between random effects. R package **lme4** (63) was used to adjust mixed models.

294 Model (2) was used to compute broad-sense heritabilities, applying the following

295 formula adapted from (64):

296 $$H^2 = \frac{\sigma_G^2}{\sigma_G^2 + \frac{\sigma_B^2}{n_B} + \frac{\sigma_I^2}{n_B * n_R} + \frac{\sigma_{\epsilon}^2}{n_B * n_R}} \quad (2)$$

297 where $n_B$ denotes the number of blocks and $n_R$ the number of repetitions.

298 Conditions presenting a broad-sense heritability lower than 0.1 were filtered for later

299 analysis [S5]. Thus, the following conditions were chosen: 7 and 10 Dai for a temperature of

300 28°C and 4, 5, 6, 7 and 10 Dai at 32°C. The early days did not pass the threshold since the

301 disease symptoms took a few days to appear.

302 For the selected conditions and for each accession, least-square-means (lsmeans) were

303 obtained using a modified version of model (1): the genotypic random effect of model was

304 turned into a fixed effect. Lsmeans were then computed using the **lmerTest** (65) R package.

305 In the rest of the article, these lsmeans will be designated as the phenotypes.

306

307 GWAS and QTL identification

308 For each selected condition, the GWAS model from (66) was fitted:

309 $$Y = X\beta + S\tau + Zg + \epsilon \quad (3)$$

310 $g \to N(0, K * \sigma^2)$

311 $\epsilon \to N(0, \sigma_{\epsilon}^2)$ iid
312 $g \perp \epsilon$

313 where Y is the phenotype vector; $\tau$ is the fixed SNP effect; $S$ is the SNP incidence matrix
314 (with values in {-1,0,1}); $\beta$ is the fixed structure effect; $X$ is the structure matrix
315 (corresponding to the first 3 PCA axes, refer to [S4] for the choice of the number of axes); $K$
316 is the kinship matrix; $g$ is the random genotypic effect vector and $\epsilon$ corresponds to the vector
317 of errors.

318 In this study, an approximation for the variance component estimation of the original
319 model from Yu et al. (66) was used for computational purposes, using EMMAX (Efficient
320 Mixed-Model Association eXpedited) (67). We used the EMMAX version implemented in R
321 package rrBLUP (68), throughout the GWAS() function.

322 Subsequently, the previously described local score method (69) was applied to p-
323 values obtained from the EMMAX model. It aims to control the amount of false positives and
324 to delimit QTL positions precisely. According to Bonhomme et al. (69), the parameter $\xi$, which
325 controls QTL detection stringency, was set to $\xi = 3$. The procedures returns zones, i.e.
326 intervals of genomic positions in which the association is significant. These significant zones
327 will hereafter be called (putative) QTLs. One GWAS was performed for every retained
328 condition. For QTLs detected on several consecutive days during kinetics, overlapping QTL
329 positions were merged. Based on the genome annotation of our reference genome, physical
330 positions of genes were calculated. When the position of the gene +/-1000 bp overlapped a
331 QTL, the gene was defined as a "strict candidate gene". An interval of "+/- 1000bp" was added
332 to take into account the promoter regions of genes. The latter definition did not take into
333 account the case where a peak was localized in an intergenic region. The closest genes
334 downstream and upstream of the QTL were therefore also considered and were defined as
335 "intergenic candidate genes".
337 *Comparisons of QTLs and candidate genes between two reference genomes*

338 The physical position of genes varied between the two reference genomes. To ascertain
339 the similarity of candidate genes, they were subject to BLAST analysis onto each other's
340 genomes. Genes were considered similar if they were (1) located on the same chromosome;
341 (2) their BLAST-N p-values were closed to zero; (3) they shared a similar annotation; and (4)
342 they had similar genomic environments (i.e genes were found upstream and downstream).

343 The relevancy of candidate genes was assessed using the following methodology. First,
344 gene ontologies and identification of putative biological functions in which candidate genes
345 underlying *QTLs* might be involved was investigated. For this purpose, coding sequences of
346 candidate genes were extracted and subject to BLAST analysis (BLAST-N) via the
347 Solgenomics site (https://solgenomics.net/tools/blast/; Tomato Genome cDNA ITAG release
348 4.0). The same strategy was applied to find orthologs in the *A.thaliana* genome using the
349 BLAST-X tool on the TAIR website (https://www.arabidopsis.org/Blast/), selecting
350 candidates with the highest p-values. Then, the spatiotemporal expression of identified genes
351 was explored using expression browser tools for *A.thaliana* and tomato on the Bio-Analytic
352 Resource for Plant Biology website (eplant tools; https://bar.utoronto.ca). Second,
353 comparison between the two reference genomes was performed to determine whether detected
354 *QTLs* were either common or specific and if structural variations could explain the specificity
355 of the *QTLs*. Gene alignment was performed using annotations of both reference genomes.
356 For each pair of paralogs from the two reference genomes, the following process was applied:
357 (1) start positions of the two genes were set to zero in a script to align candidate genes at the
358 same absolute position; (2) for visualization of putative structural variation, gene structure
359 (exons, 5', 3'UTR) was mapped onto each genome for gene pairs using the MultiAlin tool
360 (70) and regions of both reference genomes were aligned with IGV genome browser (71) on
361 a window 13 kbp in length starting at 1 kbp before the candidate gene start codon; (3) finally,
positions and p-values of SNPs were added to the custom alignment script to aid in discerning whether the gene coverage of SNPs was equivalent for each reference genome and whether top SNPs were in exons. Moreover by visualizing positions, it was possible to determine if peaks were shared between reference genomes. The *QTL* identified [Table 3] were named as follows, using the example of *QTL* '9-2-BOTH': the first number (9) indicates the chromosome, the second (2) indicates the order of appearance on the chromosome (starting from the beginning of the chromosome) and the presence of BOTH indicates that this *QTL* was detected using the two reference genomes. Alternatively, if the *QTL* is specific to one or other of the reference genomes, the pedigree of the latter is indicated. Finally, *QTL* names beginning with 'IG' indicate that they have been classified as 'Intergenic *QTL*'.

372

## 373 Results

### 374 Panel characteristics and choice of reference genomes

The 189-accession panel used in this study included five *Solanum* species: 167 *S. pimpinellifolium*, 20 *S. lycopersicum* var *cerasiforme*, one *S. cheesmaniae*, and the *S. lycopersicum* cultivar 'Hawaii7996', known to confer tolerance to bacterial wilt (62). The precise localization information available for 168 accessions enabled their positioning on a map [Fig. 1, A and C]. Among the 189 accessions selected, a majority came from Peru (73%) and a significant proportion came from Ecuador (14%). The *S. lycopersicum* var. *cerasiforme* accessions originated from Central America and from the western side of the Andes. Some *S. pimpinellifolium* were also found in this area, but most were sampled from the eastern side, either in the mountainous areas of southern Ecuador / northern Peru or in the coastal valley near the Pacific Ocean in southern Peru. The accessions were collected at different altitudes. The majority (45%) were found below 500 m. 11% were found in a range of altitudes between
386 500 m and 1,000 m and finally 11 % above 1,000 m. The highest sampling point indicated was
387 3,000 m.

388 We selected two reference accessions within our panel based on results from a previous
389 genetic study and climatic data obtained using their GPS coordinates (72), [S1, S6]. Indeed,
390 Blanca et al. showed that genetic diversity of their smaller panel of S. pimpinellifolium was
391 organised in two major genetic clusters (50). We decided to select one accession per cluster
392 and in addition accessions had to belong to two contrasted biomes. Thus, we selected LA1670
393 and LA1246: LA1670 originated from an arid environment and LA1246 a wetter cooler
394 environment.

<figure><img src="image_3.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_6.png" /></figure>
**Figure 1: Geographical origin and population structure of the panel.** A-C: Geographical localization on a map of South America of the groups identified depending on the reference genome used: LA1246 (A) or LA1670 (C). B-D: Representation of the panel in the first two PCA axes computed with population structure using LA1246 (B) and LA1670 (D) reference genomes. Colored spots represent groups of accessions that were identified using kinship clustering (see the Material & methods section for more details). Square: LA1246 from Ecuador; triangle: LA1670 from Peru; circle: cultivar 'Hawaii 7996'.

403

404 **Genomic data analyses of the panel**

405 Reference genomes were sequenced using PacBio technology and reached a depth of 406 82X for LA1246 and 80X for LA1670 [S2, S3]. The results obtained were similar for both 407 reference genomes and were evidence of their high sequence quality. Interestingly, genome 408 assembly highlighted large chromosomic rearrangements between the two reference genomes, 409 especially on chromosomes 6, 7 and 8. The final assembled genome size was 815.7 Mb for 410 LA1670 and 808.6Mb for LA1246. Gene prediction revealed 46,202 (LA1670) and 45,746 411 (LA1246) genes.

412 Illumina sequencing of the panel revealed a depth of between 15X and 25X for raw 413 sequenced data which varied among accessions [S2]. For SNP [Table 1, filter A], the average 414 coverage at a locus was between 13.66x (LA1246) and 13.81x (LA1670).
Reference genomes were used to call SNPs obtained by panel sequencing. We assessed the gene SNP coverage by using filtered GWAS SNP matrices [Table 1, filter A, B, C and E]. Results indicated that at least one SNP was found in 80% of genes with, on average, 9.6 SNPs/gene whichever reference genome was chosen. This coverage varied between 7.5 and 13.2 SNPs/gene depending on the chromosome [Fig 2].

<figure><img src="image_7.png" /></figure>

**Figure 2: Gene coverage by chromosome for each reference genome.** Bar height indicates the mean number of SNPs per gene for each chromosome. **A:** LA1246, **B:** LA1670. The red line indicates the fraction of the gene containing at least one SNP.

**Population structure and LD analysis reveals a large panel genetic diversity suitable for GWAS**

Population structure was investigated through PCA using SNPs called from the two reference genomes. [Fig. 1, B and D] represent panel accessions on the first two PCA axes, which explain cumulatively around 10% (9.05% for LA1246 and 10.60% for LA1670) of the genetic variation, along which panel accessions segregate into five clusters. These results
431 indicate that the choice of the reference genome has little impact on the PCA and on the clustering. Based on their distance on PCA axes [Fig. 1, B and D] the two reference genomes 432 appear diversified.
433

434 The first PCA axis can be interpreted as a North-South gradient whereas the second PCA axis suggests the existence of an East-West gradient [Fig. 1, A and C]. Accessions in 435 the middle of the graph [Fig. 1, B and D] are probably admixed genotypes. Clusters identified 436 from the hierarchical kinship clustering are represented in colour [Fig. 1], and show consistent 437 F ST values [S7]. For instance, accessions from clusters 4 and 5 for LA1670 are close together 438 on the PCA axes [Fig. 1D], on a geographical map [Fig. 1C], and share a low F ST value [S7]. 439
440 The extent of LD was investigated using the filtered GWAS matrix [Table 1, filters A, B, C 441 and E]; its half-decay was calculated per chromosome and is of order of a few kbp for most 442 chromosomes, with the exception of chromosomes 5, 6 and 8 for which LD decay is lower 443 [S8, S9]. Specifically, LD visualization on these latter chromosomes showed large blocks of 444 LD, located in central chromosomic regions, whereas LD is relatively low in telomeric regions 445 [S10].

446

447 **Panel responses to R. solanacearum reveal three temperature-dependent**
448 **profiles**

449 Panel accessions were phenotyped at 28°C and 32°C for the severity of bacterial wilt 450 disease symptoms for ten days following pathogen inoculation. The three temperature- 451 dependent profiles identified among panel accessions and shown in [Fig. 3] were: (1) 452 accessions more susceptible to disease at 32°C than at 28°C (133 out of 189 accessions); (2) 453 accessions exhibiting a same behavior independently of the temperature applied (49 out of 454 189); (3) accessions less susceptible at 32°C (7 out of 189) [Fig. 3]. Following inoculation, 455 disease severity increased with time [Fig. 4], indicating that plants became gradually more
susceptible and could not recover from the disease. At 28°C, the trend showed a difference in response to *R. solanacearum* between the two reference accessions, with LA1246 being symptomless [Fig. 4]. However, at 32°C, both reference accessions developed wilting symptoms. The 'Hawaii 7996' cultivar, which was used as a control due to its tolerance to bacterial wilt, started to wilt moderately in the later days of the experiment at 32°C [Fig. 4]. Interestingly, for some accessions, disease symptom curves remained below the one of 'Hawaii 7996' [Fig. 3], suggesting that these accessions were more resistant to bacterial wilt than 'Hawaii 7996' at 32°C. Altogether, phenotypic data suggested temperature-dependent patterns of response to *R. solanacearum*.

<figure><img src="image_8.png" /><figcaption>**Figure 3: Difference between the phenotypic response of the panel to *R. solanacearum* at 32°C and 28°C at 10 Dai.** Ratings used were adjusted means (lsmeans) for every panel accession, which enabled classification of the panel into three groups according to their defense response to *R. solanacearum*. Colours represent groups in which the response to *R. solanacearum* differs. Dark gray: more susceptible at 32°C ($\Delta_{ratings32C-28C} > 0.5$); light gray: similar at both temperatures ($|\Delta_{ratings32C-28C}| < 0.5$); white: more susceptible at 28°C. $\Delta_{ratings32C-28C} < -0.5$).</figcaption></figure>
<figure><img src="image_9.png" /></figure>

Figure 4: Kinetics of the response of panel accessions to R. solanacearum at two different temperatures. A: 28°C; B: 32°C. The y-axis represents adjusted means (Lsmeans) of the disease index (ranging from zero to four) over Dai. Each line represents the disease curve of one accession. Coloured curves represent the overall mean response of the panel (green), the 'Hawaii 7996' cultivar (yellow) and the accessions used as reference, LA1246 (blue) and LA1670 (purple).

GWAS and post-GWAS analyses identify relevant candidate genes involved in bacterial wilt defense response

GWA was performed on a sub-sample of selected conditions (see M&M). Results are presented in [S11] and [Table 3]. After filtering QTLs, 21 "strict candidate genes" (see M&M
for details) belonging to 18 *QTLs* were selected. *QTL* size did not exceed a few kbp. In almost all cases, one candidate gene was positioned at one *QTL*, indicating gene-level resolution. Exceptions include *QTL 1-3-LA1246* whose position was larger (12 kbp); gene density was particularly important in this region. The other exception was *QTL 1-4-BOTH*, which underlies two candidate genes; however, SNPs with the lowest p-values (RIZ2870_SpimpiLA1670Chr01_88223852_T for LA1670 and RIZ2870_SpimpiLA1246Chr01_89956087_T for LA1246) were located in an exon of the *Solyc01g105270* homolog. When "intergenic candidate genes" were considered [Table 3], 23 additional candidate genes related to ten *QTLs* were added. Even if they did not pass the 'vicinity with annotated genes' criterion, some were of interest: for instance, *QTL IG12-1-BOTH* was systematically detected over four consecutive days [S11], which constitutes the maximum number observed in the analysis.

**Table 3: QTL and corresponding candidate genes.** A: name of the *QTL*. B: the reference genome(s) on which the *QTL* was detected. According to our candidate gene filters, a * stipulates the *QTL* underlying the candidate gene was detected with one reference genome and was slightly below the threshold p-value with the other reference genome, even if an association peak was observed (see Suppl. Fig [S11] for details). C: temperature condition (°C) at which the *QTL* was detected. D: Corresponding *Solanum lycopersicum* Gene ID and its related annotation found for the 'Heinz' reference. E: Summary of gene expression pattern in organs retrieved from eplant tomato visualization tools (https://bar.utoronto.ca/eplant_tomato/). F: Corresponding *A. thaliana* orthologous gene ID and its related annotation when available; (?) corresponds to genes with low-confidence BLAST results. G: Summary of gene expression data in organs retrieved from eplant *A. thaliana* visualization tools (https://bar.utoronto.ca/eplant/). NA: not available.
<table><thead><tr><th>QTL name (A)</th><th>Reference genome (B)</th><th>Temperature (C)</th><th>Homolog (D)</th><th>Expression pattern (E)</th><th>Ortholog (F)</th><th>Expression pattern (G)</th><th>References</th></tr></thead><tbody><tr><td>1-1-LA1670</td><td>LA1246*/LA1670</td><td>28</td><td>Solyc01g006660<br>Subtilisin-like serine protease, CO(2)-response secreted protease-like</td><td>Roots, extracellular</td><td>Atg20160<br>SBTS2, subtilisin-like serine protease</td><td>Stem, roots; roots; endoplasmic reticulum, extracellular; negative response to bacteria</td><td>(Ascenscibanez et al., 2008)</td></tr><tr><td>1-2-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>Solyc01g086890<br>Splicing factor 4-like protein G-patch</td><td>Leaves, mature fruits, roots; nucleus</td><td>Atg52120<br>CIS1, CRY2 interacting splicing factor 1, regulate thermosensory flowering via FLM alternative splicing.</td><td>Everywhere; nucleus, cytosol</td><td></td></tr><tr><td>1-3-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>Solyc01g100630<br>Catalase</td><td>Leaves, fruits, roots, peroxysome</td><td>At4g35090 (?)<br>multigene family</td><td>everywhere; peroxysome, mitochondrion, response to infection</td><td></td></tr><tr><td>1-3-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>Solyc01g100640<br>Catalase</td><td>fruits, peroxysome</td><td>At4g35090 (?)<br>multigene family</td><td>everywhere; peroxysome, mitochondrion, response to infection</td><td></td></tr><tr><td>1-3-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>Solyc01g100650 (?)<br>NHL repeat-containing protein 2-like<br>Solyc01g105260</td><td>leaves, fruits, chloroplast</td><td>At1g56500</td><td>everywhere; plastids; small response to pathogen infection</td><td></td></tr><tr><td>1-4-BOTH</td><td>LA1246/LA1670</td><td>28</td><td>Protein of unknown function<br>DUF125, vacuolar iron transporter<br>homolog 4-like<br>Solyc01g105270</td><td>Roots</td><td>NA</td><td>NA</td><td></td></tr><tr><td>1-4-BOTH</td><td>LA1246/LA1670</td><td>28</td><td>SITOM1b tobamovirus multiplication<br>1 homolog b<br>Solyc02g067460</td><td>Mature fruits and roots; plasma membrane</td><td>NA</td><td>NA</td><td>(Ishikawa et al., 2022)</td></tr><tr><td>2-1-BOTH</td><td>LA1246/LA1670</td><td>28</td><td>Mitochondrial outer membrane<br>protein porin<br>Solyc03g123790</td><td>seedling roots, fruits, mitochondrion</td><td>Multigene family</td><td>NA</td><td></td></tr><tr><td>3-1-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Putative calcium/proton exchanger<br>Solyc03g093130 (?)<br>xyloglucan-modifying enzyme<br>Solyc03g097470</td><td>roots, fruits, plasma membrane<br>Fruits, roots; extracellular</td><td>At1g55730 (?)<br>multigene family<br>Multigene family</td><td>NA</td><td></td></tr><tr><td>3-2-LA1670</td><td>LA1246*/LA1670</td><td>28</td><td>3-Beta-ketoacyl-acyl carrier protein<br>synthase III (FabH)</td><td>Fruits; plastids</td><td>Atg62640<br>3-ketoacyl-acyl carrier protein synthase III</td><td>Everywhere; response to infections</td><td>(Ascenscibanez et al., 2008)</td></tr><tr><td>3-4-LA1670</td><td>LA1246*/LA1670</td><td>28</td><td>Solyc03g019730<br>Sumo activating enzyme 1b<br>Molybdenum cofactor biosynthesis</td><td>Leaves, fruits, roots; mitochondria</td><td>At5g50580 & At5g50680<br>AT-SAE1-2, SAE1B, sumo-activating enzyme 1B</td><td>Seeds; cytosol</td><td></td></tr><tr><td>4-1-LA1246</td><td>LA1246/LA1670*</td><td>28</td><td>Solyc04g008310<br>UDP-glucuronosyl/UDP-glucosyltransferase<br>Solyc04g078590</td><td>Leaves, fruits, roots</td><td>At2g15490<br>UDP-glycosyltransferase 73B4</td><td>Seeds, roots; cytosol, cell wall, response to pathogen and nitrogen</td><td>(Langlois-Meurinne et al., 2005)</td></tr><tr><td>4-2-BOTH</td><td>LA1246/LA1670</td><td>28</td><td>Receptor kinase Serine/threonine<br>kinase<br>Solyc07g005040 &<br>Solyc00g257110 ATPase 8,<br>plasma membrane-type-like</td><td>Flowers, immature fruits; plasma membrane</td><td>Multigene family</td><td>NA</td><td>(Gou et al., 2023)</td></tr><tr><td>7-1-LA1670</td><td>LA1670</td><td>32</td><td>NA</td><td>Flowers</td><td>Multigene family</td><td>NA</td><td></td></tr><tr><td>7-2-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc07g044730 (?)<br>3-hydroxylisobutyryl-CoA hydrolase-like protein 5</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>8-1-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>NA</td><td>NA</td><td>At1g06550 (?)<br>multigene family</td><td>NA</td><td></td></tr><tr><td>9-1-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc09g009700<br>GDSL esterase/lipase</td><td>Flowers, roots; extracellular</td><td>At5g03610<br>GGL25 - GDSL-motif esterase/acyltransferase/ lipase</td><td>NA</td><td></td></tr><tr><td>9-2-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc09g011640<br>Glutathione S-transferase-like</td><td>Leaves, roots; cytosol</td><td>At2g29420 (?)<br>Glutathione S-transferaseE</td><td>Stem, embryo, primary root; endoplasmic reticulum, extracellular; response to pathogen and nitrogen</td><td>(Mohr & Cahill, 2007)</td></tr><tr><td>10-1-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>Solyc09g081260<br>Multidrug resistance protein mdtK/<br>Multi antimicrobial extrusion protein<br>MatE<br>Solyc01g016350<br>(Unknown Protein)</td><td>Fruits; plasma membrane</td><td>At5g17700<br>MATE efflux family protein</td><td>Roots, petals. Response to hormones and infection</td><td>(Ascenscibanez et al., 2008)</td></tr><tr><td>IG1-1-LA1670</td><td>LA1246*/LA1670</td><td>28</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG1-1-LA1670</td><td>LA1246*/LA1670</td><td>28</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG1-1-LA1246</td><td>LA1246/LA1670*</td><td>28</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG1-1-LA1246</td><td>LA1246/LA1670*</td><td>28</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG2-1-LA1670</td><td>LA1670</td><td>28</td><td>Protein SHI RELATED SEQUENCE 5</td><td>flower buds, fruits, roots</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG2-1-LA1670</td><td>LA1670</td><td>28</td><td>Solyc02g084670<br>NA</td><td>flower buds, plastids</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG3-1-LA1246</td><td>LA1246</td><td>28</td><td>Solyc03g093770<br>histone-lysine N-methyltransferase</td><td>leaves ?</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG3-1-LA1246</td><td>LA1246</td><td>28</td><td>NA</td><td>NA</td><td>At4g12560 (?)</td><td>NA</td><td></td></tr><tr><td>IG4-1-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>NA</td><td>NA</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG4-1-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>Solyc06g028960<br>Glutaredoxin family protein</td><td>leaves</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG4-1-LA1246</td><td>LA1246/LA1670*</td><td>32</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG6-1-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc06g051510<br>Protein kinase superfamily protein</td><td>roots, fruits, leaves, nucleus</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG6-1-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Multigene family<br>Solyc06g065070</td><td>NA</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG6-2-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>FAD-binding domain-containing<br>protein<br>Solyc06g065060<br>FAD-binding domain-containing<br>protein</td><td>fruits ?, vacuole</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG6-2-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc07g007610<br>At2G21500 protein (Fragment)<br>(AHRD V1 ***- B9DHX0_ARATH); contains Interpro<br>domain(s) IPR001841 Zinc finger, RING-type</td><td>fruits ?, vacuole</td><td>multigene family</td><td>NA</td><td></td></tr><tr><td>IG7-1-LA1670</td><td>LA1246*/LA1670</td><td>32</td><td>Solyc07g007600<br>vacuolar-type H+-pyrophosphatase</td><td>fruits, roots, leaves</td><td>At1g15690</td><td>Everywhere; mostly vacuole & plasma membrane; response to some infections</td><td></td></tr><tr><td>IG7-2-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG7-2-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc07g007940<br>Small nuclear RNA activating<br>complex (SNAPc), subunit SNAP43<br>protein (AHRD V3.3 ***<br>AT3G53270.9)</td><td>fruits, roots, leaves, cytosol</td><td>At3g53270</td><td>Everywhere; cytosol, nucleus; response to some infections</td><td>(Ascenscibanez et al., 2008)</td></tr><tr><td>IG8-1-LA1670</td><td>LA1670</td><td>32</td><td>Solyc08g045620<br>Ycf2 (AHRD V1 ***- A6Y9Y9_9MAGN); contains Interpro<br>domain(s) IPR008543 Chloroplast<br>Ycf2</td><td>NA</td><td>AtCg01280 & AtCg00860</td><td>Everywhere; mostly plastids; small response to infections</td><td></td></tr><tr><td>IG8-1-LA1670</td><td>LA1670</td><td>32</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td></td></tr><tr><td>IG12-1-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc12g008960<br>Calmodulin-binding protein</td><td>roots, nucleus</td><td>At4g33050 (?)<br>multigene family</td><td>Everywhere; cytosol, mitochondrion, nucleus; response to some infections</td><td></td></tr><tr><td>IG12-1-BOTH</td><td>LA1246/LA1670</td><td>32</td><td>Solyc12g008950<br>DEAD-box ATP-dependent RNA helicase</td><td>roots, nucleus</td><td>At1g72690 (?)</td><td>nucleus; response to some infections</td><td></td></tr></tbody></table>
511 GWAS using two reference genomes improve QTL detection

512 Among the 21 “strict candidate genes” presented in [Table 3], 11 out of 21 (indicated
513 by a * in [Table 3]) were identified with one reference genome and displayed an association
514 peak slightly below the detection threshold with the other one, and 9 were identified with
515 significant peaks in both reference genomes. These 20 candidate genes can be considered as
516 common to the two reference genomes.

517 First, a statistical explanation could be given: based on the criterion of QTL selection,
518 some QTLs were close to the significance threshold and passed it only with one reference
519 genome. This is the case for QTL 1-2-LA1246, identified as specific to LA1246 [Fig. 5A]. We
520 selected this QTL because it was detected at only one Dai at 32°C, but its peak exceeded ten
521 in local score (LA1246). By contrast, at the same position on the LA1670 reference genome,
522 the peak value was below ten, also at the same Dai, meaning this QTL did not meet the criteria
523 for our selection. However, since the peak surpasses the significance threshold of the local
524 score method (red and blue lines), QTL 1-2-LA1246 has been flagged as nearly shared (denoted
525 by an *) in [Table 3]. The GWAS model used was based on a statistical test using both kinship
526 and population structure (see M&M for more details). Although the correlations between
527 kinship matrices from the two reference genomes were strong (> 0.9), the differences between
528 them could still result in the detection of different QTLs in each reference genome.

529 A second explanation is linked to the variant calling used when building SNP matrices.
530 A candidate gene cannot be detected if no informative allelic variant is present in proximity.
531 [Fig. 5 B] illustrates this situation with the QTL 7-1-LA1670, underlying a gene encoding an
532 APT8 Plasma membrane like protein with different SNP coverage depending on the reference
533 genome used. The high gene coverage with LA1670 allowed its detection whereas the exon in
534 which the top SNP is found is not covered by any SNP in the LA1246 reference.
A final explanation relates to structural variations between reference genomes, which were investigated by BLAST analysis of reference genome sequences against each other. For instance, structural variations affecting the *QTL* detection are highlighted in [Fig. 5 C] for the *QTL 4-1-LA1246* underlying a gene encoding a potential UDP-glycosyltransferase. The two indels modifying the genomic environment on LA1246 reference genome may have affected the detection of its counterpart in LA1670. The differential detection of several *QTLs* specific to one of the two reference genomes, as shown in [Fig. 5 A], could be attributed to this type of structural variation. For example, a genomic inversion is observed only near *QTL 3-2-LA1670*. Structural variations also particularly affected the regions of 'intergenic candidate genes'; for instance, an inversion was detected near *QTL IG3-1-LA1246*, while an indel of approximately 200 kbp was found between the two genes underlying *QTL IG8-1-LA1670*. Additionally, BLAST analysis of the genes closest to *QTL IG2-1-LA1670* in the LA1246 genome did not reveal a gene annotated as a putative serine/threonine phosphatase, which is present in the LA1670 genome.

549
550
551
552
553
554
555
556
557
558
559
<figure><img src="image_10.png" /><figcaption>**Figure 5: Three different cases for which the use of two reference genomes helped QTL detection.** A: example of the effect of the reference genome on QTL detection due to selection criteria. GWAS with local score at 32°C at 10 Dai for LA1246 (left) and LA1670 (right) reference genomes. The light-blue peak in chromosome 1 underlies QTL 1-2-LA1246, reported</figcaption></figure>

560

561

562

563

564
565 as being unique to LA1246 even if a peak of association, below the threshold p-value of
566 detection, was observed in LA1670 (* in [Table 3]). The x-axis represents markers'
567 chromosomal positions. The y-axis represents local score values. Maximal (red line) and
568 minimal (blue line) detection thresholds for *QTLs* with the local score approach are shown.

569 **B**: example of the *QTL 7-1-LA1670* for which variant calling based on reference genome
570 LA1246 (left panel) failed to detect an SNP in the exon of a *QTL* that was detected using the
571 LA1670 reference genome. SNPs (dots) are localized on the gene sequence whose structure
572 (exon in red, 3'UTR in blue) is shown at the top of each graph. Arrows indicate the exon on
573 which the *QTL* is detected for LA1670. The y-axis represents -log₁₀ (p-values). Random noise
574 was added to the x-axis for representation purposes for all p-values in the two conditions. *:

575 The most significant SNP. **C**: example of structural rearrangements between the LA1670
576 (upper panel) and LA1246 (lower panel) reference genomes with two indels (correspondence
577 of the genomic regions specified in dotted lines) that may have affected detection of the *QTL*
578 4-1-LA1246 counterparts in LA1670. Alignments of common genomic regions with good
579 accuracy are colored in light green while different genomic regions are in light blue.

580

**GWAS highlight temperature and time-dependent, biologically relevant**
**candidate genes**

583 *Different QTLs detect at 28°C and 32°C*

584 Comparison of GWAS results showed that no *QTL* was common to the two
585 temperatures. Among the 21 'strict candidate genes' (see Materials & Methods section for
586 details) corresponding to 18 *QTLs*, a similar number of *QTLs* was identified at both
587 temperatures: eight out of 18 at 28°C and ten out of 18 at 32°C. This suggests that the plant
588 response to GMI1000 is quantitative but regulated differently at the two temperatures.
590 *Detected QTLs vary over time following pathogen inoculation*

591 Regardless of temperature, peaks appeared and disappeared on Manhattan plots
592 depending on the phenotyping day [S11], suggesting that plants may use different strategies
593 as the infection progresses. Notably, when a *QTL* is present, it is detected on either one or,
594 more commonly, two consecutive days. Exceptions include *QTLs 7-2-BOTH* and *IG8-1-
595 LA1670*, observed over three days, and *QTL IG12-1-BOTH*, detected over four days.

596

597 *Biological relevancy of candidate genes underlying detected QTLs*

598 BLAST analyses were performed to find tomato homologs and *A.thaliana* orthologs
599 of candidate genes. Expression patterns were investigated when statistically relevant
600 homologs/orthologs were found by BLAST and when transcriptomic data were available. We
601 focus on “strict candidate genes” because they had a higher GWAS resolution enabling a better
602 identification of the causal gene. Twelve out of 19 tomato homologs were expressed in roots
603 in various cell compartments. Interestingly five genes (*Solyc01g006660*, *Solyc01g105270*,
604 *Solyc04g008310*, *Solyc09g009700* and *Solyc10g081260*) have already been annotated as
605 immunity-related genes and involved in response to other pathogens. Only eight orthologs of
606 *A.thaliana* were found with high BLAST confidence levels [Table 3], probably due to the
607 genetic distance between *S. pimpinellifolium* and *A. thaliana*. Five out of eight were
608 particularly expressed in roots and six out of eight showed different expression levels when
609 plants were subjected to a biotic stress. Although many candidate genes could not be analyzed
610 further due to a lack of available data in literature, the trend is towards candidate genes that
611 appear to be preferentially expressed in roots and on pathogen attack.

612

613 **Discussion**
To identify resistance to R. solanacearum that remains efficient at elevated temperature in tomato, we chose to study the pathosystem tomato/R. solanacearum at two contrasted temperatures: the one commonly used in controlled conditions (28°C) and also 32°C – this last temperature being chosen to mimic temperature elevation, and having been described as altering the defense response of tolerant tomato cultivars in the field (39). To this end, we implemented a GWAS approach using a selected panel of wild tomato parents composed mainly of S. pimpinellifolium accessions, newly sequenced on this occasion, and including two internal reference genomes.

The selection of the panel was based on the fact that tomato ancestors originated from a broad geographical range, spanning Central to South America, and likely evolved and adapted to diverse environments. Indeed, the Central American regions experience an equatorial climate, with elevated average temperatures and abundant rainfall, while the climate in the southern regions is more arid, with colder temperatures and lower precipitation (73). The presence of RSSC in this vast geographical region has been demonstrated by Wicker and colleagues, with phylotype II strains being specifically detected in Central and South America (2). As the main abiotic determinants of bacterial wilt development are temperature and moisture (38), and tomato defense response to bacterial wilt is known to be thermo-sensitive (32, 39), we hypothesized that tomato wild-relatives developed biome- and temperature-dependent defense mechanisms to RSSC. Our phenotyping results confirmed this assumption, with the accessions showing wide variability in their response to the R. solanacearum GMI 1000 reference strain, which was further accentuated by elevated temperature [Fig. 3]. This finding contrasts with previous results obtained in A. thaliana, showing little variation of plant response to the bacteria at 30°C, with nearly all accessions being susceptible ten days after inoculation. They also suggest tomato wild relatives are better adapted to elevated temperatures (20, 22). Interestingly, the evolution of the response between 28°C and 32°C
639 allowed the clustering of the accessions into three distinct groups, with a majority of 640 accessions becoming more susceptible at the elevated temperature. Another fraction is more 641 resistant, and the final group shows the same response independently of the temperature 642 applied. These results support the existence of diverse underlying genetic architectures of plant 643 responses to combined stress within the panel [Fig. 4].

644 The analysis of population structure [Fig. 1] confirmed the wide genetic diversity of 645 the panel, raising the question of the choice of an adequate reference genome for variant 646 calling. In tomato, the majority of GWAS papers have used the ‘Heinz’ cultivar as the 647 reference genome (74, 75). However, ‘Heinz’ is a cultivar that has experienced a reduction in 648 genetic diversity due to domestication (76). Indeed, immunity and plant development is a 649 trade-off that plants have to face with a limited quantity of energy. Some parts of the genome, 650 notably immunity genes, may have suffered a reduction compared to wild relatives (variant 651 copy number, indels, absence of genes etc.) (76). Consequently, when carrying out variant 652 calling, some genomic regions, potentially linked with immunity are either poorly or not 653 covered by SNPs. A natural alternative to the Heinz genome would be to use a pangenome as 654 a reference for variant calling. Indeed, pangenomes aim to capture as much structural variation 655 as possible within a species, making them an optimal choice for consistent variant calling (77). 656 However, constructing a pangenome requires large amounts of high-quality data and 657 significant bioinformatics resources (78). In this article, we propose an alternative strategy that 658 involves assembling two reference genomes selected within the panel, an approach that 659 remains relatively uncommon in GWAS (79).

660 Our proposal is first to select reference genomes that are relevant to the biological 661 question. In the present study the objective was to identify mechanisms that remain effective 662 in fluctuating environments, especially in the case of temperature elevation. Consequently, the 663 two reference genomes selected originated from contrasted climates [S6]. The choice was
also influenced by their geographical and genetic distance [Fig. 2]. As with a pangenome, the aim is to be able to call variants in all regions of the two reference genomes, and more particularly to be able to cover non-homologous regions. For GWAS, the reference genome chosen affects GWAS outputs by modifying peak height on Manhattan plots or even the presence or absence of certain peaks. In addition to confirming the relevance of *QTLs* detected in common using two reference genomes that represent the genetic structure of our panel, we identified at least three cases where our approach improves upon single-reference genome GWAS, and can thus be considered a promising alternative to pangenomic studies. First, we identified a unique case with *QTL 7-1-LA1670* [Fig. 5B]: this *QTL* was detected only in the LA1670 reference genome because SNP calling failed to provide variants in the homologous region of LA1246. Interestingly, BLAST analyses revealed that the gene underlying *QTL 7-1-LA1670* possesses multiple copies in both reference genomes, probably making this region very difficult to call. Second, in homologous regions, the statistical tests performed using the GWAS model (1) were affected by minor differences in both the kinship and population structure matrices. These variations led to changes in the p-values of markers within peaks, sometimes preventing them from reaching the significance thresholds [Fig. 5A] [Table 3]. Third, structural variations in non-homologous regions between the two reference genomes influenced *QTL* detection in two distinct ways. In a first case, a large indel present in one reference genome made variant calling in the corresponding region of the other genome impossible (*QTL IG8-1-LA1670*). We hypothesize that the SNPs in this area could be interpreted as presence-absence variants. Alternatively, structural variations modified local LD between markers, such as with an inversion (*QTL IG3-1-LA1246*) or a small indel (*QTL IG2-1-LA1670*). Since *QTL* detection relied on the local score method, which depends on local LD, these structural variations had an impact on the results.
Using this method, we identified time- and temperature-dependent *QTLs* [Table 3], with no common *QTLs* detected between the two temperature treatments, corroborating previous results observed in *A. thaliana* (22). The underlying QDR mechanisms induced under heat stress remain poorly understood. However, increasing evidence suggests that components of PTI and ETI are interconnected and characterized by different temporal induction dynamics (80). Studies on the *A. thaliana/P. syringae* pv. *tomato* DC3000 pathosystem has shown that a lower temperature favors ETI, while a higher temperature favors PTI (81). Moreover, the progression of the disease follows a temporal sequence in which the bacteria first invade the plant roots before colonizing the vascular system, with each stage involving different factors from both partners in the interaction (82). Our results support this observation, as a significant proportion of "strict candidate gene" orthologs were found to be expressed in the roots. This suggests that the defense mechanisms involved in our study may be dependent on time, temperature, and/or the specific plant organ. Given this complexity, we opted for a model using phenotypic ratings by Dai over a model that integrates temporal dynamics through measures, such as e.g. the ones used by Crispim and colleagues to decipher the genetic bases of beef cattle growth (83).

In the analyses of quantitative responses associated with bipartite plant-pathogen interactions, QDR genes identified were not only classical immunity genes: they have been described to be mostly associated with various pathways including detoxification, vesicle trafficking, epigenetic regulation, phosphorylation with kinase and associated receptors, phytohormone regulation and degradation of proteins and regulation of transcription (42,84). In our case, we decided to classify candidates by functional category. In addition, our analyses could allow, for the first time, definition of the nature of the genes underlying the *bwr-6* and *bwr-12* *QTLs*, widely described over the last three decades as major *QTLs* conferring the QDR to *R. solanacearum* in tomato.
**The first functional category** corresponds to five *QTLs* for which the underlying homologs/orthologs, found either in the tomato 'Heinz 1706' cultivar or *A. thaliana*, have already been annotated as immunity genes in other pathosystems. Therefore, it suggests their involvement in broad-spectrum defense responses. Indeed, for *QTL 10-1-LA1246*, the orthologous underlying gene was found to have modulated expression in *A.thaliana* under attack by the cabbage leaf curl virus (CaLCuV) (85). According to TAIR, it is involved in « xenobiotic detoxification by transmembrane export across the plasma membrane». Microarray data obtained from *A. thaliana* challenged by the *Pseudomonas syringae* pv tomato DC3000 strain led to the identification of the ortholog of *QTL 4-1-LA1246* (UDP-glucuronosyl/UDP-glucosyltransferase) (86). Interestingly the GDSL esterase/lipase (*QTL 9-1-BOTH*) ortholog of *A.thaliana* is known to be down-regulated by abscisic acid treatment, leading to lower resistance towards the pathogen *Pseudomonas syringae* pv. tomato strain 1065 (87). *QTL 1-4-BOTH* corresponding to the *Solyc01g105270* 'Heinz 1706' cultivar ortholog coding for SlTOM1b (*S.lycopersicum* TOBAMOMIVUS MULTIPLICATION PROTEIN 1b), was annotated as an immunity gene involved in tobamovirus defense responses (88). The most significant SNP falls inside an exon of this gene. Translation of the CDS into protein reveals that one allele codes for a Methionine (M) whereas the other codes for an Isoleucine (I). Interestingly, M-I are non-synonymous substitutions that have already been shown to affect protein function (89, 90). Moreover, since some TOM (TOBAMOVIRUS MULTIPLICATION) proteins have transmembrane domains (91), different alleles of the SlTOM1b gene may play a role in protein stability inside the plasma membrane. Finally, the *Solyc12g008960* ortholog underlying *QTL IG12-1-BOTH* was identified in a transcriptome analysis of tomato leaves infected by thrips transmitting the tomato spotted wilt orthotspovirus (TSWV) (92).
**The second functional category** is constituted of calcium-related candidate genes underlying *QTLs* 3-1-BOTH and *IG12-1-BOTH*. Calcium is a universal second messenger contributing to either multiple pathogen or temperature-dependent signaling. However, information on calcium concentration variation during combined heat/pathogen stress is lacking (93). The *Solyc03g123790* gene which encodes a putative calcium/proton exchanger (*QTL-3-1-BOTH*) could participate in such variation of the Ca²⁺ flux and is thus an interesting candidate gene. Variation of Ca²⁺ concentrations are then relayed by calcium sensors, such as Calmodulin (CaM), Calmodulin-like (CML), Calmodulin-binding (CBP) proteins and Calcium-dependent protein kinases (CDPK) leading to the deployment of stimuli-specific responses (93). One of these sensors could be the CBP/IQM1 encoded by the *Solyc12g008960* gene (*QTL IG12-1-BOTH*). Many pieces of evidence make good arguments for the pertinence of the candidate: the first is statistical and has been previously discussed: this gene was among the most differentially expressed genes found in response to a 72-hour-post-thrips inoculation of TSWV (92). Based on several BLAST analyses using genes located within the *bwr-12* *QTL* (94), the location of *QTL IG12-1-BOTH* was found to be approximately 600kbp upstream of the supposed location of *bwr-12* on our reference genomes. Another argument in favor of this discovery is that *bwr-12* is specific to phylotype I strains to which GMI1000 belongs. Furthermore, the *bwr-12* *QTL* was found to be thermosensitive, with a reduction of the *bwr-12* effect during heat stress (32). However, the temperature was not precisely regulated because experiments were carried out in greenhouses (temperature variations between 32 and 38°C) with a mean temperature of up to 36.5°C (32). Thus, the heat stress was higher than the one applied in our experiments. As for molecular arguments, IQM1 is known to bind to Catalase 2 (*At4g35090*) (95) in *A. thaliana*, and a similar mechanism could have been pinpointed in this study since two catalases were identified within *QTL 1-3-LA1246*. Despite
761 the orthology between Solyc01g100630 and Solyc01g100640 in A.thaliana not having been
762 clearly identified [Table 3], Catalase 2 had the highest BLAST score.

763 **The third functional category** of candidate genes is constituted of receptor kinases.
764 Indeed, to trigger ETI, plants have to recognize effectors that are molecules secreted by
765 pathogens and they use many receptor-like kinases located on the plasma membrane. The
766 recognition mechanism is based on the binding of effectors, which trigger their
767 phosphorylation leading to a transduction signal in plant cells involving numerous other
768 pathways forming ETI (42). Genes underlying QTL 4-2-BOTH and QTL IG6-1-BOTH
769 correspond to receptor kinases. Using previously obtained fine mapping results (96); the
770 position of IG6-1-BOTH was likely to be inside QTL bwr-6. Interestingly, bwr-6 confers
771 resistance to both phylotype I & II strains (32).

772 **The fourth class** of identified candidate genes is made up of genes belonging to gene
773 families known to be involved in resistance against some pathogens. However, for this class,
774 neither tomato nor *A.thaliana* orthologs have been described as taking part in responses to
775 biotic stress. Sumo activating enzyme 1b Molybdenum cofactor biosynthesis (*QTL 3-4-LA1670*) is involved in the ubiquitination process (97), leading to protein degradation. The
776 *QTL 1-1-LA1670* gene codes for a Subtilisin-like serine protease, a protein family that plays a
777 role in pathogen recognition and immune priming (98). Finally, some glutathione
778 S-transferases have been demonstrated to confer resistance to diseases, such as Fusarium head
779 blight, using their detoxification ability (99). This could be also the case in our study for the
780 gene underlying *QTL 9-2-BOTH*.

782 **The final class** contains all remaining genes. Sometimes no information about them is
783 available or their link with immunity is not straightforward. However, previously cited reviews
784 on QDR have shown that QDR involves various pathways, some of which have probably not
785 yet been discovered (42, 84); this is all the more possible since each specific combination of
786 stresses leads to a specific plant response (37).

787 Overall, by examining the natural diversity of responses to two combined stresses
788 within a specific collection of wild tomato species accessions, our study confirms the
789 significant impact of elevated temperatures on the defense mechanisms activated against R.
790 solanacearum. This panel proves to be a valuable resource for identifying new thermostable
791 resistance mechanisms. The development and characterization of a unique genomic resource,
792 along with its use with two internal reference genomes in GWAS, have enabled the
793 identification of several promising candidate genes, both common and specific to the two
794 reference genomes. The next phase will involve functionally validating the genes involved in
795 responses to combined stresses and evaluating their protective spectrum, as they could
796 potentially represent a robust source of resistance based on their annotations.

797

798 **Data Availability Statement**

799 The two reference genomes sequenced and annotated in this study have been deposited
800 in the GenBank database under the accession numbers JASTWH000000000 (LA1246) and
801 JASTWI000000000 (LA1670). The initial polymorphism matrices, derived from panel
802 sequencing and SNP calling based on LA1246 and LA1670 reference genomes, are available
803 *via* the following DOIs: https://dx.doi.org/10.25794/published_result/mk3v99ws (based on
804 LA1246) and https://dx.doi.org/10.25794/published_result/bh8cvjct (based on LA1670). All
805 phentotypic and genotypic as well as scripts developed and used in this study were archived in
806 a publicly accessible platform at
807 https://forgemia.inra.fr/adrien.belny1/gwas-belny_et_al_2025/.

808

809 **Author Contributions**
810 R.B. conceptualized the project. R.B. and F.V. acquired funding for its achievement.

811 R.B., T.M.H supervised the project. G.B. and C.L. provide wild tomatoes accessions collection

812 and enabled its sequencing and SNP calling. S.C. and J. G. analyzed sequence data, assembled,

813 annotated and build a genome browser for the two reference genomes. H.D. and R.B. designed

814 experiments. M.R. and H.D set-up and performed phenotyping experiments. H.D. analyzed

815 the phenotypic traits. A.B., H.D. performed structure and LD analyses. A.B., H.D. created

816 models, performed the GWA mapping and the genome-wide local score analysis. F.R.,

817 T.H.M., A.E., L.G. and R.B. verified experiments and analyses outputs. A.B, H.D. and R.B.

818 wrote the initial draft of the manuscript. All authors contributed to critical discussions and the

819 revisions.

820

821 Acknowledgments

822 This project was funded in the framework of the INRAE / SYNGENTA

823 BURNED/Rethink bilateral research project and subsequent addendum (N°15000441). We

824 acknowledge SYNGENTA for funding the PhDs of H. Desaint and A. Belny and for technical

825 support; Ludovic Legrand for his help with bioinformatic tools; and Caitlin Griffiths and

826 Rebecca Stevens for reading and correcting the English usage.

827

828 References

829 1. Caldwell D, Kim BS, Iyer-Pascuzzi AS. Ralstonia solanacearum Differentially Colonizes Roots of Resistant and Susceptible Tomato Plants. Phytopathology. 2017 May;107:528–36.

830 2. Wicker E, Lefeuvre P, de Cambiaire JC, Lemaire C, Poussier S, Prior P. Contrasting recombination patterns and demographic histories of the plant pathogen Ralstonia solanacearum inferred from MLSA. The ISME Journal. 2012 May;6(5):961–74.

831 3. Elphinstone, J.G. The current bacterial wilt situation: a global overview. In Allen C., Piror P. and Hayward A. C. (Eds). Bacterial wilt disease and the Ralstonia solanacearum complex. APS press, Minnesota USA. 2005 : 9-28.
838 4. Ahmed W, Yang J, Tan Y, Munir S, Liu Q, Zhang J, et al. Ralstonia solanacearum, a deadly pathogen: Revisiting the bacterial wilt biocontrol practices in tobacco and other Solanaceae. Rhizosphere. 2022 Mar 1;21:100479.

841 5. Elsayed TR, Jacquiod S, Nour EH, Sørensen SJ, Smalla K. Biocontrol of Bacterial Wilt Disease Through Complex Interaction Between Tomato Plant, Antagonists, the Indigenous Rhizosphere Microbiota, and Ralstonia solanacearum. Frontiers in Microbiology 2020 Jan 10:2835.

845 6. Mohammed AF, Oloyede AR, Odeseye AO. Biological control of bacterial wilt of tomato caused by Ralstonia solanacearum using Pseudomonas species isolated from the rhizosphere of tomato plants. Archives of Phytopathology and Plant Protection. 2020 Jan 2;53(1-2):1-16.

849 7. Niu J, Chao J, Xiao Y, Chen W, Zhang C, Liu X, et al. Insight into the effects of different cropping systems on soil bacterial community and tobacco bacterial wilt rate. Journal of Basic Microbiology. 2017 Jan;57(1):3-11.

852 8. Huet G. Breeding for resistances to Ralstonia solanacearum. Frontiers in Plant Science 2014 Dec 12:00715

854 9. Vailleau F, Genin S. Ralstonia solanacearum: An Arsenal of Virulence Strategies and Prospects for Resistance. Annual Review of Phytopathology. 2023 Sep 5;61(1):25-47.

856 10. Jones JDG, Dangl JL. The plant immune system. Nature. 2006 Nov;444(7117):323-9.

857 11. Ramírez-Zavaleta CY, García-Barrera LJ, Rodríguez-Verástegui LL, Arrieta-Flores D, Gregorio-Jorge J. An Overview of PRR- and NLR-Mediated Immunities: Conserved Signaling Components across the Plant Kingdom That Communicate Both Pathways. International Journal of Molecular Sciences. 2022 Oct 26;23(21):12974.

861 12. Roux F, Voisin D, Badet T, Balague C, Barlet X, Huard-Chauveau C, et al. Resistance to phytopathogens e tutti quanti: placing plant quantitative disease resistance on the map. Molecular Plant Pathology. 2014 Jun;15(5):427-32.

864 13. Bartoli C, Roux F. Genome-Wide Association Studies In Plant Pathosystems: Toward an Ecological Genomics Approach. Frontiers in Plant Science. 2017 May 23;8:763.

866 14. Deslandes L, Pileur F, Liaubet L, Camut S, Can C, Williams K, et al. Genetic Characterization of RRS1, a Recessive Locus in Arabidopsis thaliana that Confers Resistance to the Bacterial Soilborne Pathogen Ralstonia solanacearum. Molecular Plant Microbe Interactions. 1998 Jul;11(7):659-67.

870 15. Le Roux C, Huet G, Jauneau A, Camborde L, Trémousaygue D, Kraut A, et al. A Receptor Pair with an Integrated Decoy Converts Pathogen Disabling of Transcription Factors to Immunity. Cell. 2015 May;161(5):1074-88.

873 16. Sarris PF, Duxbury Z, Huh SU, Ma Y, Segonzac C, Sklenar J, et al. A Plant Immune Receptor Detects Pathogen Effectors that Target WRKY Transcription Factors. Cell. 2015 May;161(5):1089-100.

876 17. Van Der Linden L, Bredenkamp J, Naidoo S, Fouché-Weich J, Denby KJ, Genin S, et al. Gene-for-Gene Tolerance to Bacterial Wilt in Arabidopsis. Molecular Plant Microbe Interactions. 2013 Apr;26(4):398-406.

879 18. Narusaka M, Shirasu K, Noutoshi Y, Kubo Y, Shiraishi T, Iwabuchi M, et al. RRS1 and RPS4 provide a dual Resistance- gene system against fungal and bacterial pathogens. The Plant Journal. 2009 Oct;60(2):218-26.

882 19. Debieu M, Huard-Chauveau C, Genissel A, Roux F, Roby D. Quantitative disease resistance to the bacterial pathogen Xanthomonas campestris involves an Arabidopsis immune receptor pair and a gene of unknown function. Molecular Plant Pathology. 2016 May;17(4):510-20.
886 20. Aoun N, Tauleigne L, Lonjon F, Deslandes L, Vailleau F, Roux F, et al. Quantitative Disease Resistance under Elevated Temperature: Genetic Basis of New Resistance Mechanisms to Ralstonia solanacearum. Frontiers in Plant Science. 2017 Aug;8:1387.

889 21. Peeters N, Guidot A, Vailleau F, Valls M. R alstonia solanacearum, a widespread bacterial plant pathogen in the post-genomic era. Molecular Plant Pathology. 2013 Sep;14(7):651-62.

892 22. Aoun N, Desaint H, Boyrie L, Bonhomme M, Deslandes L, Berthomé R, et al. A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress. Molecular Plant Pathology. 2020 Nov;21(11):1405-20.

897 23. Demirjian C, Razavi N, Yu G, Mayjonade B, Zhang L, Lonjon F et al. An atypical NLR gene confers bacterial wilt susceptibility in Arabidopsis. Plant Communications. 2023 Sep 11;4 : 100607.

900 24. Ailloud F, Lowe T, Cellier G, Roche D, Allen C, Prior P. Comparative genomic analysis of Ralstonia solanacearum reveals candidate genes for host specificity. BMC Genomics. 2015 Dec;16(1):270.

903 25. Nuez F, Díez MJ. Solanum lycopersicum var. lycopersicum (Tomato). In Maloy S, Hughes K (Eds). Brenner's Encyclopedia of Genetics (Second Edition). Academic Press. 2013 Jul 13:476-80.

906 26. Ishihara T, Mitsuhara I, Takahashi H, Nakaho K. Transcriptome Analysis of Quantitative Resistance-Specific Response upon Ralstonia solanacearum Infection in Tomato. PLOS ONE. 2012 Oct 5;7(10):e46763.

909 27. Cui J, Xu P, Meng J, Li J, Jiang N, Luan Y. Transcriptome signatures of tomato leaf induced by Phytophthora infestans and functional identification of transcription factor SpWRKY3. Theor etical and Applied Genetics. 2018 Apr 1;131(4):787-800.

912 28. Sullenberger MT, Jia M, Gao S, Ashrafi H, Foolad MR. Identification of late blight resistance quantitative trait loci in Solanum pimpinellifolium accession PI 270441. The Plant Genome. 2022 Dec;15(4):e20251.

915 29. Sarfatti M, Katan J, Fluhr R, Zamir D. An RFLP marker in tomato linked to the Fusarium oxysporum resistance gene I2. Theoretical and Applied Genetics. 1989 Nov 1;78(5):755-9.

918 30. Yang H, Wang H, Jiang J, Liu M, Liu Z, Tan Y et al. The Sm gene conferring resistance to gray leaf spot disease encodes an NBS-LRR (nucleotide-binding site-leucine-rich repeat) plant resistance protein in tomato. Theoretical and Applied Genetics. 2022 May;135(5):1467-1476.

922 31. Riely BK, Martin GB. Ancient origin of pathogen recognition specificity conferred by the tomato disease resistance gene Pto. Proceedings of the National Academy of Sciences. 2001 Feb 13;98(4):2059-64.

925 32. Kunwar S, Hsu Y, Lu S, Wang J, Jones JB, Hutton S, et al. Characterization 3333 of tomato (Solanum lycopersicum) accessions for resistance to phylotype I and phylotype II strains of the Ralstonia solanacearum species complex under high temperatures. In Flachowsky H (Eds). Plant Breeding. 2020 Apr;139(2):389-401.

929 33. Wang Y, Zhang Y, Gao Z, Yang W. Breeding for Resistance to Tomato Bacterial Diseases in China: Challenges and Prospects. Horticultural Plant Journal. 2018 Sep;4(5):193-207.

932 34. Shin IS, Hsu JC, Huang SM, Chen JR, Wang JF, Hanson P, et al. Construction of a single nucleotide polymorphism marker based QTL map and validation of resistance loci to bacterial wilt caused by Ralstonia solanacearum species complex in tomato. Euphytica. 2020 Mar 6;216(3):54.
936 35. Zarattini M, Farjad M, Launay A, Cannella D, Soulié MC, Bernacchia G, et al. Every
937 cloud has a silver lining: how abiotic stresses affect gene expression in plant-pathogen
938 interactions. Noctor G, editor. Journal of Experimental Botany. 2021 Feb
939 24;72(4):1020-33.

940 36. Pascual LS, Segarra-Medina C, Gómez-Cadenas A, López-Climent MF, Vives-Peris
941 V, Zandalinas SI. Climate change-associated multifactorial stress combination: A
942 present challenge for our ecosystems. Journal of Plant Physiology. 2022
943 Sep;276:153764.

944 37. Desaint H, Aoun N, Deslandes L, Vailleau F, Roux F, Berthomé R. Fight hard or die
945 trying: when plants face pathogens under heat stress. New Phytologist.
946 2021;229(2):712-34.

947 38. Hayward AC. Biology and Epidemiology of Bacterial Wilt Caused by Pseudomonas
948 Solanacearum. Annual Review of Phytopathology. 1991 Sep;29(1):65-87.

949 39. Prior P, Bart S, Leclercq S, Darrasse A, Anais G. Resistance to bacterial wilt in tomato
950 as discerned by spread of Pseudomonas (Burholderia) solanacearum in the stem tissues.
951 Plant Pathology. 1996 Aug;45(4):720-6.

952 40. Mew TW, Ho WC. Effect of soil temperature on resistance of tomato cultivars to
953 bacterial wilt. Phytopathology. 1977 Jan;67:909-11.

954 41. French E, Kim BS, Iyer-Pascuzzi AS. Mechanisms of quantitative disease resistance
955 in plants. Seminars in Cell & Developmental Biology. 2016 Aug 1;56:201-8.

956 42. Gou M, Balint-Kurti P, Xu M, Yang Q. Quantitative disease resistance: Multifaceted
957 players in plant defense. Journal of Integrative Plant Biology. 2023 Feb;65(2):594-
958 610.

959 43. Huerta AI, Milling A, Allen C. Tropical Strains of Ralstonia solanacearum
960 Outcompete Race 3 Biovar 2 Strains at Lowland Tropical Temperatures. Applied and
961 Environmental Microbiology. 2015 May 15;81(10):3542-51.

962 44. Monnot S, Desaint H, Mary-Huard T, Moreau L, Schurdi-Levraud V, Boissot N.
963 Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of
964 the Art and Potential Advances. Cells. 2021 Nov;10(11):3080.

965 45. Hong JC, Norman DJ, Reed DL, Momol MT, Jones JB. Diversity Among Ralstonia
966 solanacearum Strains Isolated from the Southeastern United States. Phytopathology.
967 2012 Oct;102(10):924-36.

968 46. Boucher CA, Barberis PA, Trigalet APH, Demery DA. Transposon Mutagenesis of
969 Pseudomonas solanacearum: Isolation of Tn5-Induced Avirulent Mutants.
970 Microbiology. 1985 Sep 1;131(09):2449-57.

971 47. Salanoubat M, Genin S, Artiguenave F, Gouzy J, Mangenot S, Arlat M, et al. Genome
972 sequence of the plant pathogen Ralstonia solanacearum. Nature. 2002
973 Jan;415(6871):497-502.

974 48. Plener L, Manfredi P, Valls M, Genin S. PrhG, a transcriptional regulator responding
975 to growth conditions, is involved in the control of the type III secretion system regulon
976 in Ralstonia solanacearum. Journal of Bacteriology. 2010 Feb;192(4):1011-9.

977 49. Morel A, Peeters N, Vailleau F, Barberis P, Jiang G, Berthomé R, et al. Plant
978 Pathogenicity Phenotyping of Ralstonia solanacearum Strains. Methods in Molecular
979 Biology. 2018;1734:223-39.

980 50. Blanca J, Montero-Pau J, Sauvage C, Bauchet G, Illa E, Díez MJ, et al. Genomic
981 variation in tomato, from wild ancestors to contemporary breeding accessions. BMC
982 Genomics. 2015 Dec;16(1):257.

983 51. Raymond O, Gouzy J, Just J, Badouin H, Verdenaud M, Lemainque A, et al. The Rosa
984 genome provides new insights into the domestication of modern roses. Nature Genetics.
985 2018 Jun;50(6):772-7.
986
987
988

52. Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Research. 2017 Jan 5;27(5):722-36.

989
990
991

53. Sallet E, Gouzy J, Schiex T. EuGene: An Automated Integrative Gene Finder for Eukaryotes and Prokaryotes. In Kollmar, M. (eds) Gene Prediction. Methods in Molecular Biology, Humana, New York. 2019 Apr ; 1962 :97-120.

992
993

54. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv-Genomics. 2012 Jul 17 :1207.3907v2.

994
995
996

55. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics. 2007 Sep;81(3):559-75.

997
998
999

56. Aulchenko YS, de Koning DJ, Haley C. Genomewide Rapid Association Using Mixed Model and Regression: A Fast and Simple Method For Genomewide Pedigree-Based Quantitative Trait Loci Association Analysis. Genetics. 2007 Sep 1;177(1):577-85.

1000
1001
1002

57. Balding DJ, Nichols RA. A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identity and paternity. Genetica. 1995 Jun 1;96(1):3-12.

1003
1004
1005

58. Zheng X, Gogarten SM, Lawrence M, Stilp A, Conomos MP, Weir BS, et al. SeqArray—a storage-efficient high-performance data format for WGS variant calls. Bioinformatics. 2017 Aug 1;33(15):2251-7.

1006
1007

59. Weir BS, Cockerham CC. Estimating F-Statistics for the Analysis of Population Structure. Evolution. 1984 Nov;38(6):1358-70.

1008
1009
1010

60. Mangin B, Siberchicot A, Nicolas S, Doligez A, This P, Cierco-Ayrolles C. Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity. 2012 Mar;108(3):285-91.

1011
1012
1013

61. Desrousseaux D, Sandron F, Siberchicot A, Cierco-Ayrolles C, Mangin B. LDcorSV: Linkage Disequilibrium Corrected by the Structure and the Relatedness. 2020 Aug 26; https://CRAN.R-project.org/package=LDcorSV.

1014
1015
1016

62. Wang JF, Ho FI, Truong HTH, Huang SM, Balatero CH, Dittapongpitch V, et al. Identification of major QTLs associated with stable resistance of tomato cultivar 'Hawaii 7996' to Ralstonia solanacearum. Euphytica. 2013 Apr 1;190(2):241-52.

1017
1018

63. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015 Oct 7;67:1-48.

1019
1020

64. Gallais A. Théorie de la sélection en amélioration des plantes. Elsevier Masson (Eds) Sciences Agronomiques; 1989 Nov : 1-588.

1021
1022

65. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software. 2017 Dec 6;82:1-26.

1023
1024
1025

66. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics. 2006 Feb;38(2):203-8.

1026
1027
1028

67. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S yee, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nature Genetics. 2010 Apr;42(4):348-54.

1029
1030

68. Endelman JB. Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP. The Plant Genome. 2011 Nov;4(3):250-5.

1031
1032
1033
1034

69. Bonhomme M, Fariello MI, Navier H, Hajri A, Badis Y, Miteul H, et al. A local score approach improves GWAS resolution and detects minor QTL: application to Medicago truncatula quantitative disease resistance to multiple Aphanomyces euteiches isolates. Heredity. 2019 Oct;123(4):517-31.
1035 70. Corpet F. Multiple sequence alignment with hierarchical clustering. Nucleic Acids Research. 1988 Nov 25;16(22):10881-90.

1037 71. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative Genomics Viewer. Nature Biotechnology. 2011 Jan;29(1):24-6.

1039 72. Zepner L, Karrasch P, Wiemann F, Bernard L. ClimateCharts.net - an interactive climate analysis web platform. International Journal of Digital Earth. 2021 Mar 4;14(3):338-56.

1042 73. Zuriaga E, Blanca JM, Cordero L, Sifres A, Blas-Cerdán WG, Morales R, et al. Genetic and bioclimatic variation in Solanum pimpinellifolium. Genetic Resources and Crop Evolution. 2009 Feb 1;56(1):39-51.

1045 74. Albert E, Segura V, Gricourt J, Bonnefoi J, Derivot L, Causse M. Association mapping reveals the genetic architecture of tomato response to water deficit: focus on major fruit quality traits. Journal of Experimental Botany. 2016 Dec 1;67(22):6413-30.

1048 75. Gai W, Yang F, Yuan L, ul Haq S, Wang Y, Wang Y, et al. Multiple-model GWAS identifies optimal allelic combinations of quantitative trait loci for malic acid in tomato. Horticulture Research. 2023 Apr 4;10(4): uhad021.

1051 76. Su X, Wang B, Geng X, Du Y, Yang Q, Liang B, et al. A high-continuity and annotated tomato reference genome. BMC Genomics. 2021 Dec 15;22:898.

1053 77. Manuweera B, Mudge J, Kahanda I, Mumey B, Ramaraj T, Cleary A. Pangenome-Wide Association Studies with Frequented Regions. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2019 Sep :627-32.

1057 78. Zhou Y, Zhang Z, Bao Z, Li H, Lyu Y, Zan Y, et al. Graph pangenome captures missing heritability and empowers tomato breeding. Nature. 2022 Jun 16;606(7914):527-34.

1060 79. Gage JL, Vaillancourt B, Hamilton JP, Manrique-Carpintero NC, Gustafson TJ, Barry K, et al. Multiple Maize Reference Genomes Impact the Identification of Variants by Genome-Wide Association Study in a Diverse Inbred Panel. The Plant Genome. 2019;12(2):180069.

1064 80. Delplace F, Huard-Chauveau C, Berthomé R, Roby D. Network organization of the plant immune system: from pathogen perception to robust defense induction. The Plant Journal. 2022 Jan;109(2):447-70.

1067 81. Cheng C, Gao X, Feng B, Sheen J, Shan L, He P. Plant immune response to pathogens differs with changing temperatures. Nature Communtions. 2013 Sep 26;4(1):2530.

1069 82. De Pedro-Jové R, Puigvert M, Sebastià P, Macho AP, Monteiro JS, Coll NS, et al. Dynamic expression of Ralstonia solanacearum virulence factors and metabolism-controlling genes during plant infection. BMC Genomics. 2021 Mar 9;22(1):170.

1072 83. Crispim AC, Kelly MJ, Guimarães SEF, e Silva FF, Fortes MRS, Wenceslau RR, et al. Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle. PLoS ONE. 2015 Oct 7; 10(10): e0139906.

1075 84. Demirjian C, Vailleau F, Berthomé R, Roux F. Genome-wide association studies in plant pathosystems: success or failure? Trends in Plant Science. 2023 Apr 1;28(4):471-85.

1078 85. Ascencio-Ibáñez JT, Sozzani R, Lee TJ, Chu TM, Wolfinger RD, Cella R, et al. Global Analysis of Arabidopsis Gene Expression Uncovers a Complex Array of Changes Impacting Pathogen Response and Cell Cycle during Geminivirus Infection. Plant Physiology. 2008 Sep 1;148(1):436-54.

1082 86. Langlois-Meurinne M, Gachon CMM, Saindrenan P. Pathogen-Responsive Expression of Glycosyltransferase Genes UGT73B3 and UGT73B5 Is Necessary for
1084 Resistance to Pseudomonas syringae pv tomato in Arabidopsis. Plant Physiology. 2005

1085 Dec 1;139(4):1890-901.

1086 87. Mohr PG, Cahill DM. Suppression by ABA of salicylic acid and lignin accumulation

1087 and the expression of multiple genes, in Arabidopsis infected with Pseudomonas syringae pv. tomato. Functional & Integrative Genomics. 2007 Jul 1;7(3):181-91.

1089 88. Ishikawa M, Yoshida T, Matsuyama M, Kouzai Y, Kano A, Ishibashi K. Tomato

1090 brown rugose fruit virus resistance generated by quadruple knockout of homologs of TOBAMOVIRUS MULTIPLICATION1 in tomato. Plant Physiology. 2022 Jun 1;189(2):679-86.

1093 89. Cama A, Quon MJ, de la Luz Sierra M, Taylor SI. Substitution of isoleucine for

1094 methionine at position 1153 in the beta-subunit of the human insulin receptor. A mutation that impairs receptor tyrosine kinase activity, receptor endocytosis, and insulin action. Journal of Biological Chemistry. 1992 Apr 25;267(12):8383-9.

1097 90. Pérez L, Kerrigan JE, Li X, Fan H. Substitution of methionine 435 with leucine,

1098 isoleucine, and serine in tumor necrosis factor alpha converting enzyme inactivates 1099 ectodomain shedding activity. Biochemistry and Cell Biology. 2007 Feb 22;85(1):141-9.

1101 91. Tsujimoto Y, Numaga T, Ohshima K, Yano M aki, Ohsawa R, Goto DB, et al.

1102 Arabidopsis TOBAMOVIRUS MULTIPLICATION (TOM) 2 locus encodes a 1103 transmembrane protein that interacts with TOM1. The EMBO Journal. 2003 Jan 1;22(2):335.

1105 92. Ordaz NA, Nagalakshmi U, Boiteux LS, Atamian HS, Ullman DE, Dinesh-Kumar SP.

1106 The Sw-5b NLR Immune Receptor Induces Early Transcriptional Changes in 1107 Response to Thrips and Mechanical Modes of Inoculation of Tomato spotted wilt 1108 orthotsospovirus. MPMI. 2023 Nov 27;36(11):705-15.

1109 93. Carpentier S, Aldon D, Berthomé R, Galaud JP. Is there a specific calcium signal out

1110 there to decode combined biotic stress and temperature elevation? Frontiers in Plant 1111 Science. 2022 Nov 3; 13 : 1004406.

1112 94. Kim B, Hwang IS, Lee HJ, Lee JM, Seo E, Choi D, et al. Identification of a molecular

1113 marker tightly linked to bacterial wilt resistance in tomato by genome-wide SNP 1114 analysis. Theoretical and Applied Genetics. 2018 Jan 19;131(5):1017-30.

1115 95. Lv T, Li X, Fan T, Luo H, Xie C, Zhou Y, et al. The Calmodulin-Binding Protein

1116 IQM1 Interacts with CATALASE2 to Affect Pathogen Defense1. Plant Physiology. 1117 2019 Nov;181(3):1314-27.

1118 96. Kim B, Hwang IS, Lee HJ, Lee JM, Seo E, Choi D, et al. Identification of a molecular

1119 marker tightly linked to bacterial wilt resistance in tomato by genome-wide SNP 1120 analysis. Theoretical and Applied Genetics. 2018 May 1;131(5):1017-30.

1121 97. Lake MW, Wuebbens MM, Rajagopalan KV, Schindelin H. Mechanism of ubiquitin

1122 activation revealed by the structure of a bacterial MoeB-MoaD complex. Nature. 2001 1123 Nov 15;414(6861):325-9.

1124 98. Figueiredo A, Monteiro F, Sebastiana M. Subtilisin-like proteases in plant-pathogen

1125 recognition and immune priming: a perspective. Frontiers in Plant Science. 2014 Dec 1126 19;5:739.

1127 99. Wang H, Sun S, Ge W, Zhao L, Hou B, Wang K, et al. Horizontal gene transfer of

1128 Fhb7 from fungus underlies Fusarium head blight resistance in wheat. Science. 2020 1129 May 22; 368(6493) : eaba5435.

1130

1131 Supporting information captions

1132 S1: Information on panel composition.
1133 **S2:** Sequencing data for reference genomes and the mean coverage by SNPs for all accessions of the panel with raw data after SNP calling using the two reference genomes.

1135 **S3:** Supplementary information on sequencing methodology and assembly.

1136 **S4:** Part of variance explained by PCA axes for both reference genomes. **A:** LA1246. **B:** LA1670. A slight decrease is observed when the number of axes is above three. **S5:** Heritability ($h^2$) calculated per Dai for experiments performed at 28°C and 32°C.

1139 **S5:** Heritability ($h^2$) calculated per Dai for experiments performed at 28°C and 32°C. Blue bars indicate heritability for the 28°C experiment and orange bars for the 32°C one.

1141 **S6:** Average climate data recorded monthly between 1990 and 2019, relative to the GPS coordinates of LA1246 (**A:** Loja; Ecuador; 3,99S 79,36W; elevation 1259m, Köppen climate classification: Cfb) and LA1670 (**B:** Sama Grande; Tacna; Peru; 17,833S 70,51W; elevation 452m; Köppen climate classification: Bwh). The red line corresponds to the average temperature recorded while the average amount of precipitation is represented by blue histograms. [https://climatecharts.net/] [Zepner et al., 2021].

1147 **S7:** Fixation index (F ST) within identified clusters and population structure of the panel analysed based on the LA1670 reference genome. **A:** F ST was computed using vcftools (using a window size of 10kb on the GWAS SNP matrix). Then the weighted means (by the number of markers within these windows) was computed between all combinations of the two clusters.

1151 **B:** Kinship matrix and representation of population structure: individuals were ordered following their clustering based on PCA axes to let blocks corresponding to structure appear.

1153 Results are consistent between the two figures: high kinship coefficients between individuals of two different clusters lead to small FST values, indicating clusters have similar genetic diversity (for example clusters 4-5).
1156 **S8:** Evolution of linkage disequilibrium (LD; *r*²) along chromosomes for both reference genomes. **A:** LA1246; **B:** LA1670. X-axis: positions on chromosomes are indicated in base pairs. Order of magnitude of half-LD is a few kb. Except for chromosomes 5, 6 and 8.

1159 **S9:** Half-decay in base pairs for all chromosomes for both reference genomes.

1160 **S10:** LD (*r*²) between pairs of SNPs. For chromosome 1 (**A**-**B**) and 8 (**C**-**D**). Raw LD is represented on the left (**A**-**B**) and LD corrected with kinship on the right (**B**-**D**). Small blocks of SNPs in LD appear on chromosome 1. However, large blocks appear on chromosome 8, which is consistent with previous results on the LD half-decay.

1164 **S11:** Manhattan plots, their related local score and QQ-plots. For each panel, data on the left are for the LA1246 reference genome while data on the right are for LA1670. Manhattan plots with local score (ξ = 3) are shown at the top of each panel. Red and blue lines represent the maximal and the minimal significance threshold computed per chromosome with autocorrelation between SNPs. In the middle of each panel are the Manhattan plots (with p-values; the dotted line represents the Bonferroni threshold) and the corresponding QQ plots (with p-values) are at the bottom of the figure. Each panel corresponds to a temperature condition and a Dai: **A:** 28°C-Dai7; **B:** 28°C-Dai10; **C:** 32°C-Dai4; **D:** 32°C-Dai5; **E:** 32°C-Dai6; **F:** 32°C-Dai7; **G:** 32°C-Dai10.