Association mapping for broomrape resistance in sunflower

Álvaro Calderón-González, Begoña Pérez-Vich, Nicolas Pouilly, Marie-Claude

Boniface, Johann Louarn, Leonardo Velasco, Stephane S. Munos

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Álvaro Calderón-González, Begoña Pérez-Vich, Nicolas Pouilly, Marie-Claude Boniface, Johann Louarn, et al.. Association mapping for broomrape resistance in sunflower. Frontiers in Plant Science, 2023, 13 (1056231), 20 p. 10.3389/fpls.2022.1056231. hal-04013004

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EDITED BY
Atsushi Okazawa,
Osaka Metropolitan University, Japan

REVIEWED BY
Stephan Reinert,
University of Erlangen Nuremberg,
Germany
Aghafakhr Mirlohi,
Isfahan University of Technology, Iran

*CORRESPONDENCE

Leonardo Velasco

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lvelasco@ias.csic.es

Stéphane Muños

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stephane.munos@inrae.fr

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This article was submitted to
Plant Pathogen Interactions,
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Frontiers in Plant Science

RECEIVED 29 September 2022

ACCEPTED 14 December 2022

PUBLISHED 06 January 2023

CITATION

Calderón-González A, Pérez-Vich B, Pouilly N, Boniface M-C, Louarn J, Velasco L and Muños S (2023) Association mapping for broomrape resistance in sunflower.

Front. Plant Sci. 13:1056231.
doi: 10.3389/fpls.2022.1056231

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© 2023 Calderón-González, Pérez-Vich, Pouilly, Boniface, Louarn, Velasco and Muños. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Association mapping for
broomrape resistance
in sunflower

Álvaro Calderón-González¹, Begoña Pérez-Vich¹,
Nicolas Pouilly², Marie-Claude Boniface², Johann Louarn²,
Leonardo Velasco¹* and Stéphane Muños²*

¹Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Científicas (IAS-CSIC), Córdoba, Spain, ²Laboratoire des Interactions Plantes Microbes-Environnement (LIPME), Université de Toulouse, CNRS, INRAE, Castanet-Tolosan, France

**Introduction:** Sunflower breeding for resistance to the parasitic plant sunflower broomrape (Orobanche cumana Wallr.) requires the identification of novel resistance genes. In this research, we conducted a genome-wide association study (GWAS) to identify QTLs associated with broomrape resistance.

**Methods:** The marker-trait associations were examined across a germplasm set composed of 104 sunflower accessions. They were genotyped with a 600k AXIOM® genome-wide array and evaluated for resistance to three populations of the parasite with varying levels of virulence (races EFR, FGV, and GTK) in two environments.

**Results and Discussion:** The analysis of the genetic structure of the germplasm set revealed the presence of two main groups. The application of optimized treatments based on the general linear model (GLM) and the mixed linear model (MLM) allowed the detection of 14 SNP markers significantly associated with broomrape resistance. The highest number of marker-trait associations were identified on chromosome 3, clustered in two different genomic regions of this chromosome. Other associations were identified on chromosomes 5, 10, 13, and 16. Candidate genes for the main genomic regions associated with broomrape resistance were studied and discussed. Particularly, two significant SNPs on chromosome 3 associated with races EFR and FGV were found at two tightly linked SWEET sugar transporter genes. The results of this study have confirmed the role of some QTL on resistance to sunflower broomrape and have revealed new ones that may play an important role in the development of durable resistance to this parasitic weed in sunflower.

KEYWORDS

broomrape resistance, genome-wide association mapping (GWAS), candidate genes, *Orobanche cumana*, parasitic plants
# 1. Introduction

Sunflower broomrape (Orobanche cumana Wallr.) is one of the main biotic stresses affecting sunflower. This holoparasitic plant parasitizes sunflower roots causing devastating effects if resistant cultivars and/or herbicide treatments are not used (Cvejić et al., 2020). The parasite has been traditionally present in many sunflower-producing areas of Europe and Asia (Fernández-Martínez et al., 2015). It has recently started to be detected in African countries such as Tunisia (Amri et al., 2012) and Morocco (Nabloussi et al., 2018).

Broomrape control strategies in sunflower have largely focused on using resistant cultivars. Their development was possible due to the existence of good sources of resistance, especially in wild Helianthus species, with monogenic inheritance in most cases (Fernández-Martínez et al., 2015; Cvejić et al., 2020). Genetic resistance to broomrape was introduced into sunflower in the early breeding programs in the former USSR in the first years of the 20th century (Velasco et al., 2016). However, the introduction of new resistance sources was followed by the appearance of new physiological races of the parasite that overcame resistance (Fernández-Martínez et al., 2015). Thus far, eight broomrape races designated with letters from A to H have been reported based on their virulence on sunflower differential lines (Cvejić et al., 2020). However, the current distinction between broomrape races in the main infested areas remains unclear, as there is little information on the correspondence of races with the same name reported in different countries (Cvejić et al., 2020). Races D and E were predominant until the middle 1990s, and they were satisfactorily controlled by the resistance gene *Or*<sub>5</sub>, widely used in commercial hybrids. Populations overcoming *Or*<sub>5</sub> resistance were detected in 1995 in Spain (Alonso et al., 1996) and shortly after in Romania, Turkey, and several other countries (Skoric et al., 2010). Currently, it seems clear that races more virulent than E (races F, G, and H) are predominant in most sunflower-producing areas where this parasite is present (Cvejić et al., 2020).

Genetic resistance to broomrape in sunflower has been found in most cases to be controlled by vertical resistance mechanisms that follow a gene-for-gene interaction, in which a dominant gene for host resistance interacts with a dominant avirulence gene in the parasite (Rodríguez-Ojeda et al., 2013). The genetic control of broomrape resistance by a single dominant gene was first reported by Pogorletsky and Geshele (1976). Shortly after, Vranceanu et al. (1980) identified five differential lines that had accumulative resistance to broomrape races A to E, controlled by five dominant resistant genes named *Or*<sub>1</sub> to *Or*<sub>5</sub>, respectively. Several other studies confirmed monogenic dominant resistance to race E (Sukno et al., 1999; Lu et al., 2000; Pérez-Vich et al., 2004). One dominant gene has also been reported controlling races overcoming *Or*<sub>5</sub> resistance, such as *Or*<sub>6</sub> conferring resistance

to race F from Romania (Pacureanu-Joita et al., 2004), *Or*<sub>7</sub> controlling race F from Spain (Duriez et al., 2019), *Or*<sub>Deb2</sub> for resistance to race G from Turkey (*G*<sub>TK</sub>) (Velasco et al., 2012), and *Or*<sub>SII</sub> and *Or*<sub>Pra1</sub> providing posthaustorial resistance to races F and G (Sayago et al., 2018; Martín-Sanz et al., 2020). Several major dominant genes have been located on the sunflower genetic map. *Or*<sub>5</sub> has been mapped to a telomeric region of chromosome (chr) 3 (Lu et al., 2000; Tang et al., 2003; Pérez-Vich et al., 2004). Later, Imerovski et al. (2013) and Imerovski et al., (2016) found simple sequence repeat (SSR) markers of chr 3 strongly associated with resistance genes other than *Or*<sub>5</sub> such as *Or*<sub>2</sub>, *Or*<sub>3</sub>, and *Or*<sub>6</sub>. Recently, Duriez et al. (2019) have mapped *Or*<sub>7</sub> to chr 7, and Martín-Sanz et al. (2020) and Fernández-Aparicio et al. (2022) have located *Or*<sub>SII</sub> and *Or*<sub>Deb2</sub>, respectively, to the upper half of chr 4.

In addition to the studies on vertical resistance, molecular studies have also focused on more complex genetic systems influencing broomrape resistance in sunflower, e.g., quantitative trait loci (QTLs) that contribute with small-to-moderate effects to decreasing the number of emerged broomrapes (Pérez-Vich et al., 2004; Akhtouch et al., 2016; Imerovski et al., 2019). It has been demonstrated that resistance QTL may act at different broomrape developmental stages, providing accumulative resistance mechanisms (Louarn et al., 2016). Within this quantitative component, the role of (i) “defeated resistance genes” corresponding to major resistance genes specific for a broomrape race which provide only moderate levels of resistance to a different-more virulent race (Imerovski et al., 2019), and (ii) resistance QTL present in susceptible cultivars (Pérez-Vich et al., 2004; Akhtouch et al., 2016), have also been demonstrated. The combination of major resistance genes with quantitative resistance factors is seen as a promising alternative to ensure durable sunflower protection against *O. cumana* (Pérez-Vich et al., 2013).

Genome wide association study (GWAS) is a powerful tool to identify QTLs by examining the marker-trait associations across diverse germplasms. Compared to traditional genetic linkage analysis based on bi-parent populations, GWAS increases mapping resolution, reduces research time, and includes more alleles (Zhu et al., 2008). The availability of high-density SNP genotyping data, linkage maps, and the full genome sequence (Badouin et al., 2017), together with sufficient linkage disequilibrium (LD) decay (Kolkman et al., 2007), have made it feasible to carry out large scale GWAS in sunflower. Association mapping studies in this crop have focused on flowering time (Cadic et al., 2013; Mandel et al., 2013; Bonnafous et al., 2018), branching pattern (Mandel et al., 2013; Nambeesan et al., 2015), fertility restoration (Goryunov et al., 2019; Talukder et al., 2019) and floral traits (Dowell et al., 2019). However, very few studies have been conducted on disease resistance, all of them on fungal pathogens (Fusari et al., 2012; Talukder et al., 2014). No association mapping studies have been reported so far on *O. cumana* resistance. Such studies are very limited for resistance to other parasitic plant
species, mainly centered on the interaction between *Striga hermonthica* and cereal crops (Adewale et al., 2020; Kavuluko et al., 2021).

In this study, we have used GWAS on a population of 104 diverse sunflower accessions with varying levels of resistance to *O. cumana*. The accessions were genotyped using a sunflower Affymetrix AXIOM Genome-Wide array and evaluated for resistance to three populations of *O. cumana* with varying levels of virulence in two environments for each broomrape population. The main objective of the study was to detect loci associated with resistance to this parasitic weed and to identify resistance candidate genes.

# 2. Materials and methods

## 2.1. Sunflower germplasm

The sunflower germplasm set included 104 accessions (Table S1) selected from the germplasm collections of the USDA-ARS (38), INRAE (46), and IAS-CSIC (20). Most of the accessions are of the oilseed type, although some of them are of the confectionery type. This information is provided in Table S1. Around one-third of the accessions (34) were selected because we had the previous indication that they possessed non-dominant resistance against broomrape, particularly to race F, but also in some cases to populations of race G. In general, the resistance of these lines was incomplete, i.e., they showed reduced infection but not immunity like the germplasm with dominant, vertical resistance. They were, in most cases, unpublished material, but some of the accessions have been reported previously, e.g., L86, K96, P96 and R96 (Fernández-Martínez et al., 2004), AM1, AM2 and AM3 (Pérez-Vich et al., 2006), and LR1 (Louarn et al., 2016).

## 2.2. Sunflower broomrape populations

Resistance of the sunflower accessions was evaluated with three contrasting sunflower broomrape populations from different origins and degrees of virulence. SP is a population of race $F_{GV}$ from the Guadalquivir Valley (GV) collected in Écija, Andalusia, Spain. Bourret is a population of race $E_{FR}$ collected in Bourret (Tarn et Garonne), Occitania, France. GT is a population belonging to race $G_{TK}$ collected in Çeşmekolu, Thrace, Turkey. Broomrape nomenclature follows Martín-Sanz et al. (2016).

## 2.3. Evaluation of broomrape parasitism

Sunflower accessions were evaluated for their reaction to broomrape populations Bourret and GT in pots in 2016 and

2017 in Córdoba, Spain. For population SP, evaluation was conducted in pots in 2017 and the field in 2018 in the same location. SP population, which belongs to the race $F_{GV}$ widely distributed in the area of the experiments, was the only population that could be evaluated under field conditions without the risk of introducing foreign populations in the area. In all cases, including the experiment in the field, all plants were inoculated with broomrape seeds as detailed below.

In all the experiments, sunflower seeds were germinated in moistened filter paper at 25 °C in the dark for 48 and sown in small pots 7 x7 x 7 cm filled with sand and peat and 50 mg of broomrape seeds. The soil mixture containing the broomrape seeds was shaken in a plastic bag to distribute broomrape seeds uniformly. The pots were maintained in a growth chamber at 25°C/20-°C (day/night) with 16 h photoperiod for six weeks, then transplanted into 5 L pots containing a soil mixture of sand, silt, and peat in a proportion 2:1:1 or to the field in the case of the field experiment in 2018. The pots were maintained under open-air conditions in the spring-summer period and watered as required. In the field, plants were watered with drip irrigation. Sowing dates were 9 to 11 March in 2016, 6 to 8 March in 2017, and 26 to 28 February in 2018. In pot experiments, seven pots per accession were used. In the field, the experiment included three replicates of eight plants each. In this case, the accessions were randomized within each replicate. Plant distance within the row was 33 cm, and the row separation was 1 m.

The number of emerged broomrape shoots (NEBS) was counted for each sunflower plant at the end of sunflower flowering.

Analysis of variance (ANOVA) was conducted on the number of emerged broomrape shoots using the accessions, the broomrape populations, and the environments (nested to the broomrape populations) as fixed factors. Mean squares values were used as an estimate of the relative weight of the factors on the number of emerged shoots. Pearson's correlation coefficients were also computed between environments for a given broomrape population and between the two-year average NEBS value of the accessions for each broomrape population. Analyses were conducted using SPSS statistical package version 27.

## 2.4. Tissue collection, DNA extraction and plant genotyping

Genomic DNA for the 104 accessions was extracted from leaf tissue using the Kit DNeasy Plant Mini Kit (Qiagen®). The DNA concentration was adjusted to 10ng/µl in water. The genotyping experiments were performed by the Gentyane platform (Plateforme Gentyane, UMR INRAE/UBP 1095 Génétique Diversité et Ecophysiologie des Céréales, Clermont-Ferrand, France) on a GeneTitan® (Affymetrix) according to the manufacturer's instructions. The AXIOM array was built using a set of 586,985 SNPs. Genotypic data were obtained with the software Axiom Analysis Suite (http://www.affymetrix.com).
## 2.5. Genetic diversity and population structure analysis

The genotyping data were imputed by genetic linkage group using BEAGLE (Browning and Browning, 2009). We filtered genotyping data by keeping a single SNP when redundant to others, and we removed SNPs showing minor allele frequency (MAF < 5%). Final filtering was done with software TASSEL v5.2.59 (Bradbury et al., 2007), removing a total of twelve markers classified as unmapped, which were discarded to create the definitive set of markers used for subsequent analyses. The kinship matrix (K-matrix) was calculated using the Centered-IBS method on this set of high-quality filtered SNP markers. Finally, we kept a set of 23,743 SNPs for further analysis after removing redundant markers.

The analysis of the genetic structure and kinship patterns of the population was computed using STRUCTURE ver. 2.3.4 (Pritchard et al., 2000) using the set of 23,743 SNPs. An admixture model following the Hardy-Weinberg equilibrium was used. The analysis was repeated ten times for each value of K (from 1 to 10) using a burn-in period of 100,000 Markov Chain Monte Carlo (MCMC) iterations and a run length of 100,000. The number of groups in the population was determined using Structure Harvester (Earl and von Holdt, 2012) with the Evanno correction (Evanno et al., 2005). The output of STRUCTURE analysis was subjected to the FullSearch algorithm of CLUMPP ver. 1.1.2b (Jakobsson and Rosenberg, 2007), and the output was used to produce bar graphs of the population structure using Origin Pro 9.1 software (OriginLab Corporation, Northampton, MA, USA).

For the genetic diversity analysis, we used 6,264 SNP bi-allelic markers, i.e., one out of every fourth marker. Shannon's information index (I) observed heterozygosity (Ho), Nei's expected heterozygosity (He), and the fixation index (F) were computed. A principal coordinates analysis (PCoA) was also conducted. GenAIEx 6.501 (Peakall and Smouse, 2012) was used for these analyses.

## 2.6. Genome-wide association analysis and linkage disequilibrium

A panel of 23,743 SNP markers with MAF > 5% was used for GWAS. A preliminary analysis evaluated the performance of the general linear model (GLM) and the mixed linear model (MLM) using either the Q-matrix or PCA covariates as cofactors. Additionally, the kinship (K) matrix was added to the MLM models to avoid spurious associations linked to the genetic relatedness. For MLM models, we also tested several compression and variance component estimation options. The analyses were conducted using phenotypic data (average NEBS per sunflower plant) for each broomrape population and environment, and the average values for each broomrape

population in the two environments. Quantile-quantile plots (QQ-plots) were constructed from the observed versus expected -log₁₀(p) values of each model. The significance of marker-trait associations (MTAs) was checked based on Bonferroni and false discovery rate (FDR) corrections at 5% and 20% (Benjamini and Hochberg, 1995). Furthermore, the range of linkage disequilibrium (LD) was computed using a sliding window of 50 kbp. Manhattan-plots were generated with the position and the p-value. The analyses were performed using TASSEL software v. 5.2.56. The matrix of p-value was used to estimate the FDR with the QVALUE package (Storey, 2002) in R.

## 2.7. Candidate gene analyses

The significant marker-trait associations obtained were mapped on the HanXRQr2.0-SUNRISE reference sunflower genome sequence (https://www.heliagene.org/HanXRQr2.0-SUNRISE). After the physical positions were extracted, the genomic regions of the significant SNPs were examined to identify the annotated protein-coding genes located in or close to the significant SNPs. Exploration of the genomic regions for identification of candidate genes was carried out as follows: (i) if a cluster of significant marker-single trait associations was found, the SNP and the physical region spanned by the significant markers (+/- 250-Kb) was explored for high confidence genes with predicted biological function; and (ii) if only one single SNP marker constituted the significant marker-single trait association, the genes putatively involved in plant disease and parasitic plant-resistance pathways containing or immediately adjacent (within a window of 250 kb) to the SNPs were identified. Finally, if no candidate genes were found using these criteria, the closest candidate gene with known function in disease and parasitic plant-resistance pathways was also selected. The nature of most significant annotated candidate genes, and of all the genes coding for uncharacterized proteins, unknown function, or directly annotated but without description, was verified in the NCBI *Helianthus annuus* annotation release 101 (2020-09-02), and through BLAST searches using the sunflower sequences.

# 3. Results

## 3.1. Phenotypic evaluation of sunflower genotypes

The analysis of variance showed a marked effect of the environment on the number of emerged broomrape shoots, accounting for 67.3% of the total estimated variance (Table 1). It was followed by the broomrape population, which accounted for 28.4% of the variance, and the sunflower accession, which contributed with 3.3% to the total variance. These three main
<table><caption>TABLE 1 Analysis of variance for the number of emerged broomrape shoots in a set of 104 sunflower accessions evaluated for three broomrape populations in two environments for each population.</caption><thead><tr><td>Source of variation</td><td>Degrees of freedom</td><td>Sum of squares</td><td>Mean squares</td><td>% MS</td><td>F</td><td>P</td></tr></thead><tbody><tr><td>Accession</td><td>103</td><td>464458</td><td>4509</td><td>3.3</td><td>40.4</td><td>&lt;0.01</td></tr><tr><td>Broomrape population</td><td>2</td><td>76607</td><td>38303</td><td>28.4</td><td>343.5</td><td>&lt;0.01</td></tr><tr><td>Environment (Br. Population)</td><td>3</td><td>271712</td><td>90571</td><td>67.3</td><td>812.3</td><td>&lt;0.01</td></tr><tr><td>Accession x Br. Population</td><td>206</td><td>109738</td><td>533</td><td>0.4</td><td>4.8</td><td>&lt;0.01</td></tr><tr><td>Accession x Environment</td><td>308</td><td>190394</td><td>618</td><td>0.5</td><td>5.5</td><td>&lt;0.01</td></tr><tr><td>Error</td><td>3359</td><td>374543</td><td>112</td><td>0.1</td><td></td><td></td></tr><tr><td>Total</td><td>3982</td><td>3267402</td><td></td><td></td><td></td><td></td></tr></tbody></table>

factors, as well as the interactions, were significant (P<0.01), although the interactions were of very low magnitude (Table 1).

The average NEBS per sunflower plant ranged from 6.91 in the evaluation for broomrape population SP in 2018 to 36.3 for broomrape population GT in 2017 (Table 2). For the three broomrape populations, there was a variable number of sunflower accessions that showed a high degree of resistance. Considering the accessions that showed less than one broomrape shoot in the average of both evaluations, we observed 23 accessions for population SP, seven accessions for Bourret, and two accessions for GT. If we consider the six evaluations, two accessions showed less than one broomrape per plant (Table S1).

Despite the large influence of the environment on the NEBS, correlation coefficients between the evaluations for the same population in different environments or even for the evaluation of different populations in different environments were in all cases positive and statistically significant. Correlation coefficients between the two evaluations for each population ranged from 0.59 for population GT to 0.82 for population SP. Considering the correlation coefficients between different populations in individual environments, they ranged from 0.50 (GT in 2016 vs. Bourret in 2016) to 0.77 (Bourret in 2017 vs. SP in 2017), whereas the correlation coefficients between populations considering the average value of the two evaluations ranged from 0.72 (GT vs. Bourret) to 0.78 (Bourret vs. SP) (Table 3).

## 3.2. Genetic diversity, population structure and linkage disequilibrium analysis

Single nucleotide polymorphism (SNP) markers were evenly distributed across the whole genome, from 629 SNPs in chr 7 to 2605 SNPs in chr 8 (Table 4). The number of SNPs per Mbp ranged from 5.1 in chr 15 to 17.1 in chr 8.

Genetic diversity analysis revealed that the means of the effective and observed allele numbers for the sunflower set were 2.0 and 1.75, respectively. The expected heterozygosity (Nei's gene diversity) and Shannon's information index were 0.42 and 0.60, respectively. The observed heterozygosity and the fixation index were 0.04 and 0.92, respectively.

The average pairwise genetic distance between sunflower accessions was 10,475 and ranged from 126 for accessions CD and HA89 to 13,423 for accessions UD and PI578010. Principal Coordinate Analysis (PCoA) revealed that the three first axes explained a low proportion of the total variance, 8.1, 6.4, and 4.3%, respectively. Figure 1 shows the biplot for PCo 1 and PCo 2. Some accessions were grouped very closely, for example accessions PO7-28, PO7-34, PO7-38, PO7-61, and PO7-63. They were developed in a recurrent selection program starting from a random mating population with selection for broomrape race F (unpublished). Their relatedness was unknown at the beginning of the research.

<table><caption>TABLE 2 Mean, standard deviation (SD), minimum and maximum values of emerged broomrape shoots in a set of 104 sunflower accessions evaluated for broomrape populations Bourret, SP and GT in two years.</caption><thead><tr><td>Population/Year</td><td>Mean</td><td>SD</td><td>Minimum</td><td>Maximum</td></tr></thead><tbody><tr><td>Bourret 2016</td><td>22.13</td><td>14.98</td><td>0.00</td><td>75.75</td></tr><tr><td>Bourret 2017</td><td>22.89</td><td>17.37</td><td>0.00</td><td>75.86</td></tr><tr><td>SP 2017</td><td>17.30</td><td>16.48</td><td>0.00</td><td>63.14</td></tr><tr><td>SP 2018</td><td>6.91</td><td>6.54</td><td>0.00</td><td>25.04</td></tr><tr><td>GT 2016</td><td>10.27</td><td>6.34</td><td>0.13</td><td>28.00</td></tr><tr><td>GT 2017</td><td>36.32</td><td>19.29</td><td>0.00</td><td>79.29</td></tr></tbody></table>
<table><caption>TABLE 3 Correlation coefficients between emerged broomrape shoots in a set of 104 sunflower accessions evaluated with three broomrape populations in two environments each one.</caption><thead><tr><th>Evaluation</th><th>SP_2018</th><th>Bourret_2016</th><th>Bourret_2017</th><th>GT_2016</th><th>GT_2017</th><th>Bourret_Average</th><th>GT_Average</th></tr></thead><tbody><tr><td>SP_2017</td><td>0.82**</td><td>0.66**</td><td>0.77**</td><td>0.59**</td><td>0.76**</td><td></td><td></td></tr><tr><td>SP_2018</td><td></td><td>0.55**</td><td>0.63**</td><td>0.55**</td><td>0.65**</td><td></td><td></td></tr><tr><td>Bourret_2016</td><td></td><td></td><td>0.67**</td><td>0.50**</td><td>0.61**</td><td></td><td></td></tr><tr><td>Bourret_2017</td><td></td><td></td><td></td><td>0.51**</td><td>0.66**</td><td></td><td></td></tr><tr><td>GT_2016</td><td></td><td></td><td></td><td></td><td>0.59**</td><td></td><td></td></tr><tr><td>SP_Average</td><td></td><td></td><td></td><td></td><td></td><td>0.78**</td><td>0.78**</td></tr><tr><td>Bourret_Average</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.72**</td></tr></tbody></table>

Correlation coefficients between the average values in the two environments for each population are also included.

**Significant at p<0.01.

The analysis of the genetic structure of the germplasm set suggested the existence of two main groups, as indicated by a K=2 using the Delta K method. LD was calculated using all the SNP markers and the LD decay was 0.25 x10⁶ bp for all the chromosomes (Figure 2), which is consistent with other studies in which it was observed that the linkage disequilibrium rapidly decays in sunflower (Liu and Burke, 2006; Kolkman et al., 2007; Fusari et al., 2008).

## 3.3. Marker trait association

The best fitting models were chosen analysing the quantile-quantile plots (QQ-plots). The deviation of observed vs expected -log₁₀ p-values was smaller for the mixed linear models (MLM) than for general linear models (GLM), with the best results within each group using GLM+PCA and MLM+K+PCA

<table><caption>TABLE 4 SNPs distribution across all the chromosomes with the position (bp) of the first and the last molecular marker per linkage group.</caption><thead><tr><th>inkage group</th><th>Number of SNPs</th><th>Length (Mbp)</th><th>SNPs/ Mbp</th><th>Physical position of the extreme markers in each chromosome (Mbp)</th></tr></thead><tbody><tr><td>1</td><td>2045</td><td>153.3</td><td>13.3</td><td>0 (0.01) - 2044 (153.3)</td></tr><tr><td>2</td><td>1605</td><td>177.7</td><td>9.0</td><td>2045 (1.4) - 3649 (179.1)</td></tr><tr><td>3</td><td>1360</td><td>167.8</td><td>8.1</td><td>3650 (0.6) - 5009 (168.5)</td></tr><tr><td>4</td><td>1234</td><td>178.5</td><td>6.9</td><td>5010 (0.2) - 6243 (178.8)</td></tr><tr><td>5</td><td>2069</td><td>218.6</td><td>9.5</td><td>6244 (0.4) - 8312 (219.1)</td></tr><tr><td>6</td><td>703</td><td>102.5</td><td>6.9</td><td>8313 (1.0) - 9015 (103.5)</td></tr><tr><td>7</td><td>629</td><td>103.8</td><td>6.1</td><td>9016 (0.05) - 9644 (103.8)</td></tr><tr><td>8</td><td>2605</td><td>152.4</td><td>17.1</td><td>9645 (0.04) - 12249 (152.5)</td></tr><tr><td>9</td><td>1206</td><td>207.8</td><td>5.8</td><td>12250 (1.3) - 13455 (209.1)</td></tr><tr><td>10</td><td>2101</td><td>245.3</td><td>8.6</td><td>13456 (0.9) - 15556 (246.2)</td></tr><tr><td>11</td><td>1055</td><td>167.9</td><td>6.3</td><td>15557 (0.01) - 16611 (167.)</td></tr><tr><td>12</td><td>989</td><td>165.6</td><td>6.0</td><td>16612 (0.05) - 17600 (165.7)</td></tr><tr><td>13</td><td>1360</td><td>195.7</td><td>6.9</td><td>17601 (1.1) - 18960 (196.8)</td></tr><tr><td>14</td><td>1108</td><td>173.9</td><td>6.4</td><td>18961 (0.5) - 20068 (174.3)</td></tr><tr><td>15</td><td>874</td><td>169.8</td><td>5.1</td><td>20069 (1.4) - 20942 (171.2)</td></tr><tr><td>16</td><td>1976</td><td>187.8</td><td>10.5</td><td>20943 (0.7) - 22918 (188.5)</td></tr><tr><td>17</td><td>2027</td><td>214.7</td><td>9.4</td><td>22919 (0.01) - 24945 (214.7)</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>FIGURE 1
Principal coordinate analysis of 104 sunflower accessions genotypes with SNP markers.</figcaption></figure>

combinations with optimum level of compression and re-
estimation after each marker (Figure 3).

Manhattan plot for GLM+PCA (Figure 4) and MLM+K
+PCA (Figure 5) revealed a total of 14 SNP markers significantly
associated with the number of emerged broomrapes. There were
six of them in GLM+PCA and four of them in MLM+K+PCA
with p < 2E-06 (5%) and 5 additional in GLM+PCA and 2 in
MLM+K+PCA with p < 8E-06 (20%) (Table 5). Significant
associations were identified on six different chromosomes
from the HanXRQr2.0-SUNRISE reference sunflower genome
assembly (https://www.heliagene.org/HanXRQr2.0-SUNRISE)
(Table 5), and for most of the broomrape populations and
environments, except for GT16. Some markers were significant
for two different broomrape populations and/or environments:
AX-105943713 for Bourret17 and SP17, and AX-105776042 for
SP17 and SP18 (Table 5). The trait variation explained by each

marker varied from 14 to 24% (Table 5). The most significant
peaks detected above the 5% Bonferroni threshold and identified
both with GLM+PCA and MLM+K+PCA were observed on chr
3, which, in addition, showed by far the highest number of
marker-trait associations. Two regions which contained
clustered associations were observed on this chromosome. The
first one was a 5.2 Mbp region spanned by the two markers AX-
105943713 and AX-147199586 [coordinates 85486771-
90700620 (HanXRQr2.0-SUNRISE)] and associated to both
race E<sub>FR</sub> (Bourret17) and race F<sub>GV</sub> (SP17) of broomrape. The
second one with markers AX-105705204, AX-105776042, AX-
105655280, and AX-105768536 ranged from physical position
129889814 bp to 136591650 bp (6.7 Mbp) (HanXRQr2.0-
SUNRISE) and it was associated only to broomrape race F<sub>GV</sub>
(SP17 and SP18) (Table 5). Other significant single marker-trait
associations were identified on chromosomes 5, 10 and 13 for
<figure><img src="image_6.png" /><figcaption>FIGURE 2
Linkage disequilibrium decay using SNP markers data set. Estimation of r² versus distance in base pair (bp) was represented. LD decay was established around 0.25e⁶ bp.</figcaption></figure>

race E<sub>FR</sub> populations (Bourret17 for chr 5, and Bourret16 for chr 10 and 13), chr15 for race F<sub>GV</sub> population SP17, and chr16 for race G<sub>TK</sub> population GT17 (Table 5).

## 3.4. Candidate genes

As mentioned above, significant associations were identified on linkage groups 3, 5, 10, 13, 15 and 16. The most relevant ones were found on two regions of chromosome 3. The first region on chr 3 was 5.2 Mbp long, delimited by markers AX-105943713 and AX-147199586. If the significant SNP was unique, the candidate gene analysis was centred on those genes containing the SNP and those found around +/- 250 Kbp. If a group of significant SNPs were found clustered, we focused on those genes containing the SNPs and on those found in the interval delimited by the tightly linked clustered SNPs (+/- 250 Kbp). Therefore, the five SNP markers in the 5.2 Mbp region were analysed as follows: (i) AX-105943713/AX-105531030, and AX-105925988/AX-105709192 as two clusters (and their +/- 250 Kbp

window), and (ii) AX-147199586 as a single marker and its +/- 250 Kbp window. SNP markers AX-105943713 and AX-105531030 delimited a 18592 bp long area (coordinates from 85486771 to 85505363). Both AX-105943713 and AX-105531030 were found within a putative SWEET sugar transporter (HanXRQr2_Chr03g0103911 and HanXRQr2_Chr03g0103941, respectively) (Table 6 and Table S2). Two additional coding regions in this interval were found, corresponding to putative mitochondrial carrier domain protein (HanXRQr2_Chr03g0103921) and to a putative potassium channel, voltage dependent EAG/ELK/ERG (HanXRQChr03g0075331). Very close to this interval (+/- 250Kbp) two putative transcription factors of the C3H (HanXRQr2_Chr03g0103891) and the AS2-LOB (HanXRQr2_Chr03g0103951) families were identified (Table 6 and Table S2). The second area in the 5.2 Mbp interval of chr 3 showing a cluster of associations was 773431 bp long. It was flanked by SNP markers AX-105925988 and AX-105709192 (coordinates from 86489659 to 87263090), which were not found within a protein coding gene. The AX-105925988 and AX-105709192
<figure><img src="image_7.png" /><figcaption>FIGURE 3</figcaption></figure>

Quantile-quantile (Q-Q) plots of observed versus expected P values of the GWAS results using GLM+PCA (A) and MLM+K+PCA (B). The straight line represents concordance of observed and expected values.

(+/- 250000 bp) interval contained 17 protein coding regions (Table S2), three of them (HanXRQr2_Chr03g0104061, HanXRQr2_Chr03g0104071, and HanXRQr2_Chr03g0104081) corresponded to putative geraniol 8-hydroxylases (cytochrome P450 genes). Annotation of two of these cytochrome P450 coding regions was corrected in NCBI *Helianthus annuus* Annotation Release 101 and they were grouped as one single locus coding for a 7-ethoxycoumarin O-deethylase (LOC110929042), which was confirmed through Blast searches of its genomic and RNAs sequences. Also, a putative non-specific serine/threonine protein kinase was identified in this interval (HanXRQr2_Chr03g0104051) (Table 6 and Table S2). Finally, within the 5.2 Mbp region, single marker trait association for race E<sub>FR</sub> (Bourret17) for marker AX-147199586 (position 90700620) was analysed for candidate genes. This SNP was found within a putative R-linalool synthase (HanXRQr2_Chr03g0104971). The AX-147199586 (+/- 250000 bp) area showed three protein coding genes, and among

them a putative transcription factor interactor and regulator of the CCHC(Zn) family (HanXRQr2_Chr03g0104961) was identified. The above described candidate genes were those tightly linked to the significant SNPs; however, it is worth mentioning that exploration of the 5.2 Mbp region in the AX-105925988/AX-105709192 to AX-147199586 interval outside the areas already described revealed an important proportion of protein kinase genes [out of 57 protein coding genes annotated in this region, 12 (21%) of them corresponding to protein kinases of the RLK-Pelle-LRR-I-1 f, RLK-Pelle-LRR-VIII-1, RLK-Pelle-LRR-XI-1, RLK-Pelle-CR4L, RLK-Pelle-SD-2b, RLK-Pelle-WAK, CMGC-GSK, and CMGC-CDK-CRK7-CDK9 families] (Table 6 and Table S2).

The second region on chr 3 showing a cluster of associations ranged from physical positions 129889814 bp to 136591650 bp (6.7 Mbp long). Due to the distance found between the four SNP markers in this interval, AX-105705204 and AX-105776042 were
<figure><img src="image_8.png" /><figcaption>FIGURE 4</figcaption></figure>

Manhattan-plots illustrating significant associations for resistance to three broomrape populations (SP, Bourret and GT) in a panel of 104 sunflower accessions evaluated in two environments each using GLM+PCA. The P values were adjusted using the Bonferroni threshold and false detection rate (FDR) correction (5% and 20%) to reduce false positive associations. The solid line corresponds to the 5% threshold and the dotted line to the 20% threshold. The vertical axis indicates -log10 of p-value and horizontal axis indicates chromosomes and physical positions of SNPs.

analysed as single markers and their +/- 250Kbp window, and AX-105655280, and AX-105768536 as an interval and their +/- 250Kbp window. The AX-105705204 +/- 250Kbp window contained 22 protein coding genes (Table 6 Table S2). Among them, a putative transcription factor interactor and regulator of the CCHC (Zn) family (HanXRQr2_Chr03g0116041) and a putative transcription factor TFIIIC (HanXRQr2_Chr03g0116071) were found tightly linked to this SNP. The AX-105776042 +/- 250Kbp region had 9 annotated genes (Table S2), which were not associated with plant resistance to pathogens. However, when exploring a larger window of 500 Kbp, three tightly linked transcription factors of the C2H2, Hap3/NF-YB, and CCHC(Zn) families were found 300 Kbp upstream this region, and other three

of the CCHC(Zn) and C2H2 families were identified 500 Kbp downstream the abovementioned region (Table 6 Table S2). Finally, within the AX-105655280 to AX-105768536 interval (+/- 250Kbp) a putative mitogen-activated protein kinase of the STE-STE11 family was identified (Table 6, Table S2).

Exploration of the genomic region surrounding the unique markers (+/- 250Kbp) of the remaining chromosomes revealed close genes that included proteins that might be associated with disease resistance, such as a putative transcription factor interactor and regulator of the CCHC(Zn) family (HanXRQr2_Chr05g0200451) in chr 5; three putative transcription factors of the TIFY and MYB families (HanXRQr2_Chr10g0458681, HanXRQr2_Chr10g0458741,
<figure><img src="image_9.png" /><figcaption>FIGURE 5</figcaption></figure>

Manhattan-plots illustrating significant associations for resistance to three broomrape populations (SP, Bourret and GT) in a panel of 104 sunflower accessions evaluated in two environments each using MLM+K+PCA. The P values were adjusted using the Bonferroni threshold and false detection rate (FDR) correction (5% and 20%) to reduce false positive associations. The solid line corresponds to the 5% threshold and the dotted line to the 20% threshold. The vertical axis indicates -log10 of p-value and horizontal axis indicates chromosomes and physical positions of SNPs.

HanXRQr2_Chr10g0458761) in chr 10; five protein kinases, a putative virus X resistance protein-like, two putative transcription factor interactor and regulator of the CCHC(Zn) family and five putative cytochrome P450s (two of them renamed as alkane hydroxylases MAH1) in chr 13; a putative protein kinase of the RLK-Pelle-DLSV and a putative transcription factor of the C2H2 family in chr 15; and two putative transcription factors (of the bHLH and AP2-EREBP families) and four clustered putative chromatin regulators of the PHD family in chr 16 (Table 6; Table S2).

## 4. Discussion

Resistance to broomrape in commercial sunflower hybrids is mainly qualitative, controlled by dominant alleles at major genes. However, this type of resistance is easily surpassed by the parasite, leading to a continuous race evolution that makes it difficult to control the parasite by means of genetic resistance (Fernández-Martínez et al., 2015). Alternative sources of resistance, such as those under quantitative genetic control are required. To that end, genome-wide association study (GWAS)
<table><caption>TABLE 5 SNP markers associated with broomrape resistance in different environments (composed of three races evaluated over 2 years) according the GLM corrected with PCA and MLM corrected with kinship (K-matrix) and PCA.</caption><thead><tr><th></th><th>Trait</th><th>Marker</th><th>Chr (HanXRQr2.0)</th><th>Position (bp) (HanXRQr2.0)</th><th>Initial mapped position (bp)in HanXRQr1.0</th><th>p-value</th><th>marker_R2</th></tr></thead><tbody><tr><td rowspan="10">GLM +PCA</td><td>Bourret17</td><td>AX-105943713</td><td>3</td><td>85486771</td><td>105501538</td><td>1.13E-06</td><td>0.16272</td></tr><tr><td>SP17</td><td>AX-105943713</td><td>3</td><td>85486771</td><td>105501538</td><td>5.48E-06</td><td>0.14168</td></tr><tr><td>Bourret17</td><td>AX-105531030</td><td>3</td><td>85505366</td><td>105482945</td><td>1.72E-08</td><td>0.23265</td></tr><tr><td>Bourret17</td><td>AX-105925988</td><td>3</td><td>86489659</td><td>51278297</td><td>3.73E-07</td><td>0.19847</td></tr><tr><td>Bourret17</td><td>AX-105709192</td><td>3</td><td>87263090</td><td>51917942</td><td>3.73E-07</td><td>0.19847</td></tr><tr><td>Bourret17</td><td>AX-147199586</td><td>3</td><td>90700620</td><td>62576510</td><td>5.79E-06</td><td>0.14342</td></tr><tr><td>SP17</td><td>AX-105705204</td><td>3</td><td>129889814</td><td>4872459</td><td>1.70E-07</td><td>0.20398</td></tr><tr><td>SP17</td><td>AX-105776042</td><td>3</td><td>133359785</td><td>110766840</td><td>2.89E-06</td><td>0.1716</td></tr><tr><td>SP18</td><td>AX-105776042</td><td>3</td><td>133359785</td><td>110766840</td><td>5.43E-06</td><td>0.15702</td></tr><tr><td>SP17</td><td>AX-105655280</td><td>3</td><td>135970618</td><td>113178523</td><td>8.84E-07</td><td>0.18535</td></tr><tr><td>SP17</td><td>AX-105768536</td><td>3</td><td>136591650</td><td>113484242</td><td>7.08E-06</td><td>0.16095</td></tr><tr><td>Bourret17</td><td>AX-105759358</td><td>5</td><td>24325063</td><td>44462658</td><td>5.82E-06</td><td>0.16604</td></tr><tr><td>Bourret16</td><td>AX-105929368</td><td>13</td><td>157389651</td><td>178662062</td><td>5.70E-06</td><td>0.18655</td></tr><tr><td>SP17</td><td>AX-105876346</td><td>15</td><td>38533277</td><td>49324811</td><td>3.79E-06</td><td>0.16838</td></tr><tr><td rowspan="6">MLM+K +PCA</td><td>Bourret17</td><td>AX-105531030</td><td>3</td><td>85505363</td><td>105482945</td><td>2.70E-06</td><td>0.21737</td></tr><tr><td>Bourret17</td><td>AX-105925988</td><td>3</td><td>86489659</td><td>51278297</td><td>1.76E-06</td><td>0.22416</td></tr><tr><td>Bourret17</td><td>AX-105709192</td><td>3</td><td>87263090</td><td>51917942</td><td>1.76E-06</td><td>0.22416</td></tr><tr><td>SP17</td><td>AX-105705204</td><td>3</td><td>129889814</td><td>4872459</td><td>1.25E-06</td><td>0.23529</td></tr><tr><td>Bourret16</td><td>AX-105891155</td><td>10</td><td>167173148</td><td>221096919</td><td>4.60E-06</td><td>0.20456</td></tr><tr><td>GT17</td><td>AX-105925592</td><td>16</td><td>2751833</td><td>4266574</td><td>5.32E-07</td><td>0.24252</td></tr></tbody></table>

Unshaded data corresponds to the markers that exceed Bonferroni 5% threshold and shaded markers corresponds with Bonferroni 20%.
<table><caption>TABLE 6 Summary of the most relevant genes identified as associated to significant marker trait associations (detailed information in Table S2).</caption><thead><tr><th>Chr</th><th>Interval explored</th><th>N. genes<sup>a</sup></th><th>SNP markers (clustered or single- marker associations)</th><th>SNP position (bp) (HanXRQr2.0)</th><th>Population</th><th>Most relevant genes<sup>b</sup></th><th>Gene start position (bp) (HanXRQr2.0)</th><th>Description</th></tr></thead><tbody><tr><td rowspan="4">3</td><td rowspan="4">AX-105943713 to AX-105531030 and their 250 Kbp window</td><td rowspan="4">11</td><td rowspan="2">AX-105943713</td><td rowspan="2">85486771</td><td rowspan="2">Bourret17/ SP17</td><td>HanXRQr2_Chr03g0103891</td><td>85268543</td><td>Putative transcription factor C3H family</td></tr><tr><td>HanXRQr2_Chr03g0103911</td><td>85462527</td><td>Putative SWEET sugar transporter</td></tr><tr><td rowspan="2">AX-105531030</td><td rowspan="2">85505366</td><td rowspan="2">Bourret17</td><td>HanXRQr2_Chr03g0103921</td><td>85489385</td><td>Putative mitochondrial carrier domain protein</td></tr><tr><td>HanXRQr2_Chr03g0103931</td><td>85489947</td><td>Putative potassium channel, voltage-dependent, EAG/ELK/ERG</td></tr><tr><td rowspan="4">3</td><td rowspan="4">AX-105925988 to AX-105709192 and their 250 Kbp window</td><td rowspan="4">17</td><td rowspan="2">AX-105925988</td><td rowspan="2">86489659</td><td rowspan="2">Bourret17</td><td>HanXRQr2_Chr03g0103941</td><td>85501458</td><td>Putative SWEET sugar transporter [bidirectional sugar transporter SWEET17 (LOC110929598)]</td></tr><tr><td>HanXRQr2_Chr03g0103951</td><td>85631556</td><td>Putative transcription factor AS2-LOB family</td></tr><tr><td rowspan="2">AX-105709192</td><td rowspan="2">87263090</td><td rowspan="2">Bourret17</td><td>HanXRQr2_Chr03g0104051</td><td>86256736</td><td>Putative non-specific serine/threonine protein kinase</td></tr><tr><td>HanXRQr2_Chr03g0104061</td><td>86283258</td><td>Putative geraniol 8-hydroxylase</td></tr><tr><td rowspan="2">3</td><td rowspan="2">Bigger interval from AX-105925988/AX-105709192 to AX-147199586 outside their 250 Kbp window</td><td rowspan="2">57</td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td>HanXRQr2_Chr03g0104071</td><td>86285554</td><td>Putative geraniol 8-hydroxylase [7-ethoxycoumarin O-deethylase (LOC110929042)]</td></tr><tr><td>HanXRQr2_Chr03g0104081</td><td>86328955</td><td>Putative geraniol 8-hydroxylase [7-ethoxycoumarin O-deethylase (LOC110929042)]</td></tr><tr><td>3</td><td>AX-147199586 and its 250 Kbp window</td><td>3</td><td>AX-147199586</td><td>90700620</td><td>Bourret17</td><td>HanXRQr2_Chr03g0104961</td><td>90567938</td><td>Putative transcription factor interactor and regulator CCHC(Zn) family</td></tr><tr><td>3</td><td>AX-105705204 and its 250 Kbp window</td><td>22</td><td></td><td></td><td></td><td>HanXRQr2_Chr03g0116041</td><td>129875663</td><td>Putative transcription factor interactor and regulator CCHC(Zn) family</td></tr></tbody><tfoot><tr><td colspan="9" style="text-align: right;">(Continued)</td></tr></tfoot></table>
<table><caption>TABLE 6 Continued</caption><thead><tr><th>Chr</th><th>Interval explored</th><th>N. genes<sup>a</sup></th><th>SNP markers (clustered or single-marker associations)</th><th>SNP position (bp) (HanXRQr2.0)</th><th>Population</th><th>Most relevant genes<sup>b</sup></th><th>Gene start position (bp) (HanXRQr2.0)</th><th>Description</th></tr></thead><tbody><tr><td rowspan="2">3</td><td rowspan="2">AX-105776042 and its 500 Kbp window</td><td rowspan="2"></td><td>AX-105705204</td><td>129889814</td><td>SP17</td><td>HanXRQr2_Chr03g0116071</td><td>129888590</td><td>Putative transcription factor TFIIIC, triple barrel domain-containing protein</td></tr><tr><td>AX-105776042</td><td>133359785</td><td>SP17</td><td>HanXRQr2_Chr03g0117711</td><td>135874296</td><td>Putative mitogen-activated protein kinase STE-STE11 family</td></tr><tr><td rowspan="2">3</td><td rowspan="2">AX-105655280 to AX-105768536 and its 250 Kbp window</td><td rowspan="2">35</td><td>AX-105655280</td><td>135970618</td><td>SP17</td><td>HanXRQr2_Chr03g0117741</td><td>135961367</td><td>Putative 1,4-alpha-glucan branching enzyme</td></tr><tr><td>AX-105768536</td><td>136591650</td><td>SP17</td><td>HanXRQr2_Chr05g0200451</td><td>24028576</td><td>Putative transcription factor interactor and regulator CCHC(Zn) family</td></tr><tr><td>5</td><td>AX-105759358 and its 250 Kbp window</td><td>8</td><td>AX-105759358</td><td>24325063</td><td>Bourret17</td><td>HanXRQr2_Chr10g0458681</td><td>167025903</td><td>Putative transcription factor TIFY family [Protein TIFY 10c (LOC110886429)]</td></tr><tr><td rowspan="3">10</td><td rowspan="3">AX-105891155 and its 250 Kbp window</td><td rowspan="3">20</td><td>AX-105891155</td><td>167173148</td><td>Bourret16</td><td>HanXRQr2_Chr10g0458741</td><td>167193456</td><td>Putative transcription factor MYB-related family</td></tr><tr><td></td><td></td><td></td><td>HanXRQr2_Chr10g0458761</td><td>167211637</td><td>Putative transcription factor MYB family [Transcription factor MYB3 (LOC110883374)]</td></tr><tr><td></td><td></td><td></td><td>HanXRQr2_Chr13g0610991</td><td>157302732; 157322524, 157325775</td><td>Three putative non-specific serine/threonine protein kinase genes</td></tr><tr><td rowspan="3">13</td><td rowspan="3">AX-105929368 and its 250 Kbp window</td><td rowspan="3">32</td><td></td><td></td><td></td><td>HanXRQr2_Chr13g0611011</td><td></td><td></td></tr><tr><td></td><td></td><td></td><td>HanXRQr2_Chr13g0611011</td><td></td><td></td></tr><tr><td></td><td></td><td></td><td>HanXRQr2_Chr13g0611021</td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>HanXRQr2_Chr13g0611031</td><td>157334169</td><td>Putative disease resistance RPP13-like protein 1 (LOC110902132)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>HanXRQr2_Chr13g0611041</td><td>157358197</td><td>Putative protein kinase CK1-CK1 family</td></tr><tr><td></td><td></td><td></td><td>AX-105929368</td><td>157389651</td><td>Bourret16</td><td>HanXRQr2_Chr13g0611081</td><td>157386420</td><td>Putative splicing factor 3B subunit 5/RDS3 complex subunit 10</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>HanXRQr2_Chr13g0611091</td><td>157419694</td><td></td></tr></tbody></table>

(Continued)
<table><caption>TABLE 6 Continued</caption><thead><tr><th>Chr</th><th>Interval explored</th><th>N. genes<sup>a</sup></th><th>SNP markers (clustered or single- marker associations)</th><th>SNP position (bp) (HanXRQr2.0)</th><th>Population</th><th>Most relevant genes<sup>b</sup></th><th>Gene start position (bp) (HanXRQr2.0)</th><th>Description</th></tr></thead><tbody><tr><td rowspan="4">15</td><td rowspan="4">AX-105876346 and its 250 Kbp window</td><td rowspan="4">23</td><td rowspan="4">AX-105876346</td><td rowspan="4">38533277</td><td rowspan="4">SP17</td><td>HanXRQr2_Chr13g0611131</td><td>157482837</td><td>Putative cytochrome P450 [Alkane hydroxylase MAH1 (LOC110899957)]</td></tr><tr><td>HanXRQr2_Chr15g0687981</td><td>38520847</td><td>Putative protein kinase RLK-Pelle-DLSV family</td></tr><tr><td>HanXRQr2_Chr15g0688011</td><td>38600086</td><td>Putative transcription factor of the C2H2 family</td></tr><tr><td>HanXRQr2_Chr16g0724281</td><td>2553825</td><td>Putative transcription factor bHLH family</td></tr><tr><td rowspan="2">16</td><td rowspan="2">AX-105925592 and its 250 Kbp window</td><td rowspan="2">23</td><td rowspan="2">AX-105925592</td><td rowspan="2">2751833</td><td rowspan="2">GT17</td><td>HanXRQr2_Chr16g0724391</td><td>2748783</td><td>Putative RNA recognition motif domain, mei2/Mei2-like RNA recognition [Protein MEI2-like 1 (LOC110914999)]</td></tr><tr><td>HanXRQr2_Chr16g0724411</td><td>2760903</td><td>Putative transcription factor AP2-EREBP family [Ethylene-responsive transcription factor ERF114 (LOC110917506)]</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>HanXRQr2_Chr16g0724481, HanXRQr2_Chr16g0724491, HanXRQr2_Chr16g0724511, HanXRQr2_Chr16g0724531</td><td>2847073, 2902831, 2945156, 2982242</td><td>Four putative chromatin regulators of the PHD family</td></tr></tbody></table>

<footnote><sup>a</sup>Number of annotated genes in the specified interval.</footnote>

<footnote><sup>b</sup>Most relevant genes (i) containing the SNP (shaded); (ii) in the interval spanned by clustered markers, within a 250 Kbp window, or (iii) closely located but outside the 250 Kbp window.</footnote>
is an optimized approach to identify new genes associated with
resistance to broomrape in sunflower. Using GLM and MLM
analysis, in combination with kinship and principal component
analysis (PCA), which reduce the computation demand and
solve the problems related to type I and type II error rates (Yu
et al., 2006), a total of 14 single nucleotide polymorphisms (SNP)
significantly associated with resistance to sunflower broomrape
were identified. Although complete resistance has been found in
the sunflower set for the three broomrape populations, the
evaluated trait NEBS showed mostly a continuous distribution
in the accessions. Considering this, the number of genomic
regions identified and their minor effects, this study confirmed
the involvement of quantitative resistance mechanisms in
genetic resistance to broomrape in sunflower, controlled by
multiple minor QTL associated to the number of broomrape
shoots per plant, as has been described previously (Pérez-Vich
et al., 2004; Akhtouch et al., 2016; Louarn et al., 2016; Imerovski
et al., 2019; Baytar et al., 2021). The complementary use of major
genes with resistance mechanisms under quantitative genetic
control has been proposed as an approach for developing more
durable genetic resistant to sunflower broomrape (Pérez-Vich
et al., 2013). A greater durability of such polygenic resistance
compared to monogenic resistance has been demonstrated in
other pathosystems involving viruses, fungi, and nematodes
(Palloix et al., 2009; Brun et al., 2010; Fournet et al., 2013).

Research on the development of resistance sources to broomrape in sunflower has been mainly focused on vertical mechanisms of resistance controlled by single loci with a major effect (Imerovski et al., 2019). Contrarily, little efforts have been devoted to the identification of germplasm with quantitative resistance to broomrape, with few accessions currently available for genetic studies. This fact has limited the size of the GWAS population used in the present study, consisting of 104 accessions but including 34 accessions for which quantitative resistance had been observed previously. Additionally, the accessions were tested with three contrasting broomrape populations under two environments, in all cases using artificial inoculation. To the best of our knowledge, there are no previous studies on the analysis of resistance to broomrape (Orobanche spp. and Phelipanche spp.) in sunflower or other crop species and accordingly it is not possible to compare the present study with previous ones. For resistance to other parasitic plants, e.g. Striga spp. in maize and sorghum, most association mapping studies were based on population sizes not very far from our population size, e.g. n=132 (Adewale et al., 2020), n=150 (Stanley et al., 2021), n=169 (Okunola et al., 2022), or n=173 (Kavuloko et al., 2021), in most cases using a single Striga population. Other studies used larger population sizes, e.g. n=380 (Gowda et al., 2021).

Most of the significant markers found in this study were
located in two different regions of chr 3. The upper region was
associated to both race E<sub>FR</sub> (Bourret17) and race F<sub>GV</sub> (SP17). It
was 5.2 Mbp long and spanned from 85.5 to 90.7 Mbp
(HanXRQr2.0-SUNRISE), delimited by markers AX-105943713

and AX-147199586. The lower one ranged from physical
positions 129.8.0 Mbp to 136.6 Mbp (6.7 Mbp) (HanXRQr2.0-
SUNRISE). It was delimited by AX-105705204 and AX-
105768536 markers and associated only to race F<sub>GV</sub> resistance
(SP17 and SP18). In chr 3, genes conferring resistance to
sunflower broomrape have been reported. Thus, Tang et al.
(2003) and Pérez-Vich et al. (2004) identified the gene Or5,
conferring resistance to sunflower broomrape race E, on the
upper telomeric region of this chromosome. These mapping
studies located this gene to the end of chr 3 distal to the SSR
marker locus CRT392 (Tang et al., 2003), which is the uppermost
SSR on chr 3, and to the RFLP marker locus ZVG406 (Pérez-Vich
et al., 2004), which is the uppermost RFLP on chr 3, and co-
segregating in BSA with the TRAP marker TRC27133 based on
chr 3 telomeric sequences (Márquez-Lema et al., 2008). CRT392 is
tightly linked to the SFW8304 SNP marker locus (Bowers et al.,
2012), located at 5.2 Mbp (HanXRQr2.0-SUNRISE). Also,
although the effect of the Or5 major gene on the number of
broomrapes per plant could be detected in an about 15 cM
support interval, it was clearly centred on the chr 3 telomeric
region (Pérez-Vich et al., 2004). Considering this and the position
of the significant SNP on chr 3 from this study, it seems likely that
the significant regions detected on this chromosome do not
underlie the major gene Or5, and their effects are more related
to other mechanisms associated to a quantitative component of
broomrape resistance. In addition to Or5, Imerovski et al. (2019)
identified two regions also in chr 3 associated with broomrape
resistance: the region between 31.97 and 38.48 Mbp (from
HanXRQr1.0 assembly), named as QTL or3.1 by the authors,
and the region between 97.13 and 100.85 Mbp (from
HanXRQr1.0 assembly), named QTL or3.2. These locations are
close to the two significant regions on chr 3 identified in this study
[the upper and lower chr 3 regions showed significant SNPs at
positions 51.3 to 51.9 and 110 to 113 Mbp, respectively in the
HanXRQr1.0 assembly (Table 5)]. The study of Imerovski et al.
(2019) was based on bi-parent genetic populations and therefore
analytical and experimental procedures were completely different
to those used in this study. In addition, these authors used only
one broomrape population, race G from Serbia, also different to
those used in this research. The fact that two different significant
regions on chr 3 have also been identified in this study, at close
proximity to the or3.1 and or3.2 QTL intervals defined by
Imerovski et al. (2019), indicates that both QTL are likely to be
coincident with the two regions detected on chr 3 in this study,
and that they are stable and expressed over a wide range of
environments, analytical procedures and broomrape races.
Additionally, Akhtouch et al. (2016) identified a QTL on chr 3
associated with recessive resistance to race F<sub>GV</sub> of broomrape in
line K-96, flanked by SSR markers ORS338 and ORS10. ORS338
blast searches against the HanXRQr2.0-SUNRISE assembly
located this marker at 138.7 Mbp, very close to the lower chr 3
significant 6.7 Mbp interval from this study. The present study
reinforces therefore the importance and effect of chr 3 regions,
other than the major *Or5* gene, on broomrape resistance in sunflower.

Following the same nomenclature of Imerovski et al. (2019), the or3.1 region in our study was that spanning from 85.5 to 90.7 Mbp (HanXRQr2.0-SUNRISE). Within this or3.1–5.2 Mbp region, candidate genes were identified at or tightly linked to the significant SNPs in three separate intervals containing 11, 17 and 3 protein coding genes, associated with Bourret17/SP17, Bourret17, and Bourret17, respectively. Among these candidate genes, two of them carrying the SNPs AX-105943713 and AX-105531030 were SWEET (Sugars Will Eventually be Exported Transporters) sugar transporter genes. SWEET transporters are mainly involved in the efflux of both mono- and di-saccharides from the site of synthesis to the sink organs, like grains, flowers, or roots (Chen et al., 2010; Chen et al., 2012). They play a critical role in important plant physiological processes such as pollen nutrition, nectar secretion, stress tolerance, phloem transport, and plant-microbe interactions (Jeena et al., 2019). It has been shown that pathogens use these genes to extract sugars for their nutrition, and that SWEET genes are negative regulators of disease resistance (Devanna et al., 2021). One of the best-known examples of this is the Xa13 (SWEET11/Os8N3) locus in rice which is responsible for recessive resistance to blight caused by Xanthomonas oryzae pv. oryzae. In this case, the pathogen effector molecule TAL (transcription activator-like) precisely binds with a cis regulatory element of the SWEET11 gene promoter and modulates its transcription for enhancing the efflux of sugars which are utilized by the pathogen. The loss of pathogen-induced transcriptional motivation alters the plant-pathogen reaction from susceptibility to resistance (Jeena et al., 2019; Devanna et al., 2021). In the context of a plant-to-plant parasitic relationship, sucrose transfer at the host-parasite interface, in addition to sucrose phloem unloading in the sink tissues of tubercle and shoot, represent key processes in the parasite growth (Misra et al., 2019). There are no previous reports on the role of SWEET genes in resistance to parasitic plants, but their involvement in parasite development and sunflower resistance deserves further studies. In addition to SWEET genes, the or3.1–Or5–5.2 Mbp region showed a putative non-specific serine/threonine protein kinase tightly linked to SNP AX-105925988, as well as 12 protein kinase genes. So far, the only gene conferring resistance to sunflower broomrape that has been cloned in sunflower (Or7 on chr 7) has been identified as a receptor-like protein kinase gene (Duriez et al., 2019). Accordingly, the kinase genes identified as candidate genes in this research are promising candidates for future investigations.

The or3.2 region on chr 3 associated to race G resistance reported by Imerovski et al. (2019) was likely to be coincident with that delimited by AX-105705204 and AX-105768536 in the present study, which ranged from physical positions 129.8.0 Mbp to 136.6 bp (6.7 Mbp) (HanXRQr2.0-SUNRISE). In our study, markers from that region were associated with broomrape

resistance to race F<sub>GV</sub> (SP17 and SP18), but not with resistance to race G<sub>TK</sub> (Table 5). Within the AX-105705204 and AX-105768536 interval, transcription factors of several families were tightly linked to the significant SNPs. Plant transcription factors play roles in diverse biological processes, including defensive responses to pathogens, in which they regulate genes related to pathogen-associated molecular pattern-triggered immunity, effector-triggered immunity, hormone signalling pathways, and phytoalexin biosynthesis (Seo and Choi, 2015). Also, they have been reported to be hubs targeted by multiple pathogen effectors in diverse ways (Mukhtar et al., 2011). Yang et al. (2020) showed that genes related to transcription factors were highly induced in a resistant sunflower cultivar after inoculation with a broomrape population of race G from China, while more transcription factor genes were found down-regulated than up-regulated in a susceptible cultivar.

For resistance to the Bourret population (race E<sub>FR</sub>), chromosomes 5, 10, 13 also showed significant marker-trait associations. Again, transcription factors and protein kinase genes were found tightly linked to the significant SNPs at these regions. Pérez Vich et al., (2004); Akhtouch et al. (2016); Louarn et al. (2016) and Imerovski et al. (2019) also found QTL in these chromosomes associated to races E, F or G using bi-parent populations. Particularly, the QTL on chr 13 or13.2 was located by Imerovski et al. (2019) at about 174.8 Mbp (HanXRQr1.0), associated to race G from Serbia and by Pérez Vich et al. (2004) close to the RFLP marker locus ZVG547, located at 173.1 Mbp (HanXRQr2.0), and associated to race F<sub>GV</sub>. These positions are close to the significant chr 13 SNP [AX-105929368; 157.4 Mbp (HanXRQr2.0), 178.7 Mbp (HanXRQr1.0)] detected in this study. Additionally, two QTL for resistance to populations SP (race F<sub>GV</sub>) and GT (race G<sub>TK</sub>), respectively, were detected at chromosomes 15 and 16. For race G<sub>TK</sub>, this was the only significant marker-trait association found, which differed from results found for the other two races evaluated. The effect of a quantitative component determining partial resistance has been described for a race G population from Serbia (Imerovski et al., 2019). A putative ethylene responsive transcription factor ERF114 (AP2-ERF) was found tightly linked (at 9070 bp) to the significant SNP on chr 16. Interestingly, in the interaction between the parasitic weed *Striga hermonthica* and rice, the systemic-acquired resistance (SAR) pathway is regulated by both jasmonic acid (JA) and salicylic acid (SA) in a cross talk mediated by WRKY45 (Mutuku et al., 2015) and regulated by ethylene responsive factor (AP2/ERF) transcription factor (Licausi et al., 2013). Additionally, AP2/ERFs were found to be significantly associated with *S. hermonthica* resistance to maize in a GWAS study (Adewale et al., 2020).

In sunflower, several GWAS studies have been conducted on traits such as basal and apical branching (Nambeesan et al., 2015), abiotic stresses (Mangin et al., 2017), flowering time (Bonnafous et al., 2018) or flower morphological traits (Dowell et al., 2019). However, there are no previous studies on the use of
GWAS approach to analyse resistance to sunflower broomrape. The present study, using three broomrape populations with contrasting degree of virulence, revealed several genomic regions that were associated with broomrape resistance. Candidate genes putatively involved in broomrape resistance were identified in these regions. This information will serve as a basis for the identification and characterization of novel broomrape resistance genes of value for developing durable genetic resistance to this parasitic weed.

## Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

## Author contributions

SM and BP-V conceived the work and planned and supervised the research. NP conducted plant genotyping. AC-G, JL, MCB and LV conducted phenotypic evaluations. AC-G, SM, and LV conducted statistical analyses. AC-G, LV, and BP-V wrote the draft of the manuscript. All authors contributed to the article and approved the submitted version.

## Funding

The study was funded by research project AGL2017-87693-R and PID2020-117286RB-I00 of the Spanish Ministry of the

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## Acknowledgments

The authors thank Plácida Nieto and Alberto Merino for technical support.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1056231/full#supplementary-material

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