# Unraveling the genetic basis of full flowering date in olive tree through QTL mapping approach: Towards climate-adaptive breeding

Othmane Lamoumni, Ahmed El Bakkali, Omar Abou-Saaid, Phillipe Cubry, Lorenzo Leon, Magalie Delalande, Pierre Mournet, Gaetan Droc, Gautier Sarah, Hayat Zaher, et al.

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Othmane Lamoumni, Ahmed El Bakkali, Omar Abou-Saaid, Phillipe Cubry, Lorenzo Leon, et al. Unraveling the genetic basis of full flowering date in olive tree through QTL mapping approach: Towards climate-adaptive breeding. 2025. hal-05207778

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# Unraveling the genetic basis of full flowering date in olive tree through QTL mapping approach: Towards climate-adaptive breeding

**Othmane Lamoumi**

University of Cadi Ayyad, Centre d'Agrobiotechnologie et Bioingénierie, Marrakech, Morocco.

**Ahmed El Bakkali**

INRA, UR Amélioration des Plantes et Conservation des Ressources Phytogénétiques, Meknès, Morocco. https://orcid.org/0000-0002-2936-8197

**Omar Abou-Saaid**

University of Cadi Ayyad, Centre d'Agrobiotechnologie et Bioingénierie, Marrakech, Morocco
https://orcid.org/0000-0002-4412-8589

**Phillipe Cubry**

DIADE, University of Montpellier, CIRAD, IRD, Montpellier, France. https://orcid.org/0000-0003-1561-8949

**Lorenzo Leon**

IFAPA, Center “Alameda del Obispo”, Córdoba, Spain.

**Magalie Delalande**

AGAP Institute / University of Montpellier / CIRAD / INRAE / Montpellier SupAgro, Montpellier, France.

**Pierre Mournet**

CIRAD, UMR AGAP Institute, F-34398 Montpellier, France. https://orcid.org/0000-0001-8011-8647

**Gaetan Droc**

CIRAD, UMR AGAP Institute, F-34398 Montpellier, France.

**Gautier Sarah**

AGAP Institute / University of Montpellier / CIRAD / INRAE / Montpellier SupAgro, Montpellier, France.
https://orcid.org/0000-0001-5179-972X

**Hayat Zaher**

INRA, UR Amélioration des Plantes, Marrakech, Morocco.

**Cherkaoui El Modafar**

University of Cadi Ayyad, Centre d'Agrobiotechnologie et Bioingénierie, Marrakech, Morocco.

**Bouchaib Khadari**

bouchaib.khadari@cirad.fr
## Research Article

**Keywords:** Olea europaea L., Quantitative Trait Loci (QTL) mapping, full flowering date (FFD), global warming, BLUPs, high-density genetic map, candidate genes, genomics, Genotyping-by-Sequencing (GBS)

**Posted Date:** April 24th, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-6510540/v1

**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License.
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**Additional Declarations:** The authors declare no competing interests.
# Abstract

# Background

Future climate models project severe impacts on flowering phenology of perennial fruit trees in the Mediterranean region under increasing global warming, including the olive tree, a key species extensively cultivated in the region. Thus, understanding the genetic factors regulating flowering is crucial for providing knowledge to select suitable cultivars and designing future olive breeding programs. Here, we aimed to investigate the genetic control of full flowering date (FFD) through Quantitative Trait Loci (QTL) mapping approach. Two high-density parental genetic maps, with more than 10k SNPs, were constructed based on an "Olivière" x "Arbequina" F1 hybrid progeny. Phenological observations of the same progeny were conducted across five environments (site × season), and data served to compute Best Linear Unbiased Predictors (BLUPs) for FFD. Both FFD-based BLUPs and single-environment data were used to detect key QTLs, which were further explored through *in-silico* candidate genes investigation.

# Results

Analysis of FFD distribution highlighted a high heritability with transgressive segregation. A total of 18 significant QTLs were identified in BLUPs analysis, and six were selected as the most relevant. Two QTLs were co-detected on the same linkage groups (LGs) of both parental genetic maps in BLUPs and some environments: LG09 (*qFDO9b*/*qFDA9*) and LG07 (*qFDO7*/*qFDA7*). Additionally, four QTLs on LG3 (*qFDA3*), LG22 (*qFDA22*) and LG13 (*qFDA13*) on "Arbequina" map, and LG13 (*qFDO13*) on "Olivière" map were revealed in BLUPs as well as in some single-environment analyses. Both *qFDA13* and *qFDA22* were characterized both by higher explained variance (14.6% and 11.6%, respectively) and additive values (-1.09 and + 1.15, respectively). Candidate genes investigation revealed genes within key QTLs probably involved in transcription regulation, including *WRKY71*, *RLT3*, and *ABSCISIC ACID-INSENSITIVE-5-LIKE*, in addition to a transport protein: *FT-INTERACTING protein1*. Genes highlighted were shown to interact with main flowering date regulators such as *FLOWERING LOCUS T (FT)* and *FLOWERING LOCUS C (FLC)*.

# Conclusion

Our study aimed to highlight the genetic control of the flowering date in the olive tree. The genomic regions covered by the detected QTLs and the candidate genes identified represent valuable resources for further investigations through genome-wide association and functional genomics studies. These findings will provide key information for applying genomic selection to develop new varieties more adapted to future climate projections.

# Background
The olive tree (Olea europaea L. subsp. europaea var. europaea) is one of the most widely cultivated tree species in the Mediterranean region. This area is currently recognized as a climate change hotspot [1], with agroecosystems being increasingly impacted by irregular and limited rainfall, along with a warming trend. Currently, the average temperature in the region is approximately 1.5°C higher than the pre-industrial era (1850–1900), with projections indicating a further increase of 0.5 to 6.5°C by 2100, depending on the climate change scenario [1]. While the impact of climate change on agriculture varies, as reported by many authors [2–5], there is broad consensus among different projection models regarding the continuous rise in winter temperatures with a significant decline in the amount of winter chill, making this a likely future scenario in the Mediterranean region. These models have succeeded in describing the current overall climatic trends in this region. However, local-scale projections may show varying degrees of severity, with some regions experiencing more or less pronounced impacts than those suggested by broader projections [6–7].

As a perennial species, olive tree production depends on its growth and development, which are impacted by climatic conditions during its annual cycle over the years. Temperatures variations are particularly crucial for key-phenological stages of the tree, mainly flowering [4,8]. With the occurrence of more frequent late frost and/or heat waves events, this key-phenological stage may be damaged, potentially causing significant olive yield losses. Moreover, under warmer winters, olive orchards may experience disruptions not only in tree phenology level but also on flower quality [8–10]. The effect of winter temperatures on olive flowering has been highlighted since early investigations in the 20th century [11]. Known as chill accumulation, this process has been linked to endodormancy, during which floral buds undergo internal regulations in response to a specific period of chilling in winter to bloom in spring [12]. The required chilling period can vary depending on the genotypes and internal factors play a crucial role in determining genetic variability among cultivars regarding chill and heat requirements, which in turn influence flowering dates [13–14]. Investigating both genetic variability and internal mechanisms governing flowering will provide valuable insights for designing and implementing new olive breeding programs. This will be particularly relevant to develop and select resilient cultivars to winter cold deficit.

The genetic basis of flowering has been widely studied in the model species, *Arabidopsis thaliana*, where multiple pathways have been identified as key processes in flowering time regulation [15–16]. These pathways control the expression of flowering integrator genes including mainly *FLOWERING LOCUS T* (*FT*), *SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1* (*SOC1*), and *LEAFY* (*LFY*). Consecutively, these genes regulate the expression of meristem identity genes, thus mediating meristematic transition to floral organs [16]. In the vernalization pathway, *FLOWERING LOCUS C* (*FLC*), a MADS-box flowering inhibitor protein regulates flowering date in response to winter temperatures through negative regulation of flowering integrator genes, mainly *FT* [17]. In perennial fruit trees, homologues of flowering date control genes, previously revealed in *A. thaliana*, have been successfully identified, such as *CiFT* (*FT* homolog) in citrus [18], *MdFLC-like* (*FLC* homolog) and *MdFT1* (*FT* homolog) in apple [19–20], and *PavFT* (*FT* homolog) in sweet cherry [21]. Moreover, *Dormancy Associated MADs-box* (*DAM*) gene family has been identified in the *Rosaceae* family, with differentiated expression patterns observed in cultivars
with contrasting chill requirements in peach [22], sweet cherry [23], and apricot [24]. In olive, two homologous *FT* genes (*OeFT1* and *OeFT2*) showed relatively higher expression levels in trees of "Barnea" cultivar under sufficient winter chill. The high expression levels of *OeFT* genes were followed by a higher expression of meristem identity genes (*OeAP1-1* and *OeAG-1*) and, consequently, flower buds' initiation [25]. Despite the progress achieved so far, knowledge on the genetic control of flowering in olive remains limited compared to many other temperate fruit species.

Quantitative Trait Loci (QTL) mapping and Genome-wide association studies (GWAS) have been widely used to investigate phenotype-genotype associations in multiple crops [26-27]. However, few studies have been reported in olive. A QTL mapping study on an F1 "Oliviere" x "Arbequina" hybrid population investigated both flowering and fruiting traits, in relation to growth units and branching. This study reported multiple significant QTLs, highlighting thus the multigenic control of the traits studied. Notably, most QTLs were year-specific, despite moderate to high heritability values [28]. In another study, QTL mapping was performed using two parental genetic maps, constructed using DArT (Diversity Arrays Technology) markers in an F1 progeny derived from "Picual" x "Arbequina" cross. The study revealed 27 QTLs for some agronomic traits such as fruit weight and oil content, with some QTLs being associated to more than one trait [29]. The same F1 population was further used for QTL mapping of fatty acid composition, revealing QTLs mapped on the same LG for oleic and linoleic acid contents, which explained substantial proportion of the phenotypic variance, 41.1% and 69.7%, respectively [30]. With advancements in Next Generation Sequencing (NGS) technologies, diverse *O. europaea* genomes assemblies have been published [31-36]. The first high-density genetic maps for olive were constructed, based on more than 1k SNP markers [37-39]. Other high-density maps were subsequently developed including one application in trait mapping, where Mariotti et al. [40] constructed two parental genetic maps using GBS-SNP markers to map the incompatibility locus in an F1 progeny derived from "Leccino" x "Dolce Agogia" cross. It is worth noting that the application of high-density genetic maps for QTLs mapping in olive tree is still at early stages. Until recently, few studies had either explored previously published high-density maps, or developed new ones, to map traits of interest. Granata et al. [41] used a published high-density genetic map of olive, derived from an F2 "Koroneiki" population [37], to map tree vigor related trait. On the other hand, a new GBS-SNP-based genetic map was recently developed using "Gemlik x Edincik Su" F1 progeny to map oil composition traits [42], while two DArT-SNP based parental maps were used for mapping of frost tolerance in an F1 "Memecik x Uslu" progeny [43]. The use of such SNP-based genetic maps has expanded QTLs identification in olive, enabling the investigation of candidate genes by aligning mapped markers on available genome assemblies [31-36]. Thus, the use of available biparental crosses and the low-cost sequencing technologies to develop high-density genetic maps will significantly enhance the olive research community' understanding of QTLs linked to agronomic traits of interest, and therefore, investigating the key genetic factors controlling these traits. Focusing on flowering-related traits, high-density SNP-based genetic association studies remain limited as the case for many other traits of interest. To our knowledge, the only study to date is the recently published work of Aqbouch et al. [44], which investigated the full flowering date trait using GWAS on a worldwide olive collection. GWAS and QTL mapping analysis are interchangeably applied to investigate
genetic control of traits using populations of unrelated genotypes and biparental cross generated progenies, respectively. Thus, QTL mapping can provide more insights that will enhance knowledge about the genetic architecture behind a complex trait such as flowering date.

In this context, the present study aimed to investigate the underexplored genetic architecture of full flowering dates (FFD) through QTL mapping approach. We used an olive F1 hybrid progeny, phenotyped for FFD across five environments and genotyped with more than 10k SNPs markers to: (i) construct high-density parental genetic maps and (ii) identify genomic regions associated with key QTLs and candidate genes linked to FFD. Our findings were discussed regarding the impact of climate conditions on FFD variations, the relevance of the constructed parental genetic maps, the detected QTLs, and the identified candidate genes.

# Methods

## 1. Plant material

Genotypic and phenotypic data were collected on an F1 hybrid progeny resulting from a cross between "Olivière" and "Arbequina" cultivars. A total of 156 genotypes, including the two parents, were planted in 2005 at DiaScope experimental unit, INRAE Montpellier-France (43.610, 3.976, altitude = 11 m), while 106 derived genotypes were planted in 2009 at the IFAPA experimental farm in Cordoba, Spain (37.859, -4.798, altitude = 92 m; **Figure 1**). At both sites, trees were planted with at least three replicates in a randomized experimental design and were maintained with standard cultural practices, including regular irrigation, fertilization and phytosanitary protection.

## 2. DNA extraction and GBS Libraries construction for sequencing

Fresh leaf samples were collected from each tree of the progeny and the two parents. Around forty mg of fresh leaf material were ground into a fine powder in the presence of liquid nitrogen using a high-throughput plant tissue homogenizer (Geno Grinder®). Total genomic DNA of each sample was extracted with an Alkyltrimethylammonium bromide-based buffer at 70°C for 1 hour, and purified using an automated pipetting method with a Biomek FXP instrument (Beckman Coulter, CA, USA) and the DNA NucleoMag Plant Kit (Macherey-Nagel, Germany) for nucleic acid extraction. DNA concentrations were quantified using Hoescht dye and a Fluoroskan Ascent FL fluorometer (ThermoFisher Scientific, Waltham, MA, USA), while DNA quality was assessed on a 1.6 % agarose-based gel.

Genomic libraries were prepared by digesting 100-150 ng of DNA per sample with *Pst*I and *Mse*I restriction enzymes (New England Biolabs, Hitchin, UK) at 37°C for two hours (**Figure S1**). The samples were then ligated to either barcoded or common adaptors using T4 DNA ligase (NEB#M0202L; New England Biolabs, Hitchin, UK). Ligated samples were pooled and PCR reactions were carried out, in several reactions, in a 50 µL volume, consisting of 5 µL of pooled and barcoded samples, 2 µL of each primer (10 µM), 25 µL of NEB 2X Taq Master Mix (NEB#M0270S; New England Biolabs, Hitchin, UK) and 16 µL of RNAse-free water. The PCR-amplified products were then purified using the Wizard PCR prep
DNA purification system from Promega (Madison, WI, USA) and verified with the Agilent 4200 TapeStation system (Santa Clara, CA, USA). Libraries were sequenced at GeneWiz Next Generation sequencing services platform (New Jersey, USA) with an Illumina HiSeq 2500 system (2 x 150 bp sequencing).

### 3. GBS-SNP calling and filtering

Reads processing, mapping, and single nucleotide polymorphism (SNP) calling were performed using the VcfHunter bioinformatic pipeline [45]. The reads were aligned to the "Farga" cultivar reference genome Oe9 [33] using Burrows Wheeler alignment (BWA; [46]) with default settings, and SNPs were called using UnifiedGenotyper in GATK v. 3.8 [47]. InDels were removed, and only positions with a depth of coverage (DP) > 3 and mapping quality (MQ) > 20 were retained. The generated VCF file was subjected to a pre-filtering procedure using VcfPreFilter.1.0 python script implemented in the VcfHunter pipeline with the following parameters: maximal read coverage for site (*MaxCov*=1000), minimal read coverage for site (*MinCov*=6), and minimal read number of minor alleles to call variant heterozygous (*MinAlCov*=3). The resulting VCF file was then filtered using VCFtools program [48] with the following options: `--mac 3`, `--maf 0.05`, `--minDP 10` and `--max-missing 0.95`.

### 4. Linkage maps construction

Following data processing, filtration and conversion, 27,359 high-quality loci were retained and used for genetic maps construction. A threshold of 5% missing data was applied for both individuals and markers. The pseudo-testcross approach was used [49], and markers were split based on their segregation patterns according to the CP model using Vcf2pop.1.0 python script implemented in VcfHunter pipeline: `"lm×ll"` and `"nn×np"` for female and male segregating markers, respectively, and `"hk×hk"` for segregating markers in both parents. Markers showing significant segregation distortion (Chi-square test, *p*-value < 0.05) were excluded, and to reduce redundancy, one representative marker was randomly chosen from each set of markers showing identical genotypic information (100%). The non-distorted and non-redundant markers were then assigned to linkage groups (LGs) using a minimum logarithm of the odds (LOD) score of 12.0 and a maximum recombination frequency of 0.35. Markers ordering within LGs was performed using RECORD algorithm [50] implemented in Onemap 3.0.0 R package [51], as it outperformed Rapid chain delineation and Unidirectional Growth algorithms. Finally, map distances, in centiMorgans (cM), were estimated using Kosambi mapping function. Parental genetic maps were constructed using the Onemap R package with parallelization options [52] and visualized with the *draw_map2()* function. All computations were performed on the MESO@LR-Platform of the University of Montpellier [53].

### 5. Phenotypic data and environment characterization

Full flowering dates (FFD) were recorded at both sites over multiple seasons according to the BBCH scale (stage 65; [54]). Following an initial curation of the phenological data, two seasons from Cordoba and three from Montpellier were retained for further analysis, resulting in five distinct environments:
Cordoba 2013-2014 (C_14), Cordoba 2014-2015 (C_15), Montpellier 2012-2013 (M_13), Montpellier 2015-2016 (M_16), and Montpellier 2022-2023 (M_23). For each genotype, the mean FFD across replicates within each environment was first calculated, followed by a summary of the phenotypic data using descriptive statistics. Normality was assessed with the Shapiro-Wilk test, and correlations between environments were evaluated using Spearman's coefficient. The FFD variability across the five environments and for all studied genotypes was examined using reaction norms.

The Best Linear Unbiased Predictions (BLUPs) of FFD for each genotype across the five environments were extracted based on a mixed model applied to dataset comprising 315 observations (103 common genotypes + two parents). The model was defined as:

$$P_{ij} = \mu + G_i + E_j + (G \times E)_{ij} + \varepsilon_{ij}$$

Where $P_{ij}$ is the phenotypic value of genotype $i$ in environment $j$, $\mu$ is the overall mean, $G_i$ is the genotype effect of genotype $i$, $E_j$ is the environmental effect of environment $j$, $(G \times E)_{ij}$ is the interaction, and $\varepsilon_{ij}$ is the residual effect.

Variance components were extracted, and the Broad-sense Heritability ($H^2$) for FFD was then computed using the following formula:

$$H^2 = \sigma_G^2 / (\sigma_G^2 + \sigma_{G \times E}^2 / a + \sigma_{\varepsilon}^2 / an)$$

Where $\sigma_G^2$, $\sigma_{G \times E}^2$, $\sigma_{\varepsilon}^2$ represent trait variance of genotype, genotype-by-environment interaction, and residual, respectively, $a$ is the number of studied environments, and $n$ is the number of replicates.

All analyses were performed in R 4.2.1 using basic functions and *lme4* package for mixed model analysis [55].

Climatic data was collected, as daily mean temperatures from both sites, between September of n-1 season and June of *x* season (Figure S2). Temperature data was used to estimate available chill in each environment as chill portions (CP) using the dynamic model and standard parameters with *chillR* package [56].

## 6. QTL mapping analysis

The QTL detection was conducted on FFD BLUPs as well as on the five single-environment datasets using the two parental genetic maps. All analyses were performed using the *R/qtl* package [57]. Missing data was first imputed using the maximal marginal probability (*maxmarginal*) method, which assigns the most probable genotype based on genotype probabilities. Subsequently, *sim.geno()*, implying Hidden Markov model, was used to insert pseudo-markers information at 0.5 cM intervals.

For each parental genetic map, the detection of QTLs was conducted following three major steps, as reported by Spindel *et al.* [58]. First, the standard interval mapping (IM) analysis using Haley-Knott
regression was performed with the *scanone()* function on the whole map, and the LOD threshold to declare significant QTLs was determined through a permutation test (n = 2000, *p*-value < 0.05). Next, each linkage group was independently scanned, and LOD thresholds for individual LG were computed using the same permutation parameters. Following this, the initially detected QTLs were used to build a preliminary model for further analysis using *addqtl()* function. Additional QTLs identified were incorporated into a second model, which was then served for a third step of stepwise analysis (forward selection + backward elimination) through multiple runs using the *stepwiseqtl()* function.

The best model was chosen based on penalty LOD score defined by Manichaikul *et al.* [59], and fitted using *fitqtl()* function. The significant QTLs retained in the final model were characterized by their LOD scores, percentage of variation explained (PVE), the peak position and the flanking markers, while the QTL confidence intervals were estimated as LOD support intervals by a 1.5 LOD drop on both sides of the peak. Additive effects were estimated as the difference in phenotypic means (*a* = *μ*<sub>AB</sub> - *μ*<sub>AA</sub>) for QTLs associated with "lm×ll" or "nn×np" markers. For those associated to "hk×hk" segregating markers, additive effects were estimated as the difference in phenotype means between the two homozygotes (*a* = *μ*<sub>AA</sub> - *μ*<sub>BB</sub>), while the dominance effect was estimated as the difference between the phenotype means of heterozygotes and the midpoint of the two homozygotes as following: *d* = *μ*<sub>AB</sub> - (μ<sub>AA</sub> + μ<sub>BB</sub>/2) [60].

# 7. Identification of putative candidate genes

To explore the biological functions of the genes located within the detected QTLs, the significant retained QTLs were investigated to identify potential candidate genes using the physical genome assembly (Oe9 version). The annotation file "*OE9D.chromosomes.gff3*" available on the Assembly and annotation team database [61] was first parsed into the R environment using the *read.gff()* function of the *ape* package [62], and putative candidate genes co-localized with SNPs within each QTL interval were then determined. All single markers in the defined QTL intervals, including those on pseudo-chromosomes and scaffolds of the genome assembly, were investigated for candidate genes. To describe the functions of the identified genes, the Swiss-Prot database and the most recent relevant literature were consulted.

# Results

## High-throughput sequencing of olive samples

The sequencing, mapping and filtration processes have generated a total of 38,617 SNPs for the 156 olive samples including the 154 hybrids and the two parents. The retained SNPs were distributed on the 23 Oe9_LGs pseudo-chromosomes of the "Farga" cultivar genome (20,264 SNPs, 52.5%) and on 1,862 scaffolds, not assigned to specific LGs (18,353 SNPs, 47.5%). The SNPs located on pseudo-chromosomes varied from 1,521 SNPs for the Oe9_LG3 to 326 for the Oe9_LG23 with an average of 850 SNPs/pseudo-chromosome and an average inter-SNP gap of 25,440 bp. For those located on scaffolds, the number of SNPs ranged from 135 for Oe9_s01922 to 1 SNP for 290 scaffolds with an average of 9.8
SNPs/scaffold and an average gap between SNPs of 14,910.8 bp. The frequencies of missing data per SNP varied from 0 to 4.5%, and per sample from 0 to 32%. Only six accessions among the 156 showed more than 10% of missing SNPs. The density of markers varied from 0 to 113 SNP/Mb with an average of 15.9 SNP/Mb (Figure S3).

# 1. High-density genetic maps construction

Using the 38,617 SNPs and following filtration arguments (--MinCov 8, --MaxCov 2000, --WinFreq 0.05; 0.15, --MinAlCov 3, --miss 0.2) with Vcf2pop.1.0 script, a total of 27,359 loci were retained including 9,853 segregating from the female parent (Im×II), 10,704 from the male parent (nn×np), and 6,802 from both parents (hk×hk), while 1,188 loci showed unknown segregation pattern. A total of 1,443 and 8,627 were excluded based on missing data cutoff (--miss argument) and χ² cutoff (--WinFreq), respectively.

The two parental genetic maps were constructed following missing data filtration (less than 5%) and exclusion of distorted and redundant markers, as well as individuals with more than 5% missing data (17 among the 154 genotypes were removed). The segregation distortion rate was relatively low and almost similar in both male and female parental datasets with only 331 markers and 302 markers, respectively (Table S1). Following exclusion of redundant and unlinked markers, a total of 16,826 SNPs of all three segregation types were positioned on parental maps. Overall, more markers were associated to the segregation pattern of the male parent (nn × np; 6,653 markers; 39.5%) compared to the female (Im×II; 6,117; 36.3%), while 4056 (24.1%) bridge markers were segregating from both parents (hk×hk). The grouping process resulted in 23 linkage groups (LGs), corresponding to the haploid chromosomes number of the olive tree (Figure 2).

Among the tested algorithms, RECORD was chosen for ordering markers regarding its high performance compared to other algorithms (Table S2; Figure S4). Specifically, the RECORD algorithm was more effective for larger LGs with over 600 markers, while other algorithms, i.e. Rapid Chain delineation (RCD) and Unidirectional growth (UG), produced more inflated maps, resulting in an increase of at least 100 cM compared to the RECORD algorithm. Overall, 10,173 and 10,706 loci were mapped and parental maps spanned total length of 5,949.24 cM (mean of 258.66 cM ± 74.82 per LG) and 6,884.69 cM (mean of 299.33 ± 97.87 per LG) for "Olivière" and "Arbequina", respectively. Both maps were characterized by a high marker density, with an average of 1.79 SNP/cM for the "Olivière" map and 1.58 SNP/cM for the "Arbequina" map (Table 1). In the "Olivière" map, the number of mapped loci per LG ranged from 305 SNPs (LG21; 169.63 cM) to 765 (LG01; 487.77 cM), with an average of 442 loci/LG. The LG23 was the densest among the 23, with 3.24 markers/cM, while the LG14 was the least dense, with 0.98 marker/cM. In the "Arbequina" map, LG03 and LG04 contained the highest number of markers, with 744 (396.41 cM) and 749 (413.04 cM), respectively, while the LG05 displayed the least number of markers with only 260 markers 221.86 cM. In terms of density, LG06 was the densest with 2.22 markers/cM, while LG01 was the least dense with 1.01 markers/cM (Table 1). Additionally, this parental map showed three major gaps in LG14, LG15, and LG19, spanning 71.59 cM, 62.13 cM, and 59.09 cM, respectively (Figure 2).
The 23 parental LGs were ordered according to the 23 pseudo-chromosomes of the Oe9 reference genome [32], where each LG assigned based on the dominance of markers from the most represented pseudo-chromosome (Oe9_LG; **Tables S3 & S4; Figure S5**). Overall, 32.4% of the markers in the "Olivière" map and 32.02% in the "Arbequina" map were successfully aligned to their corresponding Oe9 pseudo-chromosomes. The remaining markers were identified on scaffolds, with high proportions, or mapped to other pseudo-chromosomes. In both parental maps, LG23 had the lowest proportion of markers belonging to Oe9_LG23, with 14.9% and 10.8% in the "Olivière" and "Arbequina" maps, respectively, due probably to the low number of SNPs initially identified within this pseudo-chromosome (326 SNPs; **Figure S3**). In conclusion, both parental maps were characterized by strong collinearity with the physical map of the improved Oe9 genome assembly, as evidenced by the dominance of markers from the corresponding pseudo-chromosomes and scaffolds in each LG (**Figure S5**).

## 2. Full flowering date analysis

Full flowering date (FFD) was analyzed across five environments using 105 common genotypes, including the two parents with at least three replicates each (**Table S5**). This trait showed quantitative distribution and transgressive segregation compared to parents in each environment, where some progenies displayed more extreme FFD (either earlier or later) than those of both parents (**Figure 3a, Table S6**). No normal distribution was detected in any environment, as attested by the Shapiro-Wilk's test (*p*-value < 0.05), and the distribution of FFD varied significantly across the five environments studied (**Figures 3a & S5**). Overall, the FFD ranged from 113 to 164.3 Julian days (JD) with a difference of 51.3 days (**Tables S5 & S6**). Within a single environment, the FFD range was approximately 10 days in three environments (M_13, C_14, and M_23), with the highest variability being observed in M_16 (21 days) and the lowest in C_15 (5.56 days). Correlations between environments were low to moderate, ranging from 0.339 to 0.674 and the highest correlation values were observed among environments within each site (**Figure S6**). The reaction norms plot for each of the studied genotypes highlighted a notable variation in FFD across different environments (**Figure S7**). Interestingly, some genotypes with extreme FFD values showed consistent trend (towards extreme values in the population) across different environments, suggesting stable phenotypic expression under varying conditions. This observation highlights the potential influence of genetic factors on FFD stability, even in diverse environmental settings.

The hybrid population was characterized by an early flowering phenotype in Cordoba, with a total average FFD of 118.76 ± 3.11 JD in C_14 and 121.38 ± 2.06 JD in C_15. In contrast, flowering was more delayed in Montpellier across the studied seasons, with total average FFD of 161.3 ± 1.5 in M_13, 145.46 ± 4.63 in M_16 and 143.04 ± 2.02 JD in M_23 (**Figure 3a**). Notably, inter-annual variability in FFDs was observed between the three seasons within the Montpellier site. The estimation of available chill portions (CP) across environments revealed a strong association between available CP and FFD pattern. As displayed by daily mean temperatures distributions (**Figure S2**), Cordoba site was characterized by "warmer winters" compared to Montpellier, resulting in lower chill availability. Flowering tended to be more delayed in environments with extended period of chill availability (**Figure S8**). In Montpellier, where the assessed total chill availability exceeded 100 CP in M_13 and M_16, flowering
dates were delayed (161.3 ± 1.5 and 145.46 ± 4.63 JD, respectively). Conversely, in Cordoba, where the
total available chill was less than 75 CP, flowering occurred earlier in both seasons (118.76 ± 3.11 and
121.38 ± 2.06). Additionally, chill availability also followed distinct seasonal patterns. In Cordoba, chill
availability began in early November and continued until late March, while in Montpellier, it extended until
late April or early May. In M_13, which showed the most delayed FFDs (average 161.3 ± 1.5 JD), chill
availability continued until late May, reaching almost 125 CP, while in M_16 (average of 145.46 ± 4.63
JD), chill availability ended in early May with nearly 105 CP. This trend was validated by observations
from the most recent season of Montpellier site (M_23, 2022-2023), where chill availability ended earlier,
in mid-April, with a total of 80 CP. This was associated with a relatively earlier flowering distribution
within the hybrid population (143.04 ± 2.02 JD; **Figure S2 & S8**).

The Broad-sense heritability ($H^2$) analysis revealed a $H^2$ of 0.77 for FFD, indicating a strong genetic component in the trait's variation. Finally, 105 genotypic effects BLUPs values were extracted from the model and used, alongside single-environment datasets, for further QTL analysis (**Figure 3b, Table S6**).

**3. QTL mapping of Full Flowering dates**

QTL analyses of FFD were conducted on both BLUPs and single-environment datasets across five environments. In total, 106 FFD-associated QTLs were detected across all independent analyses on both parental genetic maps. Notably, over 78% (83 QTLs) were environment-specific, being detected in one environment analysis only, while five QTLs on LG11, LG14 and LG22 of "Arbequina" map, and LG09 and LG12 of "Olivière" map, were detected in two different environments, but not in BLUPs analysis. Among the 106 QTLs, 18 were detected in BLUPs analysis. Two QTLs were co-detected on both parental maps, on LG07 (qFDO7/qFDA7), and LG09 (qFDO9b/qFDA9), and four were detected in both BLUPs and some single-environment analyses, on LG03 (qFDA3), LG13 (qFDA13), and LG22 (qFDA22) of "Arbequina map", and on LG13 (qFDO13) of "Olivière" map (Table 2).

Interval mapping (IM) whole map scanning of FFD BLUPs revealed some LOD profile peaks that co-localized in proximal regions on corresponding LGs in independent single-environment analyses, as the case of QTLs identified on LG09 in both parental maps (Figure S9). However, the genome-wide (whole map) thresholds were too conservative to detect significant QTLs at the defined LOD threshold of *p* < 0.05 (Figure S9). Consequently, single-LG scans were performed with independent LOD thresholds assessment. Following the multi-QTL model established, the identified QTLs were further explored and refined, leading to the detection of additional QTLs. Consistency, QTLs detection across the six conducted analyses (BLUPs + five single-environment datasets) was restrictive, as only six QTLs were effectively considered as overlapped (qFDA3, qFDO7/qFDA7, qFDO9b/qFDA9, qFDA22) or proximate (qFDO13, qFDA13) co-detections by considering their LOD intervals (Table 2; Figures 4).

**3.1. Two QTLs detected on both parental maps: qFDO7/qFDA7 and qFDO9b/qFDA9**

The QTL on LG09 was co-detected in both BLUPs and single-environment analyses, as well as on both parental maps; "Olivière" (qFDO9b) and "Arbequina" (qFDA9). Both qFDA9 and qFDO9b included a
common "hk×hk" segregating marker, "Oe9_s01985M237573", within their intervals. Additionally, they contained very proximate SNPs on the "Oe9_s05855" scaffold: "Oe9_s05855M325901" in *qFDO9b*, and "Oe9_s05855M325942" and "Oe9_s05855M326095" in *qFDA9*. The peak signal in BLUPs analysis is positioned at 162.39 cM on the "Olivière" map and 163.67 cM on the "Arbequina" map. The *qFDA9* detected on "Arbequina" map was characterized with low PVE (5.36% and 4.05%) and moderate positive additive effects (0.61 and 1), in BLUPs and C_15 analyses, respectively. On the "Olivière" map, the *qFDO9b* displayed low PVE (3.04% and 4.82%) and moderate positive additive effects of 0.75 and 1.43, in BLUPs and C_15 analyses, respectively. However, in C_14 environment, *qFDO9b* showed a high PVE (17.69%) and an interesting negative additive value (-2.44).

On the "Olivière" map, the *qFDO7* was detected on both BLUPs and C_14 analyses. This QTL was associated with a "hk×hk" segregating marker "Oe9_LG22M5170657", which was also part of the QTL detected on "Arbequina" map (*qFDA7*). On "Olivière" map, the QTL was detected in the 232.27-234.94 cM region, and was characterized by a high PVE in BLUPs analysis (33.11%) compared to a low one in C_14 analysis (2.55%). On the other hand, a negative additive effect was observed in both analyses, with a higher absolute value in C_14 (-2.18), in addition to a negative dominance effect (-1.16). On "Arbequina" map, the QTL was positioned in the 256.5-263.08 cM in BLUPs analysis, and was characterized by a low PVE value (6.14%), and negative additive (-0.87) and dominance effects (-0.49).

## 3.2. Consistent QTLs on "Arbequina" map: *qFDA3* and *qFDA22*

On the "Arbequina" map, two QTLs were co-detected in BLUP and single-environment analyses on LG03 (*qFDA3*) and LG22 (*qFDA22*). The *qFDA3* peak is positioned at 270 cM in BLUPs and 269.17 cM in C_14 analyses. This QTL showed a relatively lower PVE (6.47%) and a moderate positive additive value (0.83) in BLUPs compared to C_14, where it showed a high PVE (17.01%) and a negative additive effect (-1.38). Additionally, a QTL on LG03 was identified in the M_13 analysis (Table 2, Figure 4), with a peak position at 265.43 cM. Although proximate to the *qFDA3* detected in BLUPs and C_14, this QTL had a narrower and non-overlapping interval. It was characterized by a higher PVE of 28.08% and a positive additive effect of 0.68. On the LG22 of "Arbequina" map, the *qFDA22* identified in BLUPs was only co-detected in M_16 in the 248.97-250.86 cM region, while multiple other distant detections were spotted on the same LG in different environmental analyses. This QTL displayed moderately high PVE values (11.66% in BLUPs and 12.57% in M_16), and high positive additive effects (1.15 in BLUPs and 2.53 in M_16).

## 3.3. Two QTLs (*qFDO13* and *qFDA13*) detected on LG13 on both maps with relatively high additive values

A QTL on LG13 of the "Arbequina" map, located in the region 264.36-267.2 cM, was characterized by a PVE of 14.67% and a relatively high absolute negative additive value (-1.09) in BLUPs analyses. In M_16 environment, a QTL was detected in a proximate region on the same LG13 (258.03-260.85 cM) and showed almost similar PVE (14.38%) and a higher negative additive value (-3.32). On the "Olivière" map, the *qFDO13* detected in BLUPs analysis, located between 201.66 and 203.36 cM, was characterized by a relatively high positive additive value (0.91). Interestingly, two closely detected QTLs flanking the
qFDO13 region - one in M_16 (191.69-193.54 cM) and another in C_14 (212.02 - 214.82 cM) - showed even high additive effects of +1.82 and -1.96, respectively.

Segregating marker type (hk x hk) with the three genotypes (AA, AB, and BB) were used to calculate additive (*a-values*) and dominance effects (*d-values*). In BLUPs analysis, a total of 17 (hk x hk) markers were linked to the 18 QTLs (11 on "Arbequina" map and 6 on "Olivière" map). Results showed an overall dominance pattern among the identified FFD QTLs **(Table 2)**. This pattern is supported by higher additive values, reflecting the differences between the mean phenotypic values of homozygous genotypes (AA and BB), as well as higher differences between the mean phenotypic value of the heterozygous genotype (AB) and the midpoint phenotypic value.

## 4. Putative candidate genes of Full Flowering Date regulation

The six most consistent and significant QTLs (*qFDO7/qFDA7*, *qFDO9b/qFDA9*, *qFDA3*, *qFDO13*, *qFDA13*, and *qFDA22*), were prioritized for putative candidate genes investigations. All SNPs within these QTLs were examined, including those mapped to their corresponding pseudo-chromosomes of the physical reference genome, as well as those located on mismatched pseudo-chromosomes and scaffolds. In total, 163 SNPs were examined, of which 39 were classified as intergenic based on the Oe9 genome annotation. The remaining 124 markers were located within genes, including 69 in exonic and 55 in intronic regions (**Table S7**). These SNPs spanned 86 annotated genes (including 83 unique genes), with 23 mapped within the *qFDA13* interval, 17 within *qFDA3*, 14 within *qFDO13*, 20 within (*qFDA9/qFDO9b*), 10 in *qFDA22*, and 2 within (*qFDO7/qFDA7*) (**Tables S7 & S8**).

Gene Ontology (GO) analysis of the 83 genes revealed functional diversity across multiple subcategories within the three main GO categories (**Table S9**). In the molecular function category, ATP-binding, metal ion binding, kinase activity, RNA binding, DNA binding, and transcription factor activity were the most represented functions, encompassing 39 genes. In the biological process category, phosphorylation and regulation of DNA-templated transcription were the predominant subcategories, with eight and seven genes, respectively. Meanwhile, nucleus and membrane were the most enriched subcategories within the cellular component category. Functional annotation identified seven key genes within QTL regions as potential candidates linked to the genomic mechanism of flowering, one in *qFDA3*, two in *qFDA9*, one in *qFDA22*, two in *qFDA13* and one in *qFDO13*. These genes showed homology with known flowering-related genes, with sequence similarity ranging from 40.9% to 71% based on UniProt BLAST analysis. The identified candidates encode transcription factor-like proteins, including a *WRKY DNA-binding protein, Homeobox-DDT domain protein RLT3, FREE1-like*, and *ABI5* (*ABSCISIC ACID-SENSITIVE 5-like protein 5*, **Table 3**).

## Discussion

Flowering date is one of the critical phenological stages in the annual cycle of perennial trees including olive, as it directly impacts fruit production and overall crop yield [4]. Thus, understanding the biological and genetic basis controlling this trait is particularly relevant and of great interest, especially in the
context of global warming. Here, we discuss our results on QTL mapping of FFD regarding the relevance
of the constructed parental genetic maps, the impact of climate conditions on FFD variation, and the
identified QTLs and candidate genes.

High-density genetic maps as relevant tool for QTL mapping and reference genome anchoring

The present study capitalized on advances in NGS technology to develop highly saturated genetic maps and identify QTLs linked to FFD in the olive tree. Here, two parental high-density genetic maps were constructed with more than 10k markers, making then, to our knowledge, the densest genetic maps developed to date for olive tree. The first high-density olive genetic map, constructed using GBS-based SNPs, contained 1,597 SNP markers, spanning 1,189.6 cM across 23 LGs [37], whereas other subsequent published high-density genetic maps spanned the range from 3,049 cM [38] to 5,679.9 cM [38-40] (Table S10). While larger sizes of published high-density olive maps were constructed with the use of high number of mapped markers, the number of markers alone does not directly drive map size inflation. Mariotti et al. [40] constructed a femelle parent map of "Leccino" cultivar with 7,006 SNPs (5,679.9 cM), while the male parent map of "Dolce agogia" contained 9,737 SNPs (3537.7 cM). In two other studies on the "Gemilik x Edincik su" F1 progeny, 5,643 and 3,404 uniquely positioned SNPs were distributed on maps spanning 3,049 cM and 3,340.8 cM, respectively [38-39]. This can also be spotted in our results at the LGs level. For example, one of the densest LG on "Olivière" map, OLG23 (161.92 cM), contained 525 markers with a density of 3.24 SNP/cM, whereas OLG14 (357.76 cM) had only 350 markers (175 fewer) and a lower density of 0.98 SNP/cM only. Increased rates of missing data and genotyping errors can significantly impact map sizes [63]. Here, we were able to distinguish the effect of missingness on map size by trying different filtration thresholds before fixing 5%. Additionally, observed gaps, regions with low genome coverage, further contributed to map size inflation. Such gaps may arise due to dataset-related factors, including missingness and genotyping errors, but can also result from the choice of restriction enzymes used in library construction. While restriction-based sequencing effectively reduces genome complexity, even enzymes with widely distributed recognition sites may fail to cover certain genomic regions, leading to insufficient marker representation [64]. Another key-factor is the choice of the ordering algorithm. Defining the optimal marker order is a challenging and computationally intensive task, particularly when dealing with thousands of markers, as the number of possible arrangements increases exponentially. The RECORD algorithm is a commonly and widely used method for marker ordering, especially when handling a high-density dataset [65]. By comparing three ordering algorithms—Rapid chain delineation, Unidirectional Growth (UG), and RECORD—our results showed that UG also performed well alongside RECORD algorithm. However, this latter was notably more effective in ordering the densest linkage groups by reducing their size (see Table S2 and Figure S4) through the minimization of the COUNT criterion, which represents the estimated number of recombination events. In evaluations of ordering algorithms, RECORD has been revealed as particularly more suitable for high-density maps (LGs with more than 500 markers) and as being less sensitive to missingness compared to other algorithms [65].
The availability of a high-quality assembled olive reference genomes remains an ongoing research effort, which may limit the ability to fully investigate the genetic architecture of agronomic traits of interest. With advances in long-read sequencing-based technologies such as PacBio and Oxford Nanopore [66], the quality and integrity of olive genome was enhanced [35-36]. The use of genetic maps can also serve as a valuable complementary tool, integrating contig assembly with recombination data to enhance genome accuracy [67-69]. Beyond genome refinement, an improved olive reference genome would facilitate functional genomics studies, gene annotation, and trait mapping. This advancement would be particularly beneficial for breeding programs, enabling genomic selection for key agronomic traits such as flowering date, disease resistance, fruit size and oil quality. In this study, we assessed the collinearity between SNP markers mapped onto LGs and their corresponding positions in the *Oe9* olive reference genome. Although the distribution of markers across the constructed LGs did not perfectly align with their physical positions in the *Oe9* assembly, all 23 pseudo-chromosomes were represented in our maps, with most markers mapping to their corresponding pseudo-chromosomes (see Tables S3 & S4; Figure S5). Despite the significant advancements represented by the *Oe9* genome of "Farga" cultivar and the improvements made using an F1-based genetic map (3,384 SNPs; [33]), the proposed anchoring still contained misassembled regions and unmapped scaffolds. Here, with more than 10k markers well distributed across the genome, the high-density maps developed may provide a valuable opportunity to further refine the genome assembly by anchoring the 23 chromosomes and improving genome accuracy.

## Inter-annual variation of flowering date in olive tree are governed by temperature trends: Insights into GxE Interactions

In this study, the FFD showed a transgressive segregation pattern within the studied progeny, a finding commonly observed for the same trait in intercross populations of fruit trees [70-72], which is mainly attributed to the high level of heterozygosity of outcrossing species, including olive tree [73-74]. The segregation patterns may also explain the non-normal distribution of the FFD, despite its quantitative nature. This was also reported in different hybrid populations of *Rosaceae* trees such as apricot [70] and sweet cherry [71], where bloom dates displayed non-normal and skewed distributions with transgressive patterns favoring one parental phenotype. Such distribution trends may result from complex genetic interactions, including epistatic effects and the contribution of multiple loci with varying degrees of dominance and additive effects. Moreover, our results highlighted a relatively high heritability ($H^2$ = 0.77) observed for FFD which aligns with finding from a population of unrelated olive genotypes ($H^2$ = 0.75), as reported by Abou-Saaid et al. [13] and Aqbouch et al. [44]. The high value of heritability in our study is in accordance with other studies carried out on *Rosaceae* fruit trees targeting phenological stages—Bloom date, Beginning and End of flowering—with values ranging from 0.67 in apricot [75] to 0.96 in sweet cherry [71-72].

Similarly to many other quantitative traits, flowering date is highly influenced by environmental conditions, particularly temperature variations as experimentally demonstrated by López-Bernal et al. [14]. Here, the two studied sites, Cordoba and Montpellier, are characterized by contrasting climatic
conditions due to their geographical locations (see Figure 1). Cordoba, located much closer to the southern rim of the Mediterranean, is characterized by a hotter and more continental Mediterranean climate compared to Montpellier site. This contrasted geographic locations largely explain the different climatic profiles observed across the studied seasons, with Cordoba displaying a relatively "warmer winters" and lower chill accumulation compared to Montpellier. Accordingly, we evidenced a clear pattern of flowering date, early and delayed phenotype in Cordoba and Montpellier, respectively. Moreover, differences in flowering date distributions between sites in olive was reported through airborne pollen-based approach comparing three locations Toledo (Spain), Lecce (Italy) and Chaal (Tunisia) [76], but also by investigating the "Picholine marocaine" cultivar in Morocco and France [3].

Here, the difference in total of available chill (CP) is notable between seasons at both sites. Prolonged chilling periods, with higher available chill, were associated to late flowering dates. One way to address these inter-environmental differences is through the concept of "ecodormancy" [77]. While "endodormancy" refers to the physiological dormancy phase where bud development is inhibited by internal regulatory mechanisms and requires specific amount of CPs to be released, "ecodormancy" corresponds to the phase following chill fulfillment, during which buds are physiologically ready to bloom and budburst remains delayed until favorable temperatures are reached [78]. The delayed flowering dates observed in Montpellier compared to Cordoba may thus be attributed to a prolonged "endodormancy" and delayed "ecodormancy" phases, driven by extended cold winters. Similarly, inter-annual flowering date variations within the same site can also be explained by differences in chill availability between seasons. These findings support the inter-annual temperature variations driven by the global warming trend characterizing the Mediterranean region and highlight the significant impact of winter warming in reducing available chill as reported by Fernandez et al. [6] using projection' models for the region.

The impact of the environmental conditions on genotype flowering responses, including FFD and flowering quality, has been documented in previous studies on the olive tree [10,13]. Here, while low to moderate correlations were observed between environments (ranging between 0.34 and 0.64), some genotypes with extreme flowering dates (either early or late) in one environment seemed to be more stable, as highlighted by their mean FFD values and reaction norms. Moreover, the evaluation of different genotypes in this study showed differences in their FFD stability across environments as the case of "Arbequina" *cv.*—male parent and an early flowering cultivar [13]—showed notable FFD variability (see Figure 3). This result aligns with Navas-Lopez *et al.* [10] study, who reported that the "Arbequina" *cv.* displayed significant flowering variability compared to other cultivars under changing climatic conditions. Our study highlights the critical role of genotype × environment (G × E) interactions in shaping the flowering behavior of the olive tree, with significant variability observed in FFD across different environmental conditions. These findings underscore the importance of integrating G × E interactions in QTL mapping studies and demonstrate the utility of BLUPs in enhancing the reliability of QTL detection.

**_Olive flowering date is under a complex polygenetic determinism_**
Previous QTL studies on olive tree have targeted agronomic traits such as yield, fruiting and flowering traits, and oil composition, using DArT- and SSR-based genetic maps with 257 to 450 markers [28-30]. More recently, high-density linkage maps have been used for QTL mapping in olive tree for oil composition [42], tree vigor [41] and frost tolerance [43]. To our knowledge, this study is the first to apply high-throughput GBS-SNP-based maps for QTL mapping of the flowering date trait.

Interval Mapping (IM) analysis revealed notable peaks in the whole-map scans performed on both parental genetic maps. However, the significance threshold initially defined for the genome-wide scan was too conservative to declare significant associations at the LOD threshold (*p*-value < 0.05). As a result, we performed independent IM analyses for each linkage group and redefined significance thresholds, focusing on initially observed peaks through the whole-map scan. Higher genome-wide thresholds may increase errors of false negative for QTL detection due to multiple factors. Van Ooijen [79] highlighted the effect of marker density, the number and the length of LGs on the defined LOD threshold, where higher threshold values were obtained in larger average LG values (>250 cM). This may explain the absence of significant associations at the genome-wide with a 5% threshold in our study by handling high-density maps (average LG length of 258.6 ± 74.8 cM and 299.33 ± 97.8 cM for Olivière and Arbequina, respectively). The significantly revealed QTLs in independent LGs IM scans were used to build an initial model—used as cofactors—for investigating additional QTLs and refining their positions. The stepwise approach consisted of a forward selection step up to a maximum of 10 QTLs, followed by a backward elimination step to conserve the best model with the higher pLOD [59]. Following this approach, we detected 106 significant QTLs across all conducted analysis. The high number of QTLs identified can be attributed to both the use of high-density genetic maps and the low size of the mapping population used (105 genotypes) [80]. This may have increased multicollinearity between markers, resulting in the identification of multiple minor QTLs.

Most of the 106 QTLs were identified as environment-specific observed in one analysis (single-environment analysis), and also specific to one parent. Several studies in perennial fruit trees have successfully identified QTLs linked to flowering date using parental maps including sweet cherry [81-82], grapevine [83], peach and apricot [75]. This demonstrates that QTL mapping using parental maps is a well-established and effective approach. Here, the crossed parents "Olivière" and "Arbequina" have significant genetic background differences, belonging to distinct Mediterranean gene pools, western and central, respectively [84]. This difference results in different recombination in both parents, which can be observed through the differences in overall map size between "Olivière" and "Arbequina", with the latter being larger with 935.45 cM. Thus, analyzing parental maps separately may provide more accurate identification of QTLs segregating from each parent, where a total of 16 out of 18 QTLs detected for FFD BLUPs, were specific to one parent. While only QTLs with overlapping intervals were considered as co-detections, multiple single-environment detections were positioned around the main QTL position in BLUPs analysis. For instance, *qFDA13* was detected in the 264.36-267.2 cM region on LG13 of "Arbequina" map in the BLUPs analysis and in the 258.03-260.85 cM region on the same LG in the M_16 environment. One possible explanation for the lack of complete overlap of QTLs across different analyses and parental genetic maps is the relatively narrow confidence intervals of most QTLs,
averaging 3.27cM. However, exceptions were observed such as BLUP *qFDA9q* (24.8cM) and BLUP *qFDA14* (12.5cM; **Table 2**). In contrast, flowering date QTLs identified in many perennial trees tend to have relatively larger average intervals, ranging from 3.3 to 151.2 cM in a study on sweet cherry [71], and with an average of 13 ± 6.43 cM in a grapevine study [83]. It is worth noting that these studies used genetic maps with fewer than 400 markers/map, with average marker distances of 5 cM and 4 cM, respectively. Therefore, the narrower QTL intervals observed in our study may be attributed to the use of both high-density genetic maps in addition to a 0.5 cM step in IM analysis.

Despite co-detection confirmed in terms of map position, most of QTLs displayed varying PVE and additive values between the different analyses. The estimations of these parameters for the same QTL can vary significantly among multiple analyses mainly due to environmental effects. Ideally, QTLs of interest should show high PVE and large additive effects, reflecting a substantial portion of the phenotypic variance with significant differences in mean phenotypic values across different genotypes. In sweet cherry, a major QTL of flowering date control on LG R4, highly stable across environments, was characterized by a higher average PVE and QTL effects values across multiple analyses [75,81-82]. Interestingly, the *qFDA22*, revealed in our study, displayed both high PVE (11.66% and 12.57%) and positive additive effects (+1.15 and +2.53) in BLUPs and C_15 analyses, respectively. Similarly, the *qFDA13* displayed the same pattern despite not being strictly considered co-detected, with relatively higher PVE values (14.67% and 14.38%) and negative additive effects (-1.09 and -3.32) in BLUPs and M_16, respectively.

In olive, QTL mapping of other flowering-related traits (number of inflorescences) revealed multiple QTLs, with most of them being year-specific despite moderate to high heritability values exceeding 0.7 [28]. In this study, despite the high heritability value assessed for flowering date in olive ($H^2$ = 0.77), most QTLs were not co-localized in single-environment analyses, which is likely due to the nature of the targeted trait, being highly impacted by environment conditions and "G x E" interactions. Investigations into the genetic basis of flowering date in fruit trees have highlighted the complexity and polygenic nature of this trait across many species, including sweet cherry [71-72, 81-82], peach [75, 85], apple [86], apricot [70, 75], and grapevine [83]. Flowering time and bud phenology traits exhibited high heritability ($H^2$ > 0.7) in apple and peach but were controlled by multiple QTLs with variable detection across years and environments [70, 86]. Moreover, in grapevine, despite high heritability estimates for flowering-veraison period ($H^2$ = 0.92), QTLs detected were often year-specific or explained a low proportion of the phenotypic variance [87]. These studies point out the instability of many identified QTLs linked to flowering date across multi-environmental analyses. Such patterns suggest that while genetic factors play a significant role in determining flowering-related traits, their expression is strongly modulated by environmental factors and G×E interactions, leading to a low degree of QTL stability across different conditions. This finding emphasizes the importance of using multi-environment analyses for QTL detection, especially for quantitative traits significantly influenced by environmental factors, and the use of genotypic effects (BLUPs) derived from multi-environment phenotypic data. Given the G×E interaction that characterizes flowering date, further investigations—such as fine mapping, Meta-QTL analysis, or
QTL-by-environment analyses as conducted in sweet cherry [72, 81-82]—are required to confirm the identified QTLs. Investigation of the GxE component can also be explored by targeting different variables, such as GxE BLUPs or slopes derived of reaction norms. Additionally, QTL mapping can serve as a valuable complement to genome-wide association studies (GWAS), offering a more comprehensive and accurate understanding of the genetic determinism of flowering date in olive trees [44].

## New insights into mediated genes regulation of FFD in olive tree

Flowering is a chronological event regulated by multiple internal regulations in response to diverse environmental stimuli. In the model species *A. thaliana*, the genetic control of flowering time has been widely studied, revealing different but interconnected regulation pathways involving multiple genes [88]. In perennial fruit trees, QTL mapping for flowering date and functional genomics studies has helped identify key genes involved in flowering regulation [18-24, 70-72, 81-82]. However, in olive tree, the molecular mechanism governing flowering remains poorly understood, with only a few studies addressing this topic. Alagna et al. [89] investigated transcriptomes from different olive tree organs, including flowers, and identified candidate genes involved in regulating floral meristem development using RT-qPCR. Additionally, an overexpression of FT gene olive homologs (*OeFT1* and *OeFT2*) at the end of winter or under artificial cold temperature was followed by inflorescence initiation [25].

Here, the investigation of key QTLs revealed genes encoding for potential transcription factors that interact with main flowering regulators such as *FLOWERING LOCUS T (FT)* or *FLOWERING LOCUS C (FLC)*. Among them, the "*OLEA9_A011887*" gene, identified within the *qFDA22* interval, encodes for *Probable WRKY transcription factor 71*. This gene belongs to the WRKY transcription factor family, which binds to *W-box* sequences (5'-(T)TGAC[CT]-3') and plays roles in various biological processes, ranging from abiotic and biotic stress responses to developmental regulation [90]. Interestingly, a member of this family, *WRKY71*, was revealed as linked to direct positive promotion of flowering. Mutants with *WRKY71* overexpression showed an early flowering phenotype compared to the wild type, with increased expression of major flowering integrators such as *FLOWERING LOCUS T (FT)* and *LEAFY (LFY)* [91]. In the same study, *WRKY71* was shown to directly interact with these flowering integrators by binding to *W-box* regions within their promoters. The identified gene also showed homology with *A. thaliana* *WRKY28* (40.9%) which was shown to be involved in ovule development [92].

Another gene, "*OLEA9_A008757*", identified within the *qFDA3*, encodes for a *DDT domain-containing protein* and showed high sequence homology with several homologs in other plant species: 66.4% with *Sesamum indicum* LOC105161282 (*Homeobox-DDT domain protein RLT3 isoform X1*), 59.3% with *Coffea arabica* (LOC113725603, *Homeobox-DDT domain protein RLT3-like*), 54.6% with *Vitis vinifera* (VIT_19s0085g00350, *Homeobox-DDT domain protein RLT3-like*) and 48.1%, with *A. thaliana* (At4g12750, *Homeobox-DDT domain protein RLT3*), respectively. All these homologies were detected with an extremely low e-value, indicating strong sequence similarity. In *A. thaliana*, *RLT3* is inferred to be involved in controlling the vegetative-to-reproductive transition. Its homologs, *Homeobox-DDT domain protein RLT1* and *RLT2*, were shown to maintain the vegetative state, as double mutants for
these genes displayed early flowering, likely due to the downregulation of the flowering repressor FLC and upregulation of the flowering integrator FT [93].

Within *qFDA13*, another gene, "*OLEA9_A051925*", was identified, encoding a *FT-interacting protein 1* (*FTIP1*) with 66.8% of homology, and a very low e-value, with the *MCTP6* in *A. thaliana*. The *FTIP1* belongs to a protein family with multiple C2 domains and transmembrane regions, known as *MCTPs*. It is an endoplasmic reticulum membrane protein that plays a key role in FT transport in *A. thaliana*. Under long-day conditions, *ftip1*-mutants exhibited a late flowering phenotype compared to wild-type plants [94]. The regulatory role of flowering time by *FTIP1* (*MCTP1*), along with the putative implication of *MCTP6*, were both highlighted by double mutants *ftip1-mctp6*. These mutants showed significantly late flowering than both single mutants (*ftip1* or *mctp6*), which also displayed late flowering compared to wild-type plants. This suggests that *FTIP1* and *MCTP6* proteins interact to regulate the flowering date in *A. thaliana* [95].

It is well known that phytohormones play critical roles in physiological regulation processes, including flowering control [96]. In this study, the gene "OLEA9_A075379", identified within *qFDO13* QTL, was predicted to encode *ABSCISIC ACID INSENSITIVE-5 (ABI5)*, a phytohormone-related transcription factor. ABI proteins interact specifically with gene promoters containing *ABA-responsive element (ABRE)*, regulating various physiological processes, including responses to stress [97]. In *A. thaliana*, several ABI genes have been shown to influence flowering time. For instance, the inhibition of *ABI3* expression through a brassionsteroid-mediated response, by the *BES-1*, resulted in flowering promotion [98]. Meanwhile, *ABI4* revealed to be directly interacted with *FLOWERING LOCUS C (FLC)*, a key flowering inhibitor. Studies have demonstrated that *abi4*-mutants showed low *FLC* expression and an early flowering phenotype, whereas *ABI4* overexpression increased *FLC* expression and delayed flowering [99]. Similarly, *ABI5* was previously revealed to be involved in the positive regulation of *FLC* [100]. It is important to note that the *FLC* is a main regulator in the vernalization pathway in response to chilling during winter seasons in *A. thaliana*, with a direct association to chill accumulation [15]. *FLC* homologs or *FLC*-like genes identified in perennial fruit trees, were shown to follow similar expression dynamics during winter dormancy period. Additionally, the involvement of *ABI5* and other *ABI5s* in the *FLC*-mediated regulation of flowering during winter dormancy have been highlighted in apple [101] and sweet cherry [102].

Within *qFDA9*, a gene encoding a *FREE1*-like protein was found to negatively impact the regulating activity of *ABI4* and *ABI5* in *A. thaliana*, thus affecting the ABA signaling process for flowering inhibition [103]. Additionally, "*OLEA9_A029109*", encoding an Aspartate aminotransferase, was identified within the same QTL *qFDA9*, showing 71% homology with *A. thaliana* At1g80360 (*Aromatic aminotransferase ISS1*). It was shown that the *ISS1/VAS1 aromatic aminotransferase* is involved in auxin and ethylene content control and its homeostasis maintenance, thus influencing these phytohormones distribution in plant organs [104-105]. These findings suggest that genes within *qFDA9* may contribute to flowering regulation through phytohormonal interactions, particularly via ABA, auxin, and ethylene-mediated pathways.
Within the *qFDA13* QTL, a gene encoding a probable pectinesterase was identified, showing 42.9% sequence homology to *A. thaliana* putative pectinesterase/pectinesterase inhibitor 24. Pectinesterases and their inhibitors are involved in pectin modulation through demethylesterification, a crucial process for cell wall remodeling [106]. It was proposed that such proteins may also be involved in developmental stages such as floral initiation, through observations of *A. thaliana* induced expression of *Pectine methylesterase inhibitor 3 (PMEI3)* on floral organ primordia development [106]. A recent study on sweet osmanthus (*Oleaceae* family) explored the impact of DNA hypomethylation on flower opening. Among the significantly regulated genes, pectinesterase/pectinesterase inhibitors were among the most enriched and highly significant during early flower opening [107]. Interestingly, a probable pectinesterase/pectinesterase inhibitor was defined in the list of candidate genes following GWAS analysis of the flowering date in olive [44], suggesting their potential involvement in flower development and regulation.

Finally, within *qFDA9*, another gene encoding a probable *SCARECROW*-like 4 transcription factor was identified, which is putatively involved in plant developmental processes. This finding aligns with a QTL mapping study on blooming date of multiple *Rosaceae* species, where *SCARECROW* was identified as a strong candidate gene within a high PVE QTL [73].

The identification of these candidate genes, which have been experimentally studied in other species— particularly in *A. thaliana*—suggests their potential role in regulating flowering date in olive tree. Future research integrating GWAS and functional genomics studies could provide deeper insights into their contributions to flowering date control in olive tree.

**Conclusion & perspectives**

The use of genetic mapping studies to decipher the genomic basis of quantitative agronomic traits of interest remains at early stage in the olive tree despite its socio-economic, cultural and genetic diversity importance. The current study aimed to address this knowledge gap by applying QTL mapping approach to unravel the genetic basis of FFD. Our results highlighted the complex genetic control of this trait, which is highly influenced by environmental factors, particularly temperature variations. Although most detected QTLs were unique to specific analysis, we were able to identify six key QTLs with consistent co-detections. Investigation of these QTLs showed putative transcription factors such as *WRKY71*, *ABI5*, and *RLT3*, which may be associated with flowering date control through interactions with main flowering regulators such as *FT* and *FLC*.

Our results provide new insights into genetic control of flowering date in the olive tree, which still requires more investigations through combination of complementary approaches such as QTL mapping, GWAS and candidate genes-based qPCR. Moreover, experimental-based approaches, integrating transcriptomic and functional genomics studies, may offer additional valuable knowledge to further elucidate the effects of temperatures on genes expression. Advancing our understanding of the
biological and genomic factors regulating flowering will ultimately support olive breeding programs
through genomic selection, particularly in the context of ongoing global warming.

**Abbreviations**

**ABI:** ABSCISIC ACID-INSENSITIVE

**BLUP:** Best Linear Unbiased Predictors

**C_14:** Cordoba season 2013/2014

**C_15:** Cordoba season 2014/2015

**cM:** CentiMorgans

**CP:** Chill Portions

**DNA:** Deoxyribose Nucleotide Acid

**FFD:** Full Flowering Date

**FLC:** Flowering locus C

**FT:** Flowering locus T

**GBS:** Genotyping-By-Sequencing

**GWAS:** Genome-Wide Association Study

**IM:** Interval Mapping

**JD:** Julian Day

**LG:** Linkage Group

**LOD:** Logarithm Of Odds

**M_13:** Montpellier season 2012/2013

**M_16:** Montpellier season 2015/2016

**M_23:** Montpellier season 2022/2023

**NGS:** Next Generation Sequencing

**OeAG-1:** *Olea europaea* AGAMOUS 1
**OeAP1-1:** *Olea europaea* APETALA 1-1

**OeFT1:** *Olea europaea* Flowering locus T 1

**OeFT2:** *Olea europaea* Flowering locus T 2

**PCR:** Polymerase Chain Reaction

**PVE:** Percentage of Variance Explained

**QTL:** Quantitative Trait Loci

**SNP:** Single Nucleotide Polymorphism

# Declarations

## Ethics approval and consent to participate

Not applicable.

## Consent for publication

Not applicable.

## Data availability

Raw sequences data are available in ClimOliveMed; GBS data of an F1 hybrid population "Olivière x Arbequina" of olive; European Nucleotide Archive; 2025-04-20; PRJEB88732. Genotypic data used for mapping in this study is publicly available on Figshare at https://doi.org/10.6084/m9 figshare.28804433.v1

## Competing interests

The authors declare that they have no competing interests.

## Funding

Laboratory cost (DNA preparation, library construction and sequencing), as well as the grant supporting AEB during his research work at the AGAP Institute, were funded by the European Bioresources for Oliviculture (BeFOre) project (2015-2019, grant agreement n. 645595), H2020-MSCA-RISE-Marie Skłodowska-Curie Research and Innovation Staff Exchange program. OL received a PhD scholarship through the ClimGenOlive project, funded by the Hassan II Academy of Sciences and Technology and the Ministry of Higher Education, Scientific Research and Innovation, Morocco. During his research work at the AGAP Institute, OAS received a grant partially supported by the Initiative TSARA/INRAE. This study was also supported by the Freeclimb project (PRIMA; call 2018) and the Labex AGRO initiative (2011-
LABX-002), as well as the ClimOliveMed project (n° 2003-001) under I-Site Muse framework, coordinated by Agropolis Fondation.

## Authors' contributions

Research design: AEB, CEM and BK. Laboratory experiment design: AEB, BK and PM. Laboratory work and Genotypic data acquisition: AEB and PM. Resources, phenotypic data acquisition: LL, MD and BK. Phenotypic data curation: OL, OAS, BK and AEB. Genotypic data curation: OL, AEB, OAS, GS, GD and PM. Data analysis: OL and AEB. Interpretation of the results: OL, AEB, BK, LL, HZ and PC. Funding acquisition: BK, AEB and CEM. PhD supervision: AEB, BK and CEM. Project administration: BK, CEM and AEB. Writing the first draft: O.L. Complementary writing: AEB, BK, PC and LL. Review & editing: all authors.

## Acknowledgments

The experimental setup was established in Montpellier and Cordoba as part of the collaboration agreement between IFAPA and INRAE for cooperation research on olive breeding. We thank the members of IFAPA and the DiaScope experimental unit at INRAE Montpellier for managing this experimental layout, and the AFEF team within AGAP Institute for supporting the management costs of plant material in Montpellier. Authors are grateful to Angelina Belaj, Raúl De La Rosa, Sebastien Martinez and Bruno Bernazeau for their assistance with phenotyping. Molecular genotyping was realized at the regional genotyping technology platform, AGAP Institute CIRAD, and data analyses were performed with the support of MESO@LR-Platform at the University of Montpellier and the bioinformatics group of the UMR AGAP Institute, a member of the French Institute of Bioinformatics (IFB)—South Green Bioinformatics. The authors extend their gratitude to the staff of both platforms for their technical support and assistance.

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## Tables

Table 1 to 3 are available in the Supplementary Files section.

## Figures

<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>

Map showing the location of the studied hybrid population at two sites IFAPA Cordoba (37.859, -4.798) and Montpellier (43.610, 3.976), along with the spatial distribution of the bioclimatic variable BIO3 (Isothermality) trends for temperatures extracted from the WorldClim version 2.1 climate data for the period 1970-2000. The BIO3 is expressed as a percentage (%) and represents the ratio between the mean diurnal temperature range (BIO2) and the annual temperature range (BIO7 = BIO5 - BIO6) for a
specific period. The index quantifies temperature stability within a region by comparing daily fluctuations to annual variations. Higher values (e.g. the Cordoba site) indicate more stable temperatures throughout the year, whereas lower values reflect more extreme variations (Montpellier). BIO2 = Mean of monthly (max temp - min temp). BIO5 = Max. Temperature of Warmest Month. BIO6 = Min Temperature of Coldest Month.

<figure><img src="image_4.png" /><figcaption>**Figure 2**</figcaption></figure>

High-density SNP-based parental genetic maps of "Olivière" and "Arbequina" parents, with 23 linkage groups each. Linkage group sizes are given in centiMorgans (cM). Markers are represented as black lines, with three large gaps highlighted on LG14, LG15, and LG19 in "Arbequina" map.
<figure><img src="image_5.png" /><figcaption>Figure 3</figcaption></figure>

Full Flowering date (FFD) distribution as Julian days (JD) in the F1 population studied. (A): variation in mean genotype values (including parents) across the five environments; M_13 (Montpellier 2013), C_14 (Cordoba 2014), C_15 (Cordoba 2015), M_16 (Montpellier 2016) and M_23 (Montpellier 2023). (B): Distribution of adjusted-mean Best Linear Unbiased Predictors (BLUPs) for FFD. The data follows a normal distribution, as indicated by the Shapiro-wilk test (p-value > 0.05). parental values are marked with red and green arrows for "Olivière" and "Arbequina" parents, respectively.

<figure><img src="image_6.png" /><figcaption>Figure 4</figcaption></figure>
Genomic Positions of the FFD QTLs detected in both parental genetic maps across five linkage groups (LG) of interest. LGs sizes are given in centiMorgans (cM). Markers are represented as black lines. Key QTLs detected through BLUPs analysis are shown in green bars, while their co-detections in single-environment analysis are given in black. QTLs are represented within their defined intervals, delimited by flanking markers.

## Supplementary Files

This is a list of supplementary files associated with this preprint. Click to download.

* Table13.docx

* additionalfile1.pdf

* additionalfile2.pdf