Stella Biabiany

Emilie Araou

Fabien Cormier

Guillaume Martin

Françoise Carreel

Catherine Hervouet

Frédéric Salmon

Jean-Claude Efile

Felicie Lopez-Lauri

Angélique D'hont

Mathieu Léchaudel

Sébastien Ricci
email: sebastien.ricci@cirad.fr

Detection of dynamic QTLs for traits related to organoleptic quality during banana ripening

Keywords: dry matter content, organoleptic banana quality, Musa, pulp acidity, pulp firmness, dynamic QTL

Fruit quality traits are directly linked to consumer acceptability, and thus key targets for banana breeding programs.

We explored the genetic control of three major organoleptic and ripening-related traits, namely pulp acidity (pH), firmness (PF) and dry matter content (DMC), over a 7-day ripening period and three production cycles in a banana segregating population genotyped by sequencing. Significant broad-sense heritabilities were estimated with 0.77, 0.46 and 0.81 values for pH, PF and DMC, respectively. QTL detection was first performed on the whole dataset to analyze their dynamics. In a second approach, per-cycle data were considered to evaluate the stability across production cycles. Finally, a meta-analysis was performed. Various QTLs were detected, as well as many QTL colocalizations, while 12 of these QTLs were more prominent as they were detected in several approaches and/or explained over 15.0% of the phenotypic variation. Candidate genes were proposed for 10 of these QTLs. The QTL with the largest contribution to pulp acidity (R 2 = 19.3-50.6%) was located on LG1_7 on the genetic map of Pisang

Introduction

Banana (Musa spp.) is a major economically important fruit that is grown throughout the world's tropical and subtropical regions. It is a cash crop that is sold on local and international markets, while also being staple food for several million people worldwide. Most world production is based on a few groups of cultivars derived from somaclonal variation, which is why bananas are particularly vulnerable to pests and diseases [START_REF] Bakry | Genetic improvement of banana. Breeding plantation tree crops: tropical species[END_REF].

Banana cultivars are generally triploid, and sometimes diploid or tetraploid, with a basic chromosome number of x=11. They are derived from hybridization between several Musa species and subspecies [START_REF] Carreel | Ascertaining maternal and paternal lineage within Musa by chloroplast and mitochondrial DNA RFLP analyses[END_REF][START_REF] Perrier | Dietary fiber, starch, and sugars in bananas at different stages of ripeness in the retail market[END_REF], and their genome consists of complex inter(sub)specific mosaics [START_REF] Baurens | Recombination and large structural variations shape interspecific edible bananas genomes[END_REF]Martin et al. 2020a;Cenci et al. 2021). The main target of banana breeding programs is to produce disease resistant cultivars while maintaining good fruit quality.

However, banana breeding is particularly complex since cultivars need to be sterile or have very low fertility to produce seedless edible fruits, and since triploid is the most agronomically efficient ploidy level.

Improving cultivars by successive crossing steps among the best cultivars is therefore not an option. Banana breeders thus use existing diploid cultivars to exploit their residual fertility and/or for artificial tetraploidization to recover fertility before crossing with wild or cultivated diploids to produce triploid cultivars. The genome complexity of banana cultivars and the lack of knowledge on the genetic determinism of the targeted traits are further banana breeding constraints. The rare QTL and GWAs studies performed so far in banana have focused on the seedless phenotype (i.e. parthenocarpy combined with female sterility, [START_REF] Sardos | A genome-wide association study on the seedless phenotype in banana (Musa spp.) reveals the potential of a selected panel to detect candidate genes in a vegetatively propagated crop[END_REF], bunch weight component traits [START_REF] Nyine | Association genetics of bunch weight and its component traits in East African highland banana (Musa spp. AAA group)[END_REF] and Fusarium wilt resistance [START_REF] Ahmad | Genetic mapping of Fusarium wilt resistance in a wild banana Musa acuminata ssp. malaccensis accession[END_REF].

Several large reciprocal translocations have been reported in Musa species and subspecies [START_REF] Martin | Evolution of the banana genome (Musa acuminata) is impacted by large chromosomal translocations[END_REF][START_REF] Baurens | Recombination and large structural variations shape interspecific edible bananas genomes[END_REF][START_REF] Dupouy | Two large reciprocal translocations characterized in the disease resistance-rich burmannica genetic group of Musa acuminata[END_REF], Martin et al. 2020b[START_REF] Šimoníková | Chromosome Painting in Cultivated Bananas and Their Wild Relatives (Musa spp.) Reveals Differences in Chromosome Structure[END_REF]. These translocations at an heterozygous state induce various extents of segregation distortion and recombination reduction that involve only the breakpoint region, a chromosome arm or an entire chromosome. They are frequently found at an heterozygous state in cultivars and breeding materials (Martin et al. 2020b), thereby at least partly explaining the major segregation distortions observed in the few genetic Musa maps developed so far [START_REF] Fauré | A molecular marker-based linkage map of diploid bananas (Musa acuminata)[END_REF][START_REF] Hippolyte | A saturated SSR/DArT linkage map of Musa acuminata addressing genome rearrangements among bananas[END_REF]DHont et al. 2012;[START_REF] Mbanjo | Molecular markerbased genetic linkage map of a diploid banana population (Musa acuminata Colla)[END_REF][START_REF] Noumbissié | Chromosome segregation in an allotetraploid banana hybrid (AAAB) suggests a translocation between the A and B genomes and results in eBSV-free offsprings[END_REF].

In recent decades, ecophysiological studies have generated insight into the morphological, physicochemical and sensory characteristics of edible Musa varieties, in order to understand banana consumers' preferences and facilitating breeding [START_REF] Gibert | Differentiation between cooking bananas and dessert bananas. 1. Morphological and compositional characterization of cultivated Colombian Musaceae (Musa sp.) in relation to consumer preferences[END_REF][START_REF] Bugaud | Sensory characterisation enabled the first classification of dessert bananas[END_REF][START_REF] Bugaud | Rheological and chemical predictors of texture and taste in dessert banana (Musa spp.)[END_REF]. As for most fleshy fruit, sweetness, sourness, firmness, mealiness and aroma are the five most relevant attributes for consumer acceptance [START_REF] Bugaud | Sensory characterisation enabled the first classification of dessert bananas[END_REF][START_REF] Bugaud | Optimal and acceptable levels of sweetness, sourness, firmness, mealiness and banana aroma in dessert banana (Musa sp.)[END_REF]. Pulp acidity is an important aspect of banana organoleptic quality since it is related to the perception of sourness and sweetness based on the sugar-acid ratio, which has a high impact on consumer acceptability [START_REF] Bugaud | Optimal and acceptable levels of sweetness, sourness, firmness, mealiness and banana aroma in dessert banana (Musa sp.)[END_REF]. pH is a practical measurement for high throughput phenotyping as this indicator is able to predict accurately the sensory characteristics sourness [START_REF] Bugaud | Rheological and chemical predictors of texture and taste in dessert banana (Musa spp.)[END_REF]. During ripening, processes involved in the metabolism and accumulation of organic acids in pulp cells are under both genetic and environmental control (Etienne et al. 2013a(Etienne et al. , b, 2014[START_REF] Etienne | A process-based model of TCA cycle functioning to analyze citrate accumulation in preand post-harvest fruits[END_REF]. Pulp texture-mostly firmness and melting-is the second most important factor regarding the eating quality of bananas [START_REF] Bugaud | Optimal and acceptable levels of sweetness, sourness, firmness, mealiness and banana aroma in dessert banana (Musa sp.)[END_REF]. Fruit texture and softening, associated with fruit cell wall disassembly during ripening, are also crucial fruit shelflife and postharvest quality factors (Brummell et al. 1999;[START_REF] Wakabayashi | Changes in Cell Wall Polysaccharides During Fruit Ripening[END_REF]. In recent decades, there has been an increased focus on the pulp dry matter content, which has been found to be linked to various quality traits, namely textural traits (Vázquez-Ovando et al., 2012;[START_REF] Gibert | Quantification and modelling of the stomatal, cuticular and crack components of peach fruit surface conductance[END_REF], soluble solids [START_REF] Bugaud | Relative importance of location and period of banana bunch growth in carbohydrate content and mineral composition of fruit[END_REF]) and sugars (Phillips et al., 2021); with sugars, organic acids and non-structural carbohydrates being the main components of pulp dry mass. The dry matter content (DMC) is also a useful trait to discriminate cooking and dessert bananas [START_REF] Gibert | Differentiation between cooking bananas and dessert bananas. 1. Morphological and compositional characterization of cultivated Colombian Musaceae (Musa sp.) in relation to consumer preferences[END_REF]. The sensory texture of boiled bananas noted by consumers can be predicted by this criterion [START_REF] Kouassi | Textural and physicochemical predictors of sensory texture and sweetness of boiled plantain[END_REF].

Phenotyping of organoleptic fruit quality characteristics is difficult and time-consuming in cultivated material and very hard to assess in fruits from wild material since they have very low pulp contents. The aim of our study was to analyze the genetic determinism of organoleptic quality traits in banana and to identify the genomic regions involved so as to be able to develop marker assisted selection for these traits and to improve breeding schemes. To this end, we performed QTL detection of three banana quality criteria: pH, firmness and DMC. This analysis was based on a segregating population obtained from a cross between two diploid parents that serve as elite genitors in several of the very few banana breeding programs existing around the world. The parent Pisang Madu is a cultivar closely related to Cavendish, the most commercially important dessert cultivar group, and the parent Galeo is a cooking banana cultivar. Phenotypic variation and heritability of the studied traits were exploited in a dynamic QTL analysis encompassing all days of the fruit ripening process and the various production cycles. The stability of QTLs across production cycles was also investigated. A meta-analysis was then performed to identify QTL clustering and colocation, and to refine the confidence intervals. Several candidate genes related to the studied quality traits were identified and the implications of these findings for banana breeding programs are discussed.

Material and Methods

Plants materials

The progeny consisted of 156 individuals generated from crosses between the diploid Pisang Madu cultivar, used as female, and the diploid Galeo cultivar. This population was developed at the CIRAD Neufchateau station in Guadeloupe (French West Indies). Due to the reduced fertility observed in the parents, this population was obtained by mixing seeds produced from 5 bunches of Pisang Madu bananas (5 inflorescences corresponding to around 500 female flowers, with 100,000 ovules potentially hybridized with Galeo pollen). Embryo rescue was performed, as described by [START_REF] Bakry | Zygotic embryo rescue in bananas[END_REF], to maximize the recovery of all viable progeny. After greenhouse acclimation, the plants were transferred to the field from October 2013 to December 2014. The banana trees used for these experiments were grown in duplicated randomized blocks under the same field conditions, using plant suckers as planting material for block 2. These individuals were grown at the CIRAD Neufchateau station in Guadeloupe (elevation 250 m; andosol; rainfall 3,500 mm/year; latitude 16° 4'46.63" N, longitude 60° 35'29.35" O) using conventional field practices.

Fruit harvesting, ripening induction and sampling

Fruits were harvested from the field when the 'first yellow finger' stage was visible on the banana bunch.

Postharvest fruit treatment and ripening induction were performed as described in [START_REF] Hubert | Changes in ethylene signaling and MADS box gene expression are associated with banana finger drop[END_REF]. Bananas were then placed in a climatic room at 20°C for 48 h minimum. Fruits were exposed to 1,000 ppm acetylene for 24 h at 20°C to initiate ripening. After treatment, fruits were kept at 20°C and ambient humidity during the phenotyping phase. For each production cycle, at least 60 fruits were sampled per bunch.

Physicochemical measurements

The progeny was studied during three successive production cycles for the three distinct criteria.

Measurements were performed over a 7-day ripening period (day 0 = the day of ripening initiation), each day for pulp pH and firmness, and at days 1, 3 and 7 for pulp dry matter content (DMC). For each biological replicate (corresponding to one harvested bunch), three analytical replicates (corresponding to three fruits) were performed for pulp firmness, while two analytical replicates were performed for pulp pH and DMC. Rheological characteristics for pulp firmness was assessed using a TA-XT2 penetrometer with a 20 mm cylindrical probe on fresh banana, as described by [START_REF] Chillet | Mechanical characterisation of banana fruits[END_REF]. The pulp pH and DMC were measured on two of the three fruits used in the rheological analysis. The pulp pH was measured by direct insertion of a pH sensor into transversely cut fruit pulp.

DMC was determined by measuring the weight loss of 2 g of fresh pulp after oven drying at 70°C for 16 h.

Phenotypic data analysis, BLUPs and heritability

All statistical analyses were performed using R software v.2.9.2 (R development Core team, 2020, http://www.R-project.org), except for the mixed linear model, which was built using PROC MIXED in SAS software, Version 9.4 (SAS Institute Inc., Carey, NC, USA). The phenotypic data were checked visually for their normal distribution and descriptive statistics were generated, including mean, range and standard deviation (SD).

Pearson correlation coefficients for the measured traits within and between cycles were also calculated using the cor.test() function. The effect of genotype, days, cycle and their interactions on the organoleptic variables was estimated using the following global mixed model, which was selected according to minimization of Akaike's information criterion (AIC) [START_REF] Akaike | A new look at the statistical model identification[END_REF]:

Pijkf = μ+Gi+Dj+Bk+Cf+(G×D)ij+(G×C)if +εijkf (1)
where Pijk is the phenotypic value of genotype i at the ripening day j, for the production cycle k and the block f. μ is the overall mean of the progeny, Gi is the random effect of genotype i, Dj is the fixed effect of the ripening day j, Bk is the random effect of the block k, Cf is the random effect of the production cycle k, (G×D)ij and (G×C)if are their random interactions and εijkf is the random residual error effect.

The residual distribution normality was checked afterwards. Broad-sense heritability (H 2 ) was then calculated using the equation proposed by [START_REF] Cullis | On the design of early generation variety trials with correlated data[END_REF] for unbalanced data:

= 1 - ῡ 2 ᵍ (2)
where σg 2 is the genotypic variance, ῡ is the mean variance of a difference of two best linear unbiased predictions (BLUPs; [START_REF] Henderson | Estimation of genetic parameters[END_REF].

Ploidy assessment

The nuclear DNA content of the progenies (i.e, 156 offspring) and the two parents was assessed by flow cytometry. A leaf sample of each plant was chopped with leaf tissue of Oryza sativa L. ssp. japonica cv. Nipponbare as internal reference in modified LB01 buffer (Doležel et al. 1989) in which mercaptoethanol was substituted by 40 mM sodium sulfite (Na2SO3). Analyses were performed with a PAS II flow cytometer (Partec GmbH, Münster, Germany), as described by [START_REF] Doležel | Plant DNA Flow Cytometry and Estimation of Nuclear Genome Size[END_REF].

Parentage verification

Young leaves of the 156 F1 individuals and parents were sampled, and DNAs were extracted from 3 g of leaf material ground in liquid nitrogen according to the modified CTAB/MATAB method [START_REF] Risterucci | A high-density linkage map of Theobroma cacao L[END_REF].

Parentage verification was performed using a total of 15 SSR loci spread over the 11 M. acuminata chromosomes based on the DH-Pahang reference genome sequence [START_REF] Lagoda | Nonradioactive sequence-tagged microsatellite site analyses: A method transferable to the tropics[END_REF][START_REF] Hippolyte | A saturated SSR/DArT linkage map of Musa acuminata addressing genome rearrangements among bananas[END_REF][START_REF] Denoeud | The banana (Musa acuminata) genome and the evolution of monocotyledonous plants[END_REF].

Genotyping was carried out using the protocol described by [START_REF] Christelová | A platform for efficient genotyping in Musa using microsatellite markers[END_REF] and the Applied Biosystems_3500xL Genetic Analyzer and GeneMapper v4.1 software (Applied Biosystems; www.appliedbiosystems.com). Automated data scores were corrected by manual checks.

High-throughput genotyping and genetic mapping

Genotyping by sequencing (GBS) libraries were constructed using the PstI and MseI restriction enzymes and sequencing was performed at the GeT-PlaGe platform (https://get.genotoul.fr/) using the Illumina Hiseq3000 sequencer. Raw GBS sequencing data from the F1 progeny were demultiplexed using GBSX version 1.2 [START_REF] Herten | GBSX: a toolkit for experimental design and demultiplexing genotyping by sequencing experiments[END_REF] and adaptors were removed with cutadapt [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF]. Variant calling was performed using the vcfhunter toolbox (available at https://github.com/SouthGreenPlatform/VcfHunter/) [START_REF] Garsmeur | A mosaic monoploid reference sequence for the highly complex genome of sugarcane[END_REF] as described in [START_REF] Dupouy | Two large reciprocal translocations characterized in the disease resistance-rich burmannica genetic group of Musa acuminata[END_REF]. Mapping was performed on the M. acuminata reference sequence assembly, version 2 [START_REF] Martin | Improvement of the banana "Musa acuminata" reference sequence using NGS data and semi-automated bioinformatics methods[END_REF]. For each accession, sites with read depths of <10 and >3,000 were excluded. The procedure performed to select SNPs useful for the construction of both parental maps is schematically presented in Supplemental Fig. 1. SNP markers with >10.0% missing data and individuals with >30.0% missing data were removed from the mapping dataset to avoid loss of accuracy during the genetic mapping process.

The remaining SNPs were separated into 3 sets of markers to create parental maps for cv Pisang Madu and cv Galeo using JoinMap 5.0 [START_REF] Van Ooijen | JoinMap(R) 4, software for the calculation of genetic linkage maps in experimental population Vázquez-Ovando A[END_REF]: lm × ll (markers from the female parent), nn × np (markers from the male parent), and hk × hk markers (markers heterozygous in both parents). Parental markers were phased and extracted for use as a cross pollinated population type. SNPs included in these three classes were then merged in one file and used to construct the two parental maps. All identical loci were removed and then linkage groups were determined with a LOD threshold of 15, a recombination fraction threshold of 0.35, a ripple value of 1.0 and a jump threshold of 1.0. Markers were ordered using the regression-based parameters; and Kosambi's mapping function was used to calculate genetic distances among loci [START_REF] Kosambi | The Estimation of Map Distances from Recombination Values[END_REF].

QTL detection and meta-analysis

In the present study, two QTL mapping approaches were applied on the best linear unbiased predictors (BLUPs) extracted from the selected mixed linear model (Equation 1). When significant, BLUPs for the GxD and GxC interactions, according to the studied step, were computed for each day and cycle. Quantitative trait loci analysis was performed using the multi-QTL model (MQM) in R/qtl [START_REF] Broman | A Guide to QTL Mapping with R/qtl[END_REF][START_REF] Arends | R/qtl: high-throughput multiple QTL mapping[END_REF]) and interval mapping based on the marker distance. The stepwise QTL function was used to conduct a forward-backward search and account for epistasis with a maximum of 5 QTLs. For each trait, the LOD threshold at a significance level of p = 0.05 was calculated based on 1,000 permutations. QTLs with a LOD above the threshold of 3 were declared significant. The percentage of phenotypic variance explained (R²) of a single QTL was estimated based on a maximum likelihood estimation, and those with a R² ≥ 15.0% were considered as major QTLs.

All QTL analyses were performed for each day of the ripening process. The two QTL mapping approaches were applied independently for each trait, while the meta-analysis was performed with the three traits pooled. In the first approach, QTL detection was performed on the whole dataset, including all of the production cycles, to study QTL stability during fruit ripening. In the second approach, QTL analysis was performed on the three cycle datasets (C1, C2 and C3) separately to assess the stability of QTLs across these production cycles. For a given trait, when a QTL was detected in the same region at different days of the ripening process, they were pooled into a single QTL (consensus QTL), and the consensus QTL was then refined using BioMercator software V4

(http://moulon.inra.fr/index.php/en) and then named 'consensus QTL', with the following nomenclature:

Trait' datasetID' QTL-'MapName''LinkageGroup ID'. For example, DMC W QTL-PM5 corresponds to a QTL related to DMC detected from Pisang Madu polymorphism on linkage group 5 in the whole dataset. The meta-analysis performed using BioMercator software V4 was based on QTLs obtained from the two first approaches. Five different meta-analysis models (1-, 2-, 3-, 4-, or N-QTL) are proposed on BioMercator® and the best model choice was based on Akaike's information criterion (AIC) values (the model with the lowest AIC-value was considered optimal).

Finally, the consensus QTL presented by the optimum model was considered and named MQTL [START_REF] Arcade | BioMercator: integrating genetic maps and QTL towards discovery of candidate genes[END_REF]).

Identification of potential candidate genes

Research on potential candidate genes was only performed on QTLs and MQTLs showing a confidence interval of 10 Mb or less. Genes coding for enzymes or transporters directly linked to acids, sugars, cell wall and hormone metabolism were assessed according to Gene Ontology terms related to these functions available at http://geneontology.org/. From the functional annotation of the Banana Genome Hub (BGH, https://banana-genomehub.southgreen.fr/) a systematic search of the location on the physical map of the list of identified GO terms was conducted. Colocation between a QTL and a candidate gene was identified when the latter was located in the confidence interval of the consensus QTL or MQTL using the BEDTools intersect function [START_REF] Quinlan | BEDTools: a flexible suite of utilities for comparing genomic features[END_REF].

Identification of chromosomal structures associated with pH variation

Both Pisang Madu haplotypes for chromosome 1 were searched in the progenies using the methodology described in Martin et al. (2020b). Two classes of individuals were obtained for each of these structures: individuals with haplotype 1 of Pisang Madu (with the 1/7 translocation) and individuals with haplotype 2 of Pisang Madu (with the reference sequence structure). For each of these classes, the mean pH at day 7 (corresponding to ripe fruit) and standard deviation were calculated. These values were then plotted using a box plot representation. pH averages for the two haplotypes were compared using the Tukey test.

Results

Genotyping and genetic map

The progeny consisting of 156 individuals was genotyped by sequencing, resulting in 2.4 million polymorphic SNPs after variant calling (Supplemental Fig. 1). Seven individuals were removed from the dataset due to the extent of missing data (> 20.0%). After the filtering steps, 4,663 bi-allelic SNPs were kept, with 1,250 (6.4%) The Galeo genetic map (Supplemental Table 1), consisted of 1,148 SNPs assembled in 11 linkage groups, as expected. They were distributed among 937 independent loci due to cosegregation. The map had a total length of 1,345 cM, corresponding to an average interval of 1.5 cM between markers. However, several gaps larger than 10.0 cM or 20.0 cM were observed (Supplemental Table 1). Comparison of the genetic positions of the markers to the physical ones on the M. acuminata reference genome assembly v2.0 (data not shown), revealed no large genome rearrangements in Galeo compared to the reference genome assembly. This also showed that the genetic map covered 379 Mb overall, corresponding to 95.4% of the M. acuminata genome assembly v2.0.

corresponding
The Pisang Madu genetic map (Supplemental Fig. 1) consisted of 1,319 SNPs distributed among 990 independent loci assembled in 10 linkage groups. The Pisang Madu map had a total length of 1,154 cM, corresponding to an average interval of 1.5 cM between markers. However, several gaps larger than 10.0 cM or 20.0 cM were observed (Supplemental Table 1). Comparison of the genetic positions of the markers to the physical ones on the M. acuminata reference genome assembly v2.0 revealed that all markers corresponding to reference chromosome 1 were genetically mapped in the middle of a linkage group that corresponded to reference chromosome 7. This linkage group was named LG1_7 (Supplemental Table 1, Fig. 1a). In addition, no recombination was detected between markers from reference chromosome 1. The genetic map covered 359 Mb, corresponding to 97.4% of M. acuminata genome assembly v2.0 (Supplemental Table 1).

Analysis of phenotyping data

The 156 individuals of the studied progeny were phenotyped during fruit ripening for three quality traits measured in the pulp, i.e. pH, firmness and DMC, during three cropping cycles (Supplemental Table 2). 147, 125, and 46 progeny individuals were phenotyped at cycles 1, 2 and 3, respectively. Phenotypic data of progeny individuals showed a wide distribution for all traits during fruit ripening (Fig. 2). Marked variations in pH and DMC were measured at each fruit ripening day, while for PF there was very little variation within the progenies after day 3.

For each trait at each ripening day, the phenotypic data tended towards a normal distribution, except for PF which showed a right-skewed distribution from the third ripening day (Fig. 2e-h). The range of values measured for these traits and the slopes observed during fruit ripening were similar between cycles, as confirmed by the non-significant cycle effect (Table 1 and Fig. 3).

Variance analysis and decomposition (Table 1) revealed highly significant genotypic effects (p < 0.0001), as well as high interaction effects between genotype and the ripening day and genotype and the plant cycle (p < 0.0001). The proportions of variance explained (PVE) by the different factors were, however, extremely variable between traits. Genotypic effects were the strongest ones for DMC and pH (61.3% and 39.0%, respectively), but explained the lowest part of variance for PF (12.2%). Similarly, the interaction between the genotype and ripening day ranged from 7.5% for DMC to 23.4% for PF. The PVE of the interaction between the genotype and crop cycle was more stable, varying from 13.7% for DMC to 18.5% for PF. DMC and pH finally displayed high heritability values, of 0.81 and 0.77, respectively, whereas PF had the lowest one, with a value of 0.46 (Table 1).

The curve of pulp acidity determined from the pH measurements first decreased until the fourth ripening day, and then stabilized over a period corresponding to the beginning of the second part of fruit ripening process, followed by a rise until the end of ripening, i.e. generally considered as the ready-to-eat stage (Fig. 2a-d, Supplemental Fig. 2a). The pH increase was especially marked in Pisang Madu pulp, contrary to Galeo in which it remained low. PF showed a quick decrease until day 4 of ripening and then stayed almost unchanged at low values during the second part of fruit ripening until the end of this process (Fig. 2e-h, Supplemental Fig. 2b). The DMC tended to decrease during fruit ripening, with quite low variations compared to the other traits (Fig. 2i-l, Supplemental Fig. 2c). All quality traits were found to be positively correlated, regardless of the fruit ripening day, except for DMC and pH at day 7 (Table 2). Furthermore, pH had the highest correlation with PF at days 1 and 3 (r = 0.48), while the correlation between PF and DMC was intermediate and relatively similar for the three measurement days (r = 0.3 to 0.39).

QTLs detected for each of the three quality traits over the ripening period

The QTL detection results obtained with the whole dataset (first approach) are summarized in Supplemental Table 3 and Fig. 3a. A total of 36, 15 and 10 significant associations were detected for pH, PF and DMC, respectively, which corresponded to 5, 4 and 2 consensus QTLs (QTLs detected in the same region at different ripening days), respectively (Supplemental Table 3). They were located on all linkage groups (LG), except LG2.

More QTLs were detected in Pisang Madu than in Galeo, except for PF. Variations in R² values were observed for several QTLs detected during fruit ripening (Supplemental Table 3). Most of the QTLs were not detected every day of the ripening period, many (43.0% for Pisang Madu polymorphism and 30.0% for Galeo polymorphism) were detected only for one or two days. The number of QTLs detected each day ranged from 1 to 10, with the highest number detected at the end of the ripening period (Pisang Madu map). The R² values determined for a single QTL, depending on the ripening day, ranged from 2.2 to 50.6% for pH, 7.9 to 30.0% for PF, and 6.8 to 26.1% for DMC (Supplemental Table 3).

For pulp pH, QTLs were detected on LG1_7, 3, 4, 6, 9 and 10 (Fig. 1b and Fig. 4). On LG1_7 of Pisang Madu, QTLs were detected in two distinct regions (Fig. 1b-c). QTLs detected in the central region of LG1_7 were detected for all fruit ripening days (pH W QTL-PM1-7.1: R² = 19.3-50.6 %, CI = 27.4 Mb). This central region of

LG1_7 corresponded to the part of the reference chromosome 7 and the entire reference chromosome 1 that does not recombine in Pisang Madu (Fig. 2a-c). The second region at the bottom of the LG1_7, corresponding to reference chromosome 7, was detected for all fruit ripening days, except for days 0 and 6 (pH W QTL-PM1T7.2: R² = 3.2-6.9 %, CI = 2.2 Mb). These QTLs combined with those detected for pH reached a total R² value of 67.8% for day 6 and 58.5% for day 7.

For pulp firmness, QTLs were detected on LG1, 3, 4, 5, 6, and 11 (Fig. 1b and Fig. 4). The total R² values of QTLs for PF ranged from 17.7% at day 3 (2 QTLs) to 53.8% at day 6 (4 QTLs). Similar values of QTLs for PF were found at days 5 (R² = 47.2%; 2 QTLs) and 7 (R² = 41.5%; 3 QTLs). QTLs with high R² values (Table 3 and Supplemental Table 3) were observed for PF close to the end of fruit ripening, i.e. on LG6 at day 5 (PF W QTL-G6: R² = 30.0%, CI = 9.4 Mb) and on LG11 at days 6 and 7 (PF W QTL-PM11: R² = 14.2-17.8%, CI = 1.0 Mb).

For pulp dry matter content (DMC), QTLs were detected on LG5, 6, 8, 9, 10, and 11 (Fig. 1b and Fig. 4).

Total R² values were 60.4%, 31.4% and 59.2% at days 1, 3 and 7, respectively, corresponding to 5, 2 and 5 distinct QTLs, respectively (Supplemental Table 3). Two QTLs with major effects (Table 3) were detected at the top of LG9 on the Pisang Madu map, i.e. the first one at days 1 and 3 (DMC W QTL-PM9.1: R² = 18.0-22.1%, CI = 0.9 Mb), and the second at day 7 (DMC W QTL-PM9.2: R² = 26.1%, CI = 1.2 Mb).

Several QTLs for the different quality traits were found in the same regions. Two colocalizations were identified on the Pisang Madu map, i.e. at the top of LG3 with QTLs for pH and PF, and at the top of LG9 with QTLs for pH and DMC. One colocalization was identified from Galeo polymorphism at the bottom of LG6 with

QTLs for pH and PF (Supplemental Table 3).

Consistency of QTLs throughout production cycles over the ripening period

QTL stability over the successive production cycles was investigated by comparing consensus QTLs detected in the same region at different ripening days throughout all cycles to those detected at each cycle (second approach). 110 cycle-specific QTLs were detected overall, including 21, 35 and 54 for cycles 1, 2 and 3, respectively (Fig. 2b-c and Supplemental Table 4). The maximum number of QTLs ( 51) was detected for PF, followed by pH ( 39QTLs) and DMC (20 QTLs) (Fig. 2b-c and Supplemental Table 4). The detected QTLs were usually specific to one cycle (Supplemental Table 4). Six QTLs detected at one cycle were also detected at a second cycle, i.e. one for pulp pH and the others for the PF trait. Two consensus QTLs (PF W QTL-G1 and pH W QTL-PM1_7.1) were identified for all cycles (Fig. 1b, Fig. 4, and Table 3). Ten of the 23 QTLs found in the whole dataset (Supplemental Table 3) were detected using the cycle-specific approach (Table 3). In addition, QTL detection at each cycle allowed the identification of eight additional consensus QTLs that were not detected in the first approach, i.e. two for pH, five for PF and one for DMC. Unlike the first approach, QTLs on LG2 were detected for each trait via this cycle-specific analysis (Supplemental Table 4).

QTL meta-analysis

Interestingly, many QTLs detected in this study colocalized within or between traits, and several were overlapped, thereby increasing the size of the confidence interval. Thus, to mine MQTLs and refine QTL intervals, all QTLs from the two approaches were pooled and projected on each map to run a meta-analysis. MQTLs were projected on a consensus map in Biomercator.V4 except for LG1_7. The meta-analysis results are shown in Supplemental Table 5. The meta-analysis successfully reduced the number of QTLs to investigate in this study as well as their confidence intervals. A total of 33 MQTLs were distributed unevenly across the genome, 9 of which were related to QTLs detected using both the first approach on the whole dataset and the cycle-specific approach (Fig. 4 and Table 3). Among the MQTLs (Supplemental Table 5), three were related to pH only (MQTL-G5.2, MQTL_PM1_7.3, MQTL_PM3.2), and five were related to PF only (MQTL-G4.1, MQTL-G8.1, MQTL-G9.2, MQTL-PM10.1 and MQTL-PM11.2). Remarkably, the confidence intervals obtained for MQTL-PM8.1 and MQTL-PM11.2-both related to DMC-were at least 85.0% lower than that detected for the whole dataset (DMC W QTL-PM8_1: R² = 9.9%, CI = 28.8 Mb; and DMC W QTL-PM11: R² = 13.5%, CI = 19.2 Mb).

The number of MQTLs per LG ranged from one MQTL on LG2 to six on LG10. MQTL-G1 as well as MQTL-PM8.2 covered eight QTLs, while MQTL-PM9.1 and MQTL-PM7.2 covered 15 and 18 QTLs, respectively.

Many of the MQTLs identified in this study were associated with two or three traits. 13 of these MQTLs were related to pH and PF, 3 to pH and DMC, and 3 to PF and DMC. Moreover, 4 MQTLs were found to be related to all traits (Supplemental Table 5).

Colocalization between QTLs and candidate genes (with CI lower than 10 Mb)

Colocalization between QTLs linked to pH and a selection of candidate genes culled from the literature allowed the identification of genes potentially involved in pulp organic acid metabolism (Table 3, Supplemental Tables 3 and5). On the Pisang Madu map, genes related to PEPC and PEPCK were identified in the pH W QTL-PM9 and pH W QTL-PM1_7.2 confidence intervals. In addition, the latter detected by MQTL analysis, i.e.

MQTL_PM1_7.3, was associated with genes involved in Aco, GAD and Mal activities (Table 3 andSupplemental Table 5). The gene related to V-ATPase was identified in a QTL detected by the two approaches and in the MQTL-PM3.2 confidence interval (Table 3). On the Galeo map, most of these genes were identified in pH W QTL-G10 and MQTL-G5.2 confidence intervals (Supplemental Tables 3 and5). Surprisingly, no candidate gene linked to citrate synthase was found for all QTLs linked to pH.

Candidate genes involved in cell wall metabolism (division and expansion) and hormones regulating fruit ripening were detected in seven QTLs linked to PF. Most of these genes were related to auxin regulation and peroxidase activity, as for PF W QTL-PM11 and PF W QTL-G6 and for MQTL-G4.1 (1 Mb) (Tables 3 andSupplemental Table 3). In addition, candidate genes involved in the ethylene response (ETR) were identified for PF W QTL-G11 (1.5 Mb) (Table 3). Genes involved in cell wall metabolism were found in QTL intervals on both parental maps, PF W QTL-PM3 and PF W QTL-G6, and on several MQTLs related to several quality traits, including PF.

Based on their functions, enzymes involved in glycolysis, sugar metabolism, transport, and specific hormonal activity constituted obvious candidate genes for DMC. Candidate genes involved in cell wallscorresponding to structural dry matter-were detected on two QTLs from the Pisang Madu map, i.e. PF W QTL-PM9.1 and -PM9.2 (Table 3). Five MQTLs linked to DMC colocated with genes coding for enzymes catalysing carbohydrate synthesis or degradation (Supplemental Table 5). The same MQTLs except for MQTL-M11.2 colocated with aconitate hydratase activity (Aco). Sucrose synthase activity (SuSy) colocated with MQTL-PM4.1

and MQTL-PM9.3, while UDP colocated with MQTL-PM4.1 and MQTL-PM11.2. In addition, genes linked to auxin metabolism colocated with the two MQTLs linked to DMC only (MQTL-G7 and MQTL-PM11.2).

Identification of chromosomal structures associated with pH variation

The QTL detected for pH with the greatest effect (pH W QTL-PM1_7.1) was identified on LG1_7 of Pisang Madu (Fig. 2b-c). Pisang Madu is structurally heterozygous for translocation between reference chromosomes 1 and 7. We analyzed the correlation between the presence of each of these chromosome haplotypes (chromosome 1 vs chromosome 1T7) with the pH at day 7-corresponding to the ready-to-eat stage-in the population. In the progeny, the mean pH calculated on individuals with chromosome 1T7 was significantly higher than that for individuals with chromosome 1 (Fig. 2d). The higher pH of pH W QTL-PM1_7.1 was thus associated with chromosome 1T7.

Discussion

Dynamic QTL mapping approaches and meta-analysis for banana quality traits

The phenotypic data analysis revealed high heritability values for two of the measured traits (≈ 0.8 for pulp pH and DMC~), and a slightly less one for pulp firmness (0.46), which were suitable for further QTL detection. We detected significant genotypic effects, but also strong genotype-production cycle and genotype-ripening day interactions. This led us to implement two QTL mapping approaches. The first one exploited the whole dataset to analyse QTLs over the fruit ripening period, as reported in dynamic QTLs analyses for other fruits (peach [START_REF] Desnoues | Dynamic QTLs for sugars and enzyme activities provide an overview of genetic control of sugar metabolism during peach fruit development[END_REF], apple [START_REF] Costa | QTL dynamics for fruit firmness and softening around an ethylene-dependent polygalacturonase gene in apple (Malus×domestica Borkh.)[END_REF] and tomato [START_REF] Sun | Dynamic QTL analysis for fruit lycopene content and total soluble solid content in a Solanum lycopersicum x S. pimpinellifolium cross[END_REF]). The second one exploited data by cycle to evaluate the stability of the detected consensus QTLs according to the production cycle. These approaches resulted in detection of numerous QTLs, but the confidence intervals were broad, and lot of them were overlapping. So an additional meta-analysis of QTLs identified in these approaches was performed that enabled us to refine the QTL intervals and study potential QTLs colocalization. Meta-analysis approaches were initially developed to identify fine consensus QTLs across multiple studies for a single trait [START_REF] Wu | Meta-analysis of QTL Mapping Experiments[END_REF], but they were also recently used to study colocalization between QTLs for distinct traits [START_REF] Shen | Major Co-localized QTL for Plant Height, Branch Initiation Height, Stem Diameter, and Flowering Time in an Alien Introgression Derived Brassica napus DH Population[END_REF] The construction of the two parental genetic maps, used to perform QTL detection, led to 11 linkage groups for Galeo as expected, but to only 10 linkage groups for Pisang Madu, with LG1_7 associating markers from reference chromosome 1 as well as reference chromosome 7 (Fig. 2). This particularity was linked to the presence of a large reciprocal translocation involving reference chromosomes 1 and 7 that has been reported in heterozygous state in Pisang Madu and that prevents recombination on chromosome 1 (Martin et al. 2020b). The translocation breakpoint was localized in the pericentromeric region for chromosome 7, which explains the central position of chromosome 1 markers in LG1_7.

Among the various detected QTLs, 12 seemed highly promising (Table 3), nine of which were confirmed in the meta-analysis, with a marked reduction in CI enabling us to research candidate genes. Among these nine QTLs, three corresponded to major consensus QTLs with R² values over 25.0% (pH W QTL-PM1_7.1, DMC W QTL-PM9.2, and PF W QTL-G6). Moreover, these three QTLs were involved in three MQTLs linked to several quality traits, thus highlighting colocalization between QTLs for these distinct traits. Relationship between such traits were previously reported in other fruits such as peach [START_REF] Quilot | Analysing the genetic control of peach fruit quality through an ecophysiological model combined with a QTL approach[END_REF][START_REF] Desnoues | Dynamic QTLs for sugars and enzyme activities provide an overview of genetic control of sugar metabolism during peach fruit development[END_REF]), apple (Liebhard et al. 2003;[START_REF] Kenis | Identification and stability of QTLs for fruit quality traits in apple[END_REF] or tomato [START_REF] Chaïb | Stability over genetic backgrounds, generations and years of quantitative trait locus (QTLs) for organoleptic quality in tomato[END_REF][START_REF] Saliba-Colombani | Genetic analysis of organoleptic quality in fresh market tomato. 1. Mapping QTLs for physical and chemical traits[END_REF]. Finally, two additional notable QTLs with R² values higher than 15.0%, were detected but only from the first approach, they concerned DMC W QTL-PM9.1 and PF W QTL-PM11. The latter was detected in particular at the end of fruit ripening. The tenth QTL with an R² close to 15.0%, which was not identified in the meta-analysis, was detected throughout the fruit ripening period (pH W QTL-G6). We presume that this difference in QTL detection between the two approaches was related to the number of phenotyping data, which was higher in the first approach, thereby facilitating QTL detection. The variation in QTLs identified between production cycles in the second approach could be related to the different environmental conditions. The impacts of climatic conditions-which could vary between crop cycles-are well-documented for organoleptic fruit quality in banana [START_REF] Peacock | Banana ripening: effect of temperature on fruit quality[END_REF][START_REF] Daniells | Effect of bunch-covering methods on maturity bronzing, yield, and fruit quality of bananas in North Queensland[END_REF][START_REF] Ambuko | EFFECT OF SEASONAL VARIATION[END_REF][START_REF] El Moniem | The combined effect of some organic manures, mineral N fertilizers and algal cells extract on yield and fruit quality of Williams banana plants[END_REF][START_REF] Hailu | Review on postharvest technology of banana fruit[END_REF], as well as for other fruit such as tomato [START_REF] Guichard | Tomato fruit quality in relation to water and carbon fluxes[END_REF][START_REF] Mulholland | Effects of high temperature on tomato summer fruit quality[END_REF][START_REF] Panthee | Magnitude of Genotype × Environment Interactions Affecting Tomato Fruit Quality[END_REF]) and melon [START_REF] Bernillon | Metabolomic and elemental profiling of melon fruit quality as affected by genotype and environment[END_REF]). In addition to environmental effects, this difference could at least partly be explained by the fact that in each cycle not all the same progeny individuals were phenotyped and the number of progeny individuals phenotyped per cycle varied. Accordingly, QTLs detected in cycle 3, which involved the lowest number of individuals, should be regarded with caution since the number of QTLs detected was reported to increase as the population size decreased [START_REF] Vales | Effect of population size on the estimation of QTL: a test using resistance to barley stripe rust[END_REF].

Genetic control of banana quality traits

Flavor, including taste and aroma, is the first quality perception criterion for consumer acceptance. Flavor mainly depends on sugar and acid contents, and particularly on the sugar/acid ratio, which plays a role in both sourness and sweetness perception [START_REF] Bugaud | Sensory characterisation enabled the first classification of dessert bananas[END_REF][START_REF] Bugaud | Optimal and acceptable levels of sweetness, sourness, firmness, mealiness and banana aroma in dessert banana (Musa sp.)[END_REF]. The majority of pH QTLs were detected on the Pisang Madu genetic map, using both the whole dataset and per-cycle data. The increase in pH at the end of fruit ripening, as observed in Pisang Madu, has been related to the decrease in acid contents, and described in banana cultivars with low citrate levels and sometimes with high malate levels (Etienne et al. 2013a[START_REF] Etienne | Citrate and malate accumulation in banana fruit (Musa sp. AA) is highly affected by genotype and fruit age, but not by cultural practices[END_REF]. The pH profile observed for cv Galeo was typical of these acidic bananas and could be associated with high citrate contents [START_REF] Etienne | Citrate and malate accumulation in banana fruit (Musa sp. AA) is highly affected by genotype and fruit age, but not by cultural practices[END_REF]. pH W QTL-PM1_7.1 had the highest estimated effect for QTLs detected at the optimal maturity stage, with R² values of 50.6 and 44.0% at days 6 and 7, respectively. The effects of additional pH QTLs were relatively lower with, however, some pH QTLs having marked effects, especially during the first part of ripening on LG3

(pH W QTL-PM3: 9.0%) and LG6 (pH W QTL-G6: 5.6 to 14.4%).

Genes coding for enzymes involved in acid metabolism were found in the confidence interval of QTLs for pH. They corresponded especially to the tricarboxylic acid cycle, namely with Aco, GAD, Mal, POX, PEPC and PEPCK. A previous modelling study on banana acidity highlighted the potential role of NAD-malic enzyme and mitochondrial malate carriers in the genotypic variability of citrate concentration differences [START_REF] Etienne | Citrate and malate accumulation in banana fruit (Musa sp. AA) is highly affected by genotype and fruit age, but not by cultural practices[END_REF][START_REF] Etienne | A process-based model of TCA cycle functioning to analyze citrate accumulation in preand post-harvest fruits[END_REF].These genes were linked to QTLs related to organic acid variations in other fruits like tomato [START_REF] Causse | A genetic map of candidate genes and QTLs involved in tomato fruit size and composition[END_REF]), apple (Ban and Xu, 2020) and grappeberries (Chen et al., 2015). For malic acid variations among cultivars, a similar approach highlighted the importance of proton pump activity and especially the free energy of ATP hydrolysis [START_REF] Etienne | Citrate and malate accumulation in banana fruit (Musa sp. AA) is highly affected by genotype and fruit age, but not by cultural practices[END_REF]. This activity could be studied in relation to the pH MQTL-PM3.2, where gene coding to V-ATPase was detected. QTL regions with these genes were reported to influence malate accumulation in other fruits such as apple [START_REF] Jia | Apple fruit acidity is genetically diversified by natural variations in three hierarchical epistatic genes: MdSAUR37, MdPP2CH and MdALMTII[END_REF]) and tomato [START_REF] Causse | A genetic map of candidate genes and QTLs involved in tomato fruit size and composition[END_REF]).

After flavor, fruit texture-which can be assessed by pulp firmness-is the first consumer quality perception criterion for bananas [START_REF] Bugaud | Rheological and chemical predictors of texture and taste in dessert banana (Musa spp.)[END_REF]). In addition, texture is essential for consumers with regard to shelflife and transportability [START_REF] Bugaud | Rheological and chemical predictors of texture and taste in dessert banana (Musa spp.)[END_REF][START_REF] Jaiswal | Prediction of textural attributes using color values of banana (Musa sapientum) during ripening[END_REF]. The detected genetic effect of pulp firmness in the studied progeny was particularly low compared to other traits (PVE 12.2% and H² 0,46), while presenting the highest interaction effects and residuals (PVE ~ 40.0%), which could be attributed to high variability between analytical replicates and a greater environmental influence on this trait compared to others. According to several studies on banana, fruit firmness depends on several factors, such as genotype [START_REF] Saliba-Colombani | Genetic analysis of organoleptic quality in fresh market tomato. 1. Mapping QTLs for physical and chemical traits[END_REF], growth conditions [START_REF] Ambuko | EFFECT OF SEASONAL VARIATION[END_REF][START_REF] Bugaud | Relative importance of location and period of banana bunch growth in carbohydrate content and mineral composition of fruit[END_REF], maturity stage at harvest and postharvest conditions [START_REF] Ambuko | EFFECT OF SEASONAL VARIATION[END_REF][START_REF] Salvador | Changes in colour and texture and their relationship with eating quality during storage of two different dessert bananas[END_REF][START_REF] Bugaud | Climatic conditions affect the texture and colour of Cavendish bananas (Grande Naine cultivar)[END_REF]). Some cycle-specific QTLs were detected from the first two cycles, during the second part of ripening, close to the optimum stage of consumption. PF C1 QTL.G1 and PF C1 QTL.PM10 were both found at days 6 and 7, and PF C2 QTL.PM8 and PF C2 QTL.PM1_7.2 were detected on 4 different days from days 5 to 7. Several genes involved in cell wall metabolism were found in QTL intervals related to PF on the two maps, i.e. PF W QTL-PM3 and PF W QTL-G6. Genes involved in the auxin response and auxin signaling pathway that could regulate cell wall metabolism and influence fruit softening [START_REF] Kumar | Role of plant hormones and their interplay in development and ripening of fleshy fruits[END_REF]) have been identified with PF W QTL-PM4 and PF W QTL-G4 within 0.9 Mb and 4.7 Mb, respectively. In tomato, through the SlARF4 gene, auxin has been found to control fruit firmness by regulating the pectin structure and tissue architecture [START_REF] Jones | Down-regulation of DR12, an auxinresponse-factor homolog, in the tomato results in a pleiotropic phenotype including dark green and blotchy ripening fruit[END_REF][START_REF] Sagar | SlARF4, an Auxin Response Factor Involved in the Control of Sugar Metabolism during Tomato Fruit Development[END_REF]. Moreover, genes involved in the ethylene response (ETR) were found within the 1.5 Mb interval of PF W QTL-G11. Ethylene and its biosynthetic genes have been implicated in fruit softening and shelflife maintenance regulation in several fleshy fruits [START_REF] Xiong | Different effects on ACC oxidase gene silencing triggered by RNA interference in transgenic tomato[END_REF][START_REF] Nishiyama | Ethylene regulation of fruit softening and cell wall disassembly in Charentais melon[END_REF][START_REF] López-Gómez | Ripening in papaya fruit is altered by ACC oxidase cosuppression[END_REF]), e.g. via the expression of cell wall-related genes in tomato [START_REF] Bu | Postharvest UV-C irradiation inhibits the production of ethylene and the activity of cell walldegrading enzymes during softening of tomato (Lycopersicon esculentum L.) fruit[END_REF]) apple [START_REF] Wei | Changes and postharvest regulation of activity and gene expression of enzymes related to cell wall degradation in ripening apple fruit[END_REF], and peach [START_REF] Hayama | Ethylene-regulation of fruit softening and softening-related genes in peach[END_REF].

DMC is an important determinant with regard to several aspects of fruit quality such as fruit texture, including fruit firmness, storage and taste (Ferris et al. 1999;[START_REF] Bugaud | Climatic conditions affect the texture and colour of Cavendish bananas (Grande Naine cultivar)[END_REF]. Bananas with higher pulp DMC have thus been related to firm bananas and consumers have shown a preference for fruit with higher DMC, including plantain and cooking bananas at the green stage (Ferris et al. 1999;[START_REF] Gibert | Differentiation between cooking bananas and dessert bananas. 1. Morphological and compositional characterization of cultivated Colombian Musaceae (Musa sp.) in relation to consumer preferences[END_REF][START_REF] Bugaud | Rheological and chemical predictors of texture and taste in dessert banana (Musa spp.)[END_REF].

DMC presented high genetic determinism in the studied progeny (61.3% PVE), thus confirming the potential of this trait for dessert and cooking banana breeding schemes. In addition, DMC measured at harvest was correlated with DMC at the optimal stage (r = 0.72; data not shown). The high stability of DMC at the end of fruit growth and ripening had been pointed out in previous studies on banana (Ferris et al. 1999;[START_REF] Yomeni | Influence of the stage of ripeness of plantains and some cooking bananas on the sensory and physicochemical characteristics of processed products[END_REF][START_REF] Bugaud | Climatic conditions affect the texture and colour of Cavendish bananas (Grande Naine cultivar)[END_REF][START_REF] Bugaud | Does bunch trimming affect dry matter content in banana?[END_REF][START_REF] Gibert | Quantification and modelling of the stomatal, cuticular and crack components of peach fruit surface conductance[END_REF]) and mango (Lechaudel et al. 2005). This relationship prevails especially in climacteric fruit, like bananas, that accumulate starch during growth, which is converted into soluble sugars during ripening [START_REF] Xiao | A comprehensive investigation of starch degradation process and identification of a transcriptional activator MabHLH6 during banana fruit ripening[END_REF]. Consequently, although DMC was measured at three days of fruit ripening (days 1, 3 and 7), a significant number of QTLs were detected in the whole dataset. Among the QTLs associated with very low confidence intervals, most were detected for DMC, including those on LG9 (DMC W QTL-PM9.1 and -PM9.2) and

LG10 (DMC W QTL-PM10 and -G10). Other QTLs detected presented high confidence intervals of over 19.0 Mb, which would hamper molecular breeding tool development. About 30.0% of DMC is attributed to structural dry mass, and cell walls [START_REF] Etienne | Citrate and malate accumulation in banana fruit (Musa sp. AA) is highly affected by genotype and fruit age, but not by cultural practices[END_REF], which could explain why genes involved in cell wall dynamics colocalized with four DMC QTLs, including two MQTL (MQTL-PM8.1 and -PM11.2). Interestingly, genes related to several hormone metabolisms, such as auxin transport, ethylene response and gibberellin, colocalized with MQTL-PM11.2 within a 2.9 Mb region. This clustering of genes involved in the fruit development and growth phase may have a similar effect on the structural and non-structural dry matter content.

Several QTL colocalizations were identified for different quality traits on the two parent maps, suggesting physiological relationships between them. The closest correlations between pH and firmness values were observed during the first part of fruit ripening when these traits showed a concomitant decrease. The high number of QTL colocalizations observed for pH and PF could hence be explained by the links between ripening-related changes in pH and fruit texture. Several studies have suggested that changes in pH and mineral composition during fruit ripening may regulate cell wall hydrolase activity [START_REF] Almeida | Apoplastic pH and inorganic ion levels in tomato fruit: A potential means for regulation of cell wall metabolism during ripening[END_REF][START_REF] Prasanna | Fruit Ripening Phenomena-An Overview[END_REF]. The pH acidification trend during ripening has been found to be involved in fruit softening of several species [START_REF] Johnston | Postharvest softening of apple (Malus domestica) fruit: A review[END_REF].

The colocalization between pH and DMC was expected as acids are major dry matter components. Frequent colocalizations have been previously observed between QTLs for pulp dry matter and acidity in tomato [START_REF] Paterson | Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments[END_REF][START_REF] Saliba-Colombani | Genetic analysis of organoleptic quality in fresh market tomato. 1. Mapping QTLs for physical and chemical traits[END_REF]) and peach [START_REF] Dirlewanger | Mapping QTLs controlling fruit quality in peach (Prunus persica (L.) Batsch)[END_REF]). In such cases, it is difficult to distinguish the pleiotropic effect of a single gene from the effects of the nearest genes.

The colocalization between PF and DMC could be explained by the hydrolysis of starch, i.e. the main DMC compound, into soluble sugars during banana ripening [START_REF] Bhuiyan | Characterizing fruit ripening in plantain and Cavendish bananas: A proteomics approach[END_REF]. This causes an increase in osmotic pressure and involves a decrease in turgor pressure associated with softening, as observed during berry [START_REF] Thomas | Mesocarp cell turgor in Vitis vinifera L. berries throughout development and its relation to firmness, growth, and the onset of ripening[END_REF] and tomato ripening (Saladié et al., 2007). Moreover, the pulp dry mass can be assessed by the sum of the dry weights of the main non-structural compounds, such as soluble sugars, starch and acids, and the structural compounds [START_REF] Etienne | Citrate and malate accumulation in banana fruit (Musa sp. AA) is highly affected by genotype and fruit age, but not by cultural practices[END_REF], i.e. the constitutive part of pulp cells. This relationship between the structural dry matter component and cell walls could explain the indirect links between DMC and PF.

Finally, we also noted significant QTL clustering, including a large cluster containing QTLs for all measured traits localized on LG10. This clustering may be due to tight linkage between QTLs (clustering of different genes) and/or may indicate the presence of a single QTL with pleiotropic effects. Similar fruit quality QTL clusters have been reported in other fruit species, including peach [START_REF] Quilot | Analysing the genetic control of peach fruit quality through an ecophysiological model combined with a QTL approach[END_REF][START_REF] Desnoues | Dynamic QTLs for sugars and enzyme activities provide an overview of genetic control of sugar metabolism during peach fruit development[END_REF]), apple (Liebhard et al. 2003;[START_REF] Kenis | Identification and stability of QTLs for fruit quality traits in apple[END_REF]) and tomato [START_REF] Chaïb | Stability over genetic backgrounds, generations and years of quantitative trait locus (QTLs) for organoleptic quality in tomato[END_REF].

Conclusion

High heritability was found in the progeny for the three studied traits, although pulp firmness seemed to be more affected by environmental factors. Breeding and prebreeding approaches should thus permit to improve these characters.

Several promising QTLs (R² >15.0%) were detected for the three characters, including three major QTLs (R² > 25.0%) -one per trait-(pH W QTL-PM1_7.1, DMC W QTL-PM9.2, and PF W QTL-G6). They should now be validated in other genetic contexts and their confidence intervals would need to be refined before envisioning MAS selection.

One particularly interesting QTL is QTL pH W QTL-PM1_7.1 that had the highest estimated effect at the optimal maturity stage, with an R² of 50.6 and 44.0% at days 6 and 7, respectively. We showed that the corresponding haplotype in chromosome 1T7 of Pisang Madu was associated with higher pH at fruit maturity, which is a sweet dessert banana characteristic. This haplotype in Pisang Madu is located, in a region of Chr 1T7 that does not recombine in this heterozygous context and corresponds to reference chromosome 1. This could thus be an advantage for MAS, since selection for this QTL could be performed using any specific markers from chromosome 1 in this progeny and potentially in other progeny of parents structurally heterozygous for the 1T7 chromosome structure. The effects of this QTL in other genomic contexts have yet to be tested, but could potentially explain the success of the 1T7 chromosomal structure to which it is associated and which has been found in many dessert cultivars including Cavendish, that accounts for 50.0% of world banana production (Martin et al., 2020b). ) and QTLs detected in both QTL mapping approaches and the meta-analysis, and QTLs from the first approach (whole dataset) with an R² higher than 15%. QTLs are identified according to the quality trait, i.e. pulp pH (red), firmness (green) and dry matter content (blue, the dataset (W: whole dataset, C: production cycle datasets, with C1, C2 and C3 for cycles 1, 2 and 3, respectively) and the origin of linkage groups with Galeo (G) and Pisang Madu (PM). The location of SNP markers, QTLs, and MQTLs (grey) are indicated Table 1: Variance decomposition for pH, pulp firmness (PF) and pulp dry matter content (DMC) in the studied progeny, and estimated generalized heritability [START_REF] Cullis | On the design of early generation variety trials with correlated data[END_REF]. The proportion of variance explained (PVE, in %) by each factor, i.e. progeny individuals, block, production cycle, interactions between two factors, and residuals were calculated as the percentage of the corresponding sum of squares of each variable to the total sum of squares.

Fig. 1 Fig. 2 Fig. 3 Fig. 4

 1234 Figure captions:Fig.1Genetic map and QTL detected on LG 1_7. a: comparison of the genetic linkage map of LG1_7 with the physical map of reference chromosomes 1 and 7, b: location of the QTL related to the quality traits studied detected in the two approaches (W: whole dataset, C: production cycle datasets, with C1, C2 and C3 for cycles 1, 2 and 3, respectively) and the meta-analysis (MQTL), c: detail of QTLs for each day of the fruit ripening process that contributed to the two consensus pH QTLs identified in the first two approaches and confirmed by the meta-analysis , d: box plot representation of mean pH values at day 7 (corresponding to ripe fruit) of individuals with haplotype 1 (1/7 translocated chromosome structure) of Pisang Madu and individuals with haplotype 2 (reference chromosome structure) of Pisang Madu . Different letters indicate that haplotype averages are significantly different at P<0.05 (according to Tukey's multiple comparison test)

  Fig. 1

Fig. 4

 4 Fig. 4

  to SNPs heterozygous only inGaleo, 2,287 (11.6%) to SNPs heterozygous only in 'Pisang Madu', and 1,126 (5.7%) to SNPs heterozygous in both parents. Loci were further separated into two sets of uniparental configurations to create parental maps. Linkage groups (LGs) were named according to the reference M. acuminata genome assembly[START_REF] Denoeud | The banana (Musa acuminata) genome and the evolution of monocotyledonous plants[END_REF]).

Table 2 :

 2 Correlation between quality traits, i.e. pulp pH, firmness (PF) and dry matter content (DMC) at the various studied fruit ripening days

		day 0	day 1		day 2	day 3		day 4	day 5	day 6	day 7
		PF	PF	pH	PF	PF	pH	PF	PF	PF	PF	pH
	pH	0.32***	0.48***	na	0.45***	0.48***	na	0.35***	0.3***	0.31***	0.23***	na
	DMC	na	0.39***	0.33***	na	0.3***	0.24***	na	na	na	0.36***	0.09
	***P < 0.001

Table 3 :

 3 Description of QTLs detected in both QTL mapping approaches and the meta-analysis, and QTLs from the first approach (whole dataset) with an R² higher than 15%. The day of fruit ripening, trait variability explained by the QTL (R²), position (Pos), identification (ID), including the quality trait, i.e. pulp pH, firmness (PF) and the dry matter content (DMC), dataset, origin of linkage groups with Galeo (G) and Pisang Madu (PM), and candidate genes colocated with the QTLs are indicated.

		QTL Mapping Approach 1		QTL Mapping Approach 2		Meta-analysis		Candidate genes
	Trait
		QTL ID	Day	R²	Pos	QTL ID	Day	R²	Pos	MQTLs	Pos	Quality traits
						pH C1 QTL-PM1_7	0567	[13.39-33.47]	56.85
		pH W QTL-PM1_7.1 01234567 [19.27-50.64] 54.94	pH C2 QTL-PM1_7.2	567	[16 -27.5]	58.85	MQTL-PM1_7.2	55.05	pH, PF, DMC
						pH C3 QTL-PM1_7.1	7	15.4	54.2
	pH	pH W QTL-PM1_7.2	0123457	[3.17-6.92]	93.85 pH C3 QTL-PM1_7.2	0	22.74	80	MQTL-PM1_7.3	90.53	pH	Aco; GAD; Mal; POX; PEPCK; PEPC
		pH W QTL-PM3	0	8.96	64.00 pH C1 QTL-PM3	0	13.47	65	MQTL-PM3.2	64.66	pH	Aco; POX; AG; V-ATPase
		pH W QTL-PM4	4567	[2.25-9.13]	68.24 pH C3 QTL-PM4.1	5	18.21	78	MQTL-PM4.2	78.01	pH, PF	Aco; PEPC; AuxREs
		pH W QTL-G6	01234567 [5.56-14.41]	111.9 pH C3 QTL-G6	3	24.52	152
						PF C1 QTL-G1*	67	[5.48 -11.79]	82.42
		PF W QTL-G1	36	[8.92-9.25]	82.68	PF C2 QTL-G1	6	[9.28-15.46]	74.38	MQTL-G1	78.06	pH, PF	GAD; POX
	PF					PF C3 QTL-G1	3	9.97	72.06
		PF W QTL-G6	155	[8.24-30.00] 113.84 PF C1 QTL-G6	7	9.62	136	MQTL-G6.1	149.20		Aco; CW; CW organization; POX; Mal; PEPCK; PEPC
		PF W QTL-PM11	67	[14.19-17.8]	87.09								POX
		DMC W QTL-PM8	1	9.85	65.49 DMC C3 QTL-PM8	7	22.37	63.23	MQTL-PM8.1	63.94	DMC	Aco; CW
		DMC W QTL-PM9.1	13	[17.95-22.10] 11.18
	DMC	DMC W QTL-PM9.2	7	26.12	16.00 DMC C2 QTL-PM9	3	26.12	16	MQTL-PM9.1	14.92	pH, PF, DMC CW
		DMC W QTL-PM11	3	13.49	50.00 DMC C3 QTL-PM11	1	27.38	73	MQTL-PM11.2	69.19	DMC	CW; AT; ETR; Gibberellin; UDP; CW biogenesis

Acknowledgements

We are grateful to all the staff members of the CIRAD's banana breeding platform in Guadeloupe who were involved in the progeny development, growing and phenotyping. We thank Dr Lucile Toniutti and Dr David Pot for critically reading the manuscript.The CIRAD -UMR AGAP HPC Data Center of the South Green Bioinformatics platform (http://www.southgreen.fr) provided the computational resources.

Funding

This work was supported by CIRAD, the European Agricultural Fund for Rural Development (EAFRD) 'Plan Banane Durable 2' program, the France Genomique programme (ANR-10-INBS-09-08) 'DynaMo' project with Genoscope/CEA, the French Investissements d'avenir programme (Labex Agro: ANR-10-LABX-0001-01)

Availability of data and material

The data that support the findings of this study are available from the authors upon request.

Agropolis Fondation (ID 1504-006) 'GenomeHarvest', and the CGIAR Research Programme on Roots, Tubers and

Bananas

Declaration of Competing Interest

The authors declare that they have no conflict of interest.

Abbreviations for candidate genes: Aco, aconitate hydratase activity; AuxREs, response to auxin; CW, cell wall; ETR, response to ethylene; GAD, Glu decarboxylase; Mal, malate metabolic process; PEPC, phosphoenolpyruvate carboxylase activity; PEPCK, phosphoenolpyruvate carboxykinase (ATP) activity; POX, peroxidase activity; V-ATPase, vacuolar proton-transporting V-type ATPase complex