# Mapping of QTLs for citrus quality traits throughout the fruit maturation process on clementine (Citrus reticulata x C. sinensis) and mandarin (C. reticulata Blanco) genetic maps

Hajer Khefifi, Doriane Dumont, Gilles Costantino, Agnes Doligez, Anna Carla Brito, Aurélie Bérard, Raphael Morillon, Patrick Ollitrault, Luro François

► To cite this version:

Hajer Khefifi, Doriane Dumont, Gilles Costantino, Agnes Doligez, Anna Carla Brito, et al.. Mapping of QTLs for citrus quality traits throughout the fruit maturation process on clementine (Citrus reticulata x C. sinensis) and mandarin (C. reticulata Blanco) genetic maps. Tree Genetics and Genomes, 2022, 18 (6), pp.40. 10.1007/s11295-022-01571-w. hal-03857740

HAL Id: hal-03857740

https://hal.inrae.fr/hal-03857740

Submitted on 17 Nov 2022

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

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

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

Distributed under a Creative Commons Attribution 4.0 International License
1

ORIGINAL ARTICLE

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

2 Mapping of QTLs for citrus quality traits throughout the fruit maturation process on clementine (Citrus reticulata × C. sinensis) and mandarin (C. reticulata Blanco) genetic maps

5 Hajer Khefifi¹,²,³ · Doriane Dumont⁴ · Gilles Costantino¹ · Agnès Doligez³ · Anna Carla Brito²,³ · Aurélie Bérard⁵ ·

6 Raphael Morillon²,³ · Patrick Ollitrault²,³ · François Luro¹

7

Received: 22 July 2022 / Revised: 7 October 2022 / Accepted: 16 October 2022

8

© The Author(s) 2022

9

Abstract

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

Citrus fruit quality is defined as the combination of physical and chemical traits; some of which may change during the ripening phase, e.g., acidity and sugar content. A clear understanding of their genetic control would be very helpful for marker-assisted breeding programs especially with regard to the juvenile phase and some reproductive features that hamper the selection of improved hybrids. A genetic study was thus performed on the heredity of quality traits and QTL detection based on segregation in a progeny generated from a cross between clementine *cv* "Commun" (*Citrus × reticulata cv clementine*) and mandarin *cv* "Willow leaf" (*C. reticulata* Blanco). Parental and consensus genetic linkage maps were constructed using 645 SNP and SSR markers. These maps were represented by 10 linkage groups in clementine and 12 linkage groups in mandarin, representing 75% and 58% respectively of the previously published clementine reference map. A total of 16 traits, including fruit mass, equatorial diameter, juice percentage, total soluble solids, acidity, pH, glucose, fructose, sucrose, and citric and malic acid concentrations were evaluated at three maturation dates. High variations indicating transgressive segregation were found for all traits, with normal or close to normal distributions. QTL analysis performed using the multiple QTL model allowed the detection of 34 QTLs on the three maps. QTLs were distributed in different linkage groups and generally detected at only one date of the ripening phase. The percentage of total variation explained ranged from 12 to 37% per QTL. Major QTLs ($R^2 \ge 30\%$) were detected for equatorial diameter, glucose, and fructose (expressed in percentage dry matter) on linkage groups 8 and 9. Co-localization of QTLs controlling correlated and uncorrelated traits were mainly found on linkage groups 2, 4, 8, and 9, particularly between fruit mass and acidity.

**Keywords** Heredity · SNP · SSR · Acidity · Soluble Sugars · Fruit mass · Fruit ripening

A1 Communicated by C. Chen

A2 ☑ François Luro

A3 francois.luro@inrae.fr

A4 ¹ INRAE, UMR AGAP Institut, Equipe SEAPAG.
A5 Univ Montpellier, CIRAD, INRAE, Institut Agro,
A6 20230 San Giuliano, France

A7 ² CIRAD, UMR AGAP Institut, Equipe SEAPAG,
A8 20230 San Giuliano, France

A9 ³ UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE,
A10 Institut Agro, 34398 Montpellier, France

A11 ⁴ UR-PSH Plantes Et Systèmes de Culture Horticoles INRAE,
A12 Avignon Cédex 9, 84914 Avignon, France

A13 ⁵ UR EPGV INRAE, 2 rue Gaston Cremieux, 91057 Evry,
A14 France

Introduction

27

Modern environment-friendly citriculture requires the devel-
opment of new varieties with higher yield and nutritional
quality, as well as better tolerance to biotic and abiotic con-
straints (Gmitter et al. 2007). Fruit quality and its develop-
ment during maturation are based on environmental factors
and internal complex traits, with juice percentage, acid, and
sugar contents being major determinants of internal fruit
quality (Iglesias et al. 2007). Some of these traits show con-
tinuous variation during fruit ripening (Spiegel-Roy and
Goldschmidt 1996). During the maturation of orange and
mandarin-like varieties, fruit acidity, mainly due to citric
acid, decreases while fruit sweetness increases (Bain 1958).
In addition to the ratio between total sugar content (evalu-
ated with a refractometer) and the titratable acidity, skin

28

29

30

31

32

33

34

35

36

37

38

39

40

41
42 coloration and juice percentage have been used as indicators of citrus fruit maturity, e.g., in clementines, mandarins, and sweet oranges, and they are jointly taken into account when determining fruit harvest dates (Julhia et al. 2019).

43
44
45 A comprehensive understanding of the genetic deter-
minism of fruit quality during maturation is necessary to
facilitate the breeding of new varieties (Gmitter et al. 2007).
However, conventional citrus breeding programs must cope
with many constraints: (1) the juvenility phase, which gen-
erally extends from 5 to 7 years; (2) large plant size; (3)
high heterozygosity of the main cultivars; and (4) polyem-
bryony, which reduces the chance of obtaining zygotic seed-
lings and self-incompatibility (Ollitrault and Luro 1997).
50
55 Marker-assisted selection (MAS) is potentially highly advan-
tageous for citrus breeding since it enables selection at the
seedling stage, thereby overcoming some of the mentioned
58
59 breeding difficulties related to citrus reproductive constraints
60 (Roose 2007). This approach depends on the development of
molecular markers and genetic maps to detect linkage with
61 economically important traits (Staub et al. 1996).

62 Due to the high heterozygosity of citrus germplasm, most
63 of citrus genetic maps have been developed on the basis
64 of F1 crosses, while segregation analyses have enabled the
65 development of genetic maps for each parent and sometimes
66 consensus genetic maps (Ollitrault 2019). Several saturated
67 genetic maps have been published over the last 10 years. The
68 first one is the reference clementine genetic map that was
69 constructed with 961 co-dominant markers from a progeny
70 between clementine and pummelo (Ollitrault et al. 2012a).
71 A sweet orange genetic map (569 markers) was also pub-
72 lished in the same paper. Saturated maps of sweet orange
73 with 943 markers (Xu et al. 2013) and mandarin with 706
74 markers (Shimada et al. 2014) have also been released. More
75 recently, NGS applied with complexity-reduced genomes
76 was used to produce medium- to high-density genetic maps
77 (Guo et al. 2015; Curtolo et al. 2017).

78 Although QTL mapping of fruit quality has received a
79 surge of interest with regard to many species, such as apple
80 (Calenge et al. 2005; Rymenants et al. 2020), peach (Quilot
81 et al. 2005; Rawandoozi et al. 2020), grapevine (Doligez
82 et al. 2013; Houel et al. 2015), and tomato (Ashrafi and
83 Foolad 2015; Cabodevila et al. 2021), this technique has
84 been developed to a lesser extent in citrus (Ollitrault 2019).
85 The majority of published citrus studies have dealt with
86 QTLs related to fruit yield (García et al. 2000) or tolerance/
87 resistance to diseases such as tristeza (Asins et al. 2004)
88 and *Phytophthora* (Siviero et al. 2006), as well as to salinity
89 (Tozlu et al. 1999). Few reports have been published related
90 to fruit quality traits such as acidlessness, acidity, soluble
91 solids content, seediness, color index, carotenoid and fla-
92 vonoid content, and some morphological fruit traits (Fang
93 et al. 1997; Sugiyama et al. 2011; Asins et al. 2015; Yu et al.
94 2016; Imai et al. 2017; Curtolo et al. 2017, Mou et al. 2021).

Most research on QTLs for fruit quality traits has been car-
ried out on F1 populations involving one or two mandarin
95
96
97 parents, but with a variable number of detected QTLs. Only
98 two major QTLs have been consistently detected for fruit
99 quality traits, including one on the *C. clementina* map con-
100 tributing up to 21.3% to rind thickness (Asins et al. 2015).
101 A total of 48 fruit quality QTLs have been identified, 10
102 of which were stable over two or more samplings, while a
103 cluster of QTLs for flavedo and juice colors were detected
104 in a single genomic region on linkage group 4 on the mand-
105 darin genetic map (Yu et al. 2016). A total of 19 QTLs were
106 identified for 12 fruit quality traits on an integrated linkage
107 map of Murcott tangor and Pera sweet orange (Curtolo et al.
108 2017), whereas four QTLs associated with fruit weight, one
109 QTL associated with sugar content, three QTLs associated
110 with peel puffing, and one QTL associated with water rot in
111 mandarin were also identified (Imai et al. 2017). Genome-
112 wide association mapping (GWAS) has also been used for
113 the detection of QTLs of citrus fruit quality traits (Minami-
114 kawa et al. 2017; Imai et al. 2018).

Knowledge regarding factors controlling genetic variation
in citrus fruit traits related to fruit maturation is still quite
115
116
117
118 limited, mainly due to the lack of phenotypic data and the
complexity of those traits.

In order to analyze the genetic determinants of citrus
fruit quality during maturation, the phenotypic variations
119
120 of physical and chemical attributes of fruit were studied in
121 a backcross-like population derived from a cross between
122 clementine (C. *reticulata* × C. *sinensis*) and mandarin (C.
123 *reticulata*). The aim of this study was to map QTLs associ-
124 ated with citrus fruit quality traits. Genetic maps were built
125 with codominant markers. Fruit attributes such as mass,
126 equatorial diameter, pH, acidity, sugar, and acid contents
127 were monitored at different dates during fruit maturation.
128

**Material and methods**

129

**Experimental population**

130

This study was based on a segregating population derived
131 from a cross between clementine *cv* "Commun SRA 63"
132 (Citrus *reticulata* × *C. sinensis*) (C) and mandarin *cv* "Wil-
133 low leaf" (C. *reticulata* Blanco) (M), with clementine as
134 female parent. The direction of this cross was chosen based
135 on the gametic self-incompatibility and absence of apomictic
136 reproduction in clementine, thereby enabling generation of
137 only hybrids derived from cross hybridization. This cross
138 closely resembles a backcross because clementine originates
139 from a cross between "Willow leaf" mandarin and sweet
140 orange and sweet orange probably emerged from a cross
141 between two (mandarin × pummelo) hybrids (Ollitrault et al.
142 2012a; Wu et al. 2014). Due to its pedigree, clementine is
143

131

132

133

134

135

136

137

138

139

140

141

142

143

<figure><img src="image_3.png" /></figure>
144

145

146

147

148

149

150

151

152

153

154

155

156

close to the mandarin phenotype but displays interspecific heterozygous genomic regions with alleles inherited from pummelo (*C. maxima*) (Ollitrault et al. 2012a; Wu et al. 2014). This progeny consisted of 105 offspring grafted onto Carrizo citrange (*C. sinensis* × *Poncirus trifoliata*). The trees were 25 years old. The parents and offspring were grown under the same conditions. The orchard was located at the INRAE research station at San Giuliano (France), and there is no replicate in field design. Standard cultivation practices were applied regularly in order to maintain the orchard healthy and ensure good physiological growth. Fruit quality traits were evaluated during one maturation period between autumn and winter.

157

## Phenotyping

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

Clementine and mandarin maturation occurs in Corsica over the November and January–February periods, respectively (Jacquemond and Agostini 2013), so fruit measurements were performed at three different periods, i.e., in October, December, and February. At each date, 10 random fruits per genotype were collected around the tree and their quality trait attributes were evaluated: 10 replicates for fruit mass, equatorial diameter, and five replicates for juice percentage, pH, titratable acidity, sugar content, citric and malic acids, and soluble sugar (glucose, fructose, sucrose) contents (expressed in % dry and fresh matter [DM and FM, respectively]).

Fruit diameter was measured using a digital caliper (Mitu-toya, Absolute Digimatic, Kawazaki, Japan). Fruit juice was extracted with an electric press (Santos 52C, Vaulx-en-Velin, France), filtered and weighed, according to the standardized and normative method for citrus fruit marketing (CEE-ONU FFV-14). The pH and titratable acidity (TA expressed in g of citrate/100 g of juice) were determined for each fruit using an autotitrator (Mettler Toledo DL 50, Greifensee, Swiss), as described in Albertini et al. (2006). Sugar content (TSS in Brix), was measured using a digital refractometer (RFM710, Bellinghan Stanley, UK).

181

## Measurement of sugar and organic acid contents

182

183

184

185

186

187

188

189

190

Organic acids and soluble sugars were extracted and analyzed by enzymatic assay according to Gomez et al. (2007) and Etienne et al. (2013a, b), adapted to citrus fruit. Briefly, fruit pulp was lyophilized at – 80 °C and 0.06 bar using a lyophilizer (Christ BETA 1–8-LD, Osterode Am Harz, Germany). The lyophilization period lasted 3 weeks, and then, the fruit pulp was ground into a powder using a TissueLyser II bead mill (QIAGEN). Two milliliters of water were added to 20 mg of lyophilized pulp powder.

Samples were centrifuged for 5 min (17,000 g at 4 °C; Sigma 4-16 K). Supernatants (1650 µL) were recovered and supplemented with 10 mg of polyvinylpolypyrrolidone (PVPP) (part no. 25 249/54/1, Sigma-Aldrich Corp., Lyon, France) to eliminate residual phenols. After sample homogenization using a vortex for a few seconds and agitation for 20 min at 4 °C on a rotating wheel, the microtube was centrifuged (10 min, 17,000 g, at 4 °C). The supernatant was then recovered and stored at – 80 °C prior to analysis.

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

Soluble sugars and organic acids were quantified using an absorbance microplate reader (Biotek, ELx808, Vermont, USA) according to Gomez et al. (2007) and Etienne et al. (2013a, b), with some modifications to tailor it to the citrus fruit samples. The only difference relative to the initial protocol was the enzymatic reaction duration. For glucose and fructose, the nicotinamide adenine dinucleotide hydride (NADH) concentration became stable after 3 h instead of 2 h after starting the reaction. For the two organic acids, the NADH concentration plateaued 2 h after the onset of the reaction as compared to 3 h for citric acid and 2 h 45 min for malic acid. During the enzymatic reaction, a microplate was placed in an oven at 25 °C, i.e., the optimal temperature for all of the reagents used.

## Statistical analysis and BLUPs

215

The statistical analysis was performed using the Statistica 10 (TIBCO Software Inc, Palo Alto, CA, USA; 2017), available from: https://www.tibco.com/products/tibco-statisica) and R 3.2.1 (RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/) software packages. The mean and standard deviation of each trait were estimated separately for the two parents and their offspring. Distribution normality was evaluated based on a Shapiro–Wilk test (Royston 1995). As many traits did not follow a normal distribution, phenotypic correlations among traits were calculated using the non-parametric Spearman correlation coefficient. For traits with a distribution deviating from normality, several transformations (ln, square root and cubic root) were tested. The least-skewed transformed data were used to extract the best linear unbiased predictors (BLUPs) of genetic values at each date (Robinson 1991). A linear model with a random genotypic effect was fitted: $P_{ij} = \mu + G_i + e_{ij}$, where $P_{ij}$ was the transformed phenotypic value of fruit $j$ of genotype $i$, $\mu$ the overall mean, $G_i$ the random effect of genotype $i$, and $e_{ij}$ the residual error effect. BLUPs of genotypic values were used for genetic correlation estimation and QTL detection. Variance estimates were used to estimate the broad-sense heritability ($H^2$) as: $\sigma^2_G/(\sigma^2_G + \sigma^2_e)$.

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239
240 Genotyping of the CxM population

241 Young leaves from each genotype were harvested to genotype the parents and progeny with molecular markers. Total DNA was extracted from leaf tissue using the method described by Doyle and Doyle (1987). Single-sequence repeats (SSRs) and single-nucleotide polymorphisms (SNPs) were used. SSR amplification and detection of amplified DNA fragments were performed according to Luro et al. (2008). The genetic map was constructed with 94 heterozygous SSR markers originated from genomic mandarin DNA library (Ci*****)) (Froelicher et al. 2008) or clementine EST library (MEST***) (Luro et al. 2008). SNP markers (CiC****.**) were mined from the clementine BACend Sequence database, and 1536 SNPs were used for an Illumina GoldenGate assay (Terol et al. 2008; Ollitrault et al. 2012b). Some SNP markers from genes involved in the primary and secondary metabolite biosynthesis pathway and in salt tolerance—mined by Sanger sequencing of 44 genotypes representative of *Citrus* and relatives (Garcia-Lor et al. 2012)—were added to the Illumina SNP set (CHI-*-***, LCY2-*-***, TScMI1331, HKT1c800F141, PSY-M-289, PKF-M-186). The SNP and SSR markers used in our study had been previously mapped on the clementine reference genetic map (Ollitrault et al. 2012a).

264 Genetic linkage maps

265 Genetic linkage analysis and map construction were performed with Join Map 4 (Van Ooijen 2006), and maps were drawn with Mapchart 2.3 (Voorrips 2002). Framework consensus and parental maps were constructed based on 645 markers (Additional Table 1) and 105 CxM hybrid trees, with “CP” as population type. Segregation distortion for parental and consensus data was assessed with $\chi^2$ tests according to the segregating type of each marker. These markers revealed three segregation patterns: 1:1 for markers segregating only in one parent (ll×1 m and nn×np), 1:2:1 for markers segregating in male and female parents (hk×hk), and more informative 1:1:1:1 segregation in markers segregating in both parents with three alleles (ef×eg). Grouping was achieved using a minimum LOD score of 4. The regression mapping algorithm (round 2) and Kosambi mapping function were used to establish the map order and distances in centiMorgans (Kosambi 1943; Stam 1993) within each linkage group. The linkage group nomenclature was the same as in the Clementine reference map (Ollitrault et al. 2012a). For subsequent QTL analysis, the number of markers was reduced in very dense map regions by maintaining only one marker for identical genetic positions and removing all other redundant ones with the same or a very close position < 1 cM), resulting in what we called framework maps.

290 QTL detection

Marker-phenotype associations per trait were tested by interval mapping (IM) and the multiple QTL model (MQM; composite interval mapping equivalent) using the Map QTL version 6 software package (Van Ooijen 2009). This analysis was performed on BLUPs of genotypic values at each date on parental and consensus framework maps. For each trait and map, we determined the IM LOD threshold through 1000 permutations of traits over marker data, for a genome-wide first type error rate of 5%. Thereafter, MQM was performed, using the same threshold level, by selecting markers nearest to the QTLs detected with IM as cofactors. This manual cofactor selection increased the number of identified QTLs. It allowed the detection of several QTLs which could not be detected by IM alone. The non-parametric Kruskal-Wallis rank-sum test was also used to check the MQM results, especially for QTLs detected in large intervals between adjacent markers, with a stringent significance level of 0.005. Confidence intervals of QTL positions were determined as one-LOD support intervals. The QTL results were plotted using MapChart 2.3 software.

311 Results

312 Distribution of phenotypic traits

The distribution of raw phenotypic values for fruit attributes in the progeny and parents at the three dates evaluated throughout fruit maturation was described based on the distribution of the number of genotypes by class of raw concentrations in fresh matter (Fig. 1) and by box plots (Additional Fig. 1). The concentration of primary metabolism compounds was also calculated on a dry matter basis and presented according to the distribution of the number of genotypes by class of raw concentrations (Fig. 2) and in box plots (Additional Fig. 2). During maturation, the average fruit mass and equatorial diameter of the progeny reached a maximum average value in December and then levelled off in clementine and CxM offspring, while these two parameters continuously increased in mandarin. The juice percentage increased until December and then decreased considerably in the two parents and the offspring. Otherwise, the acidity pattern was the same for the parents and the CxM offspring. It decreased until reaching low values especially for clementine and the 105 hybrids (0.3–0.6 g/100 g).

Sucrose was the major sugar detected during maturation in the CxM offspring and parents. Its mean concentration was about 3- to sixfold higher than that of glucose and fructose ones, which had equivalent levels. All sugar mean concentrations increased between October and December and then remained relatively constant. Minor differences were

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 1** Distribution of the number of genotypes by class of raw values for fruit attributes in the CxM population measured on 10/08/2012 (first column), 12/03/2012 (second column), and 02/27/2013 (third column). Mean values of the two parents are indicated by arrows: clementine (green) and mandarin (orange). FM, fresh matter</figcaption></figure>

<figure><img src="image_6.png" /></figure>

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

observed between the two parents regarding the glucose content (% of DM and % of FM). For acids, citric acid presented the highest mean concentration, especially in October where it was about 30-fold higher than that of malic acid. During maturation, citric acid decreased while malic acid increased, except in “Willow leaf” mandarin. Despite these variations during fruit maturation, citric acid remained the predominant organic acid. The pattern noted in the progeny was similar to that of clementine. The total acidity and citric acid concentration continuously declined from December until February, especially for mandarin. Nevertheless, malic acid did not show the same variation pattern since its concentration increased slightly until December and then remained constant. During maturation, mandarin fruits were significantly more acidic than clementine fruits, which showed a lower malic acid concentration but a higher citric acid concentration. The mandarin acidity level reached in February was in line with the known maturity period for

this citrus fruit (January–February). The acidity of clementine in December reached its low marketing limit under the protected geographical identification label (IGP Clementine de Corse). For other traits such as TSS, differences were minor between clementine and mandarin for the first two dates (October and December) but they increased thereafter.

High variability was observed within the population. Average trait means varied over the three dates. For fruit mass, equatorial diameter, juice percentage, pH, acidity, and TSS, the range of variation over the fruit maturation period was approximately 1.5- to twofold. The variability within the population evolved differently over time, depending on the parameter, while being almost stable for fruit mass, equatorial diameter, juiciness, TSS, and malic acid. On the other hand, it increased for pH and the three soluble sugars, while it decreased for acidity and citric acid. For organic acids and sugars, the range of variation was 3- to 50-fold over the fruit maturation period. However, the variability decreased

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373
<figure><img src="image_7.png" /><figcaption>**Fig. 2** Distribution of the number of genotypes by class of raw concentrations by 100 g of dry matter (%DM) for sugars and acids in the CxM population measured in 10/08/2012 (first column), 12/03/2012 (second column), and 02/27/2013 (third column). Arrows indicate mean values for clementine (green) and mandarin (orange)</figcaption></figure>

during maturation for citric acid to the same extent in % of DM as in % of FM.

Phenotypes with much higher and/or lower values than the highest and lowest values estimated for the two parents were observed for fruit mass, equatorial diameter, juice percentage, TSS, glucose, fructose, and sucrose. Indeed, the majority of fruit traits segregated in a transgressive manner. For example, in October, fruit mass ranged from 15.7 to 78.5 g in the population, despite the very small difference between parents (39.1–39.4 g). Conversely, acidity and citric acid were distributed essentially within the range of the parental values. Most traits, such as equatorial diameter, juice percentage, and sucrose content (in % of DM), presented a normal distribution. However, some traits such as acidity and pH deviated from normality. Therefore, appropriate transformations (ln or square root) were applied to unskew their distributions (Tables 1 and 2). The continuous variation pattern indicates that the studied traits were

controlled by several genes, so they were classified as quantitatively inherited.

## Genetic correlation and heritability

Correlation coefficients calculated between BLUPs of the genetic values are detailed in Fig. 3. Several traits appeared to be jointly correlated and some correlations between traits varied during maturation. As expected, fruit mass and equatorial diameter—highly correlated with each other—were also correlated with most of the studied traits measured in October, such as juice percentage, fructose, and sucrose. Fruit mass and equatorial diameter were negatively correlated with acidity and citric acid throughout maturation. Among sugars, the strongest positive correlations were observed, throughout maturation, between glucose and fructose, i.e., ranging from 0.50 to 0.98 depending on the date. Sucrose and TSS were jointly positively correlated in

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

<figure><img src="image_8.png" /></figure>
<table><caption>**Table 1** Transformations of raw phenotypic values of fruit attributes to unskew distributions, and broad sense heritability</caption><thead><tr><th>Fruit traits</th><th>Sampling dates</th><th>Transformation</th><th>Heritability (H<sup>2</sup>)</th></tr></thead><tbody><tr><td rowspan="3">Fruit mass (g)</td><td>October</td><td>ln</td><td>0.81</td></tr><tr><td>December</td><td>-</td><td>0.81</td></tr><tr><td>February</td><td>ln</td><td>0.8</td></tr><tr><td rowspan="3">Equatorial diameter (mm)</td><td>October</td><td>ln</td><td>0.79</td></tr><tr><td>December</td><td>-</td><td>0.8</td></tr><tr><td>February</td><td>ln</td><td>0.73</td></tr><tr><td rowspan="3">Juice percentage</td><td>October</td><td>-</td><td>0.82</td></tr><tr><td>December</td><td>-</td><td>0.75</td></tr><tr><td>February</td><td>-</td><td>0.8</td></tr><tr><td rowspan="3">pH</td><td>October</td><td>-</td><td>0.67</td></tr><tr><td>December</td><td>ln</td><td>0.94</td></tr><tr><td>February</td><td>-</td><td>0.89</td></tr><tr><td rowspan="3">Acidity (g/100 g)</td><td>October</td><td>ln</td><td>0.81</td></tr><tr><td>December</td><td>-</td><td>0.65</td></tr><tr><td>February</td><td>ln</td><td>0.75</td></tr><tr><td rowspan="3">TSS</td><td>October</td><td>Square root</td><td>0.82</td></tr><tr><td>December</td><td>ln</td><td>0.71</td></tr><tr><td>February</td><td>Square root</td><td>0.61</td></tr></tbody></table>

ln neperian logarithm

<table><caption>**Table 2** Transformations of raw phenotypic values of organic acids and sugars to unskew distributions, and broad sense heritability</caption><thead><tr><th>Fruit trait</th><th>in</th><th>Sampling dates</th><th>Transformation</th><th>Heritability (H<sup>2</sup>)</th><th>in</th><th>Sampling dates</th><th>Transformation</th><th>Heritability (H<sup>2</sup>)</th></tr></thead><tbody><tr><td rowspan="3">Citric acid</td><td rowspan="3">FM</td><td>October</td><td>Square root</td><td>0.78</td><td rowspan="3">DM</td><td>October</td><td>Square root</td><td>0.78</td></tr><tr><td>December</td><td>ln</td><td>0.74</td><td>December</td><td>ln</td><td>0.73</td></tr><tr><td>February</td><td>ln</td><td>0.82</td><td>February</td><td>ln</td><td>0.83</td></tr><tr><td rowspan="3">Malic acid</td><td rowspan="3">FM</td><td>October</td><td>ln</td><td>0.79</td><td rowspan="3">DM</td><td>October</td><td>ln</td><td>0.79</td></tr><tr><td>December</td><td>ln</td><td>0.73</td><td>December</td><td>ln</td><td>0.81</td></tr><tr><td>February</td><td>ln</td><td>0.85</td><td>February</td><td>Square root</td><td>0.77</td></tr><tr><td rowspan="3">Glucose</td><td rowspan="3">FM</td><td>October</td><td>Square root</td><td>0.67</td><td rowspan="3">DM</td><td>October</td><td>Square root</td><td>0.64</td></tr><tr><td>December</td><td>-</td><td>0.56</td><td>December</td><td>-</td><td>0.6</td></tr><tr><td>February</td><td>ln</td><td>0.82</td><td>February</td><td>Square root</td><td>0.75</td></tr><tr><td rowspan="3">Fructose</td><td rowspan="3">FM</td><td>October</td><td>Square root</td><td>0.7</td><td rowspan="3">DM</td><td>October</td><td>-</td><td>0.68</td></tr><tr><td>December</td><td>-</td><td>0.59</td><td>December</td><td>-</td><td>0.63</td></tr><tr><td>February</td><td>Square root</td><td>0.72</td><td>February</td><td>Square root</td><td>0.73</td></tr><tr><td rowspan="3">Sucrose</td><td rowspan="3">FM</td><td>October</td><td>Square root</td><td>0.74</td><td rowspan="3">DM</td><td>October</td><td>-</td><td>0.71</td></tr><tr><td>December</td><td>-</td><td>0.54</td><td>December</td><td>-</td><td>0.5</td></tr><tr><td>February</td><td>Square root</td><td>0.62</td><td>February</td><td>-</td><td>0.61</td></tr></tbody></table>

ln neperian logarithm, FM in % of fresh matter, DM dry matter

December and February. Both acidity and pH were correlated with citric and malic acid contents.

For all traits, broad-sense heritability (H<sup>2</sup>) values (repeatability among the 10 fruit replicates) were quite high (> 0.5) (Tables 1 and 2). They ranged from 0.64 to 0.82, 0.50 to 0.94, and 0.61 to 0.89 for traits measured in October, December, and February, respectively. In

October, fruit mass, acidity and TSS showed the highest heritability (> 0.8). In December, the highest heritability values (> 0.8) were observed for fruit mass, equatorial diameter, pH, and malic acid. However, in February, the traits showing > 0.8 heritability were fruit mass, juice percentage, pH, citric acid FM and DM, malic acid FM, and glucose FM (Tables 1 and 2).

415

416

417

418

419

420

421
<figure><img src="image_9.png" /><figcaption>**Fig. 3** Genotypic correlations (Spearman) between fruit characteristics measured during maturation on 10/08/2012 (D1), 12/03/2012 (D2), and 02/27/2013 (D3), based on genotypic BLUPs</figcaption></figure>

422

## Genetic linkage maps

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

A total of 551 SNP and 94 SSR markers were genotyped in clementine × mandarin offspring. Out of these, 622, 618, and 275 markers were selected to construct the consensus, clementine, and mandarin maps, respectively (Additional Table 1). Among these markers, 333 segregated at 1:2:1, 268 segregated at 1:1, and 21 segregated at 1:1:1:1. The consensus map and the previously published clementine reference map (Ollitrault et al. 2012a) shared 551 common markers. A comparative analysis of these two maps showed high syntenity and colinearity, with very few inversions and distance differences (Additional Fig. 3). However, LG3 on the reference clementine genetic maps was split into two sub-linkage groups on the consensus genetic map as well as on our clementine genetic map (Additional Table 1). There is little resolution on the position of the markers due to the small size of the population, which

results in many co-locations of markers. The multiplicity of markers at the same locus is non-informative and lengthens the computer processing when detecting QTLS. To facilitate QTL analysis, the map density was reduced by removing markers with several missing data and with the same or very close positions (< 0.1 cM), but without modifying the map coverage. These reduced density maps are hereafter called framework maps. The final numbers of markers retained for the framework consensus, clementine, and mandarin maps were 310, 277, and 147, respectively. SSR and SNP markers were grouped in 10 linkage groups on the consensus and clementine maps and in 12 linkage groups on the mandarin map. There was a greater number of chromosomes divided into several linkage groups on the mandarin map due to the lower number of markers: three referenced linkage groups were represented by two linkage groups each. The consensus, clementine, and mandarin maps, respectively, covered 795.7 cM, 809.8 cM,

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

<figure><img src="image_10.png" /></figure>
457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

and 629.7 cM, which corresponded to 74%, 75%, and 58% of the genome, respectively, compared to the clementine genetic reference map (Ollitrault et al. 2012a). Linkage groups had a mean distance of 2.6, 3.3, and 4.7 cM between adjacent markers on the consensus, clementine, and mandarin maps, respectively. Some linkage groups had large marker intervals. Linkage groups 6 and 7 had intervals ranging from 16 to 24 cM on the three maps. Moreover, linkage group 2 on the mandarin map had one 18 cM interval. Another large interval was observed at the extremity of linkage group 8 on the clementine map. Common markers within the consensus and parental maps allowed a between-map comparison of their marker orders. Except for minor changes, strong collinearity was observed especially between the clementine and consensus maps.

## QTL identification

472

QTL analysis overview: QTL detection was performed using a model with both additive and dominant effects and genotypic BLUPs at each date on the consensus (Con) and both parental (C and M) framework maps. The LOD score of significant QTLs ranged from 3.6 to 8.3. We only retained QTLs detected by MQM and confirmed by a Kruskal-Wallis test. A total of 28 QTLs were identified on the consensus map for all traits except glucose FM and citric acid FM during maturation (Table 3), with 1–3 QTLs per trait and date. Nine QTLs were detected in October, 10 in December, and 9 in February. QTLs were found on all LGs, except LG6 and LG1. The proportion of the total variation (R²) ranged from 13.1 to 34.1%. Sixteen QTLs showed an R² ranging from 10 to 20%, 9 QTLs from 20 to 30%, while 2 QTLs had an R² of

473

474

475

476

477

478

479

480

481

482

483

484

485

486

<table><caption>**Table 3** List of QTLs detected on the consensus map</caption><thead><tr><th>Traits</th><th>Dates</th><th>LG</th><th>Max LOD peak</th><th>LOD GW</th><th>Nearest marker</th><th>Map position (cM)</th><th>Confidence interval (cM)</th><th>R<sup>2</sup> (%)</th><th>Kruskal-Wallis analysis</th></tr></thead><tbody><tr><td rowspan="10">Fruit mass (g)</td><td rowspan="3">October</td><td>2</td><td>5.1</td><td>4.1</td><td>CiC5209-05</td><td>118.9</td><td>116.9-120.8</td><td>21.2</td><td>*******</td></tr><tr><td>4</td><td>4.6</td><td>4.1</td><td>CiC0279-03</td><td>1.0</td><td>0.0-7.3</td><td>15.11</td><td>****</td></tr><tr><td>5</td><td>4.1</td><td>4.1</td><td>CiC1891-02</td><td>0.0</td><td>0.0-5.9</td><td>13.8</td><td>*******</td></tr><tr><td rowspan="3">December</td><td>8</td><td>5.2</td><td>4.1</td><td>CiC0598-01</td><td>10.8</td><td>8.6-13.7</td><td>18.7</td><td>*****</td></tr><tr><td>2</td><td>5.3</td><td>4.2</td><td>PKF-M-186</td><td>118.9</td><td>116.9-120.8</td><td>16.8</td><td>*******</td></tr><tr><td>3</td><td>5.3</td><td>4.2</td><td>CiC5796-12</td><td>85.4</td><td>83.6-86.4</td><td>13.1</td><td>****</td></tr><tr><td rowspan="3">February</td><td>8</td><td>6.9</td><td>4.2</td><td>CiC0100-04</td><td>44.7</td><td>41.0-48.7</td><td>22.7</td><td>*******</td></tr><tr><td>2</td><td>4.5</td><td>4.2</td><td>PKF-M-186</td><td>118.9</td><td>116.9-120.8</td><td>18.9</td><td>*******</td></tr><tr><td>4</td><td>4.7</td><td>4.2</td><td>CHI-M-170</td><td>0.0</td><td>0.0-3.6</td><td>15.9</td><td>****</td></tr><tr><td rowspan="3">Equatorial diameter (mm)</td><td rowspan="2">October</td><td>2</td><td>4.4</td><td>4.2</td><td>PKF-M-186</td><td>119.3</td><td>116.9-120.8</td><td>13.7</td><td>*******</td></tr><tr><td>8</td><td>7.6</td><td>4.2</td><td>CiC0100-04</td><td>45.7</td><td>41.8-48.7</td><td>25.1</td><td>*******</td></tr><tr><td rowspan="3">December</td><td>9</td><td>6.8</td><td>4.2</td><td>MEST1201</td><td>52.7</td><td>46.7-56.3</td><td>27.3</td><td>*******</td></tr><tr><td>2</td><td>3.7</td><td>3.5</td><td>CiC3457-01</td><td>124.8</td><td>122.3-124.8</td><td>16.2</td><td>****</td></tr><tr><td>7</td><td>6.5</td><td>4.4</td><td>CiC5979-03</td><td>0.0</td><td>0.0-2.0</td><td>26.1</td><td>****</td></tr><tr><td>TSS (°Brix)</td><td>February</td><td>8</td><td>4.3</td><td>4.3</td><td>LCY2-P-243</td><td>49.8</td><td>48.7-50.1</td><td>19.8</td><td>*******</td></tr><tr><td>Acidity (g/100 g)</td><td>October</td><td>2</td><td>4.0</td><td>4</td><td>CiC3457-01</td><td>124.8</td><td>119.3-124.8</td><td>17.5</td><td>*******</td></tr><tr><td rowspan="2">Malic acid DM</td><td rowspan="2">October</td><td>8</td><td>5.9</td><td>4.2</td><td>CiC0598-01</td><td>11.8</td><td>8.6-13.7</td><td>24</td><td>*******</td></tr><tr><td>8</td><td>4.4</td><td>4.2</td><td>MEST086</td><td>16.7</td><td>13.3-18.9</td><td>18.9</td><td>****</td></tr><tr><td rowspan="2">Citric acid DM</td><td rowspan="2">October</td><td>2</td><td>5.9</td><td>4.2</td><td>PKF-M-186</td><td>118.9</td><td>116.9-120.8</td><td>24.2</td><td>*******</td></tr><tr><td>4</td><td>4.7</td><td>3.5</td><td>CiC5078-07</td><td>4.6</td><td>0.0-9.0</td><td>20.1</td><td>****</td></tr><tr><td rowspan="2">Malic acid FM</td><td rowspan="2">December</td><td>9</td><td>3.6</td><td>3.5</td><td>CiC2768-01</td><td>69.0</td><td>64.1-86.6</td><td>15.5</td><td>*******</td></tr><tr><td>9</td><td>8.1</td><td>4.3</td><td>MEST149</td><td>58.2</td><td>56.6-60.0</td><td>33.2</td><td>*******</td></tr><tr><td rowspan="2">Glucose DM</td><td rowspan="2">February</td><td>2</td><td>4.7</td><td>4.2</td><td>CiC6122-04</td><td>108.4</td><td>99.5-111.2</td><td>27.3</td><td>*******</td></tr><tr><td>3</td><td>4.6</td><td>4.2</td><td>CiC3742-04</td><td>4.6</td><td>3.3-7.6</td><td>14.8</td><td>****</td></tr><tr><td rowspan="2">Fructose DM</td><td rowspan="2">December</td><td>9</td><td>8.3</td><td>4.3</td><td>MEST149</td><td>57.6</td><td>56.6-59.2</td><td>34.1</td><td>*******</td></tr><tr><td>9</td><td>5.4</td><td>4.3</td><td>CiC5567-01</td><td>66.2</td><td>65.2-68.5</td><td>24.0</td><td>*******</td></tr><tr><td>Sucrose DM</td><td>February</td><td>9</td><td>4.2</td><td>4.2</td><td>MEST149</td><td>57.6</td><td>56.6-59.2</td><td>17.8</td><td>*******</td></tr><tr><td rowspan="2">Fructose FM</td><td rowspan="2">December</td><td>9</td><td>4.2</td><td>4.2</td><td>CiC3536-01</td><td>65.7</td><td>64.6-67.7</td><td>17.9</td><td>****</td></tr><tr><td>5</td><td>4.2</td><td>4.2</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

DM dry matter, FM fresh matter, LG linkage group, GW genome wide, R²% total variance explained/ Significance levels

****0.005; *****0.001; ******0.0005; *******0.0001
more than 30%. Co-localization between QTLs for different traits was observed at several locations (Fig. 4). The majority of QTLs detected on the consensus map were also detected on the clementine map (Figs. 4 and 5; Table 3; Additional Table 2). Fewer QTLs were detected on the mandarin map than on the consensus and clementine maps (Figs. 5 and 6). Six additional QTLs were detected on the two parental maps but not on the consensus map (Figs. 4, 5, and 6).

Fruit mass and equatorial diameter: For fruit mass, the greatest number of QTLs was found in February on LG2 (Con and C maps), LG3 (Con and C maps), and LG8

(bottom part, on the 3 maps) (Figs. 4, 5, and 6). The QTL on LG2 was also present in October. On the upper part of LG8, an additional QTL for fresh mass was detected in December on the 3 maps. In October, 2 QTLs were found on LG4 and 5 on the consensus map and on LG5 on the clementine map. The percentage of total variance explained by each of these fruit mass QTLs ranged from 13.1 to 26.7%. QTLs for equatorial diameter colocalized with QTLs for fresh mass at the same dates on LG 2, 4, and 8 (bottom part). Diameter QTLs explained from 13.7 to 29.5% of total variance.

498
499
500
501
502
503
504
505
506
507
508

<figure><img src="image_11.png" /><figcaption>**Fig. 4** QTL location on consensus genetic map for fruit attributes analyzed during three maturation dates and determined by interval mapping and multiple QTL model. Linkage groups are labelled as LG1–LG9. QTLs are listed on the right of each linkage group. Distances are in cM (Kosambi's function). Vertical lines represent</figcaption></figure>

1-LOD confidence intervals, and horizontal ticks indicate the positions of the LOD peaks. For each confidence interval, the trait is followed by the percentage of total variance explained by the QTL. QTLs detected in October, December, and February are drawn in green, blue and red, respectively
<figure><img src="image_12.png" /><figcaption>**Fig. 5** QTL location on clementine genetic map for fruit attributes analyzed during three maturation dates and determined by inter- val mapping and multiple QTL model. Linkage groups are labelled as LG1–LG9. QTLs are listed on the right of each linkage group. Distances are in cM (Kosambi's function). Vertical lines represent</figcaption></figure>

1-LOD confidence intervals, and horizontal ticks indicate the posi- tions of the LOD peaks. For each confidence interval, the trait is followed by the percentage of total variance explained by the QTL. QTLs detected in October, December, and February are drawn in green, blue and red, respectively

Juiciness: Only one QTL, located on LG9, was associ- ated with the juice percentage in December on the consen- sus and clementine maps, and explained 27.3 and 28.3%, respectively, of the total variation (Figs. 4 and 5).

TSS: On the consensus and clementine maps, one QTL explaining 19.8 and 18.9%, respectively, of the total varia- tion was identified on LG8 in February (Figs. 4 and 5). On

the mandarin map, no QTL for TSS was detected regard- less of the maturation date (Fig. 6).

pH and acidity: In October, one QTL for pH, explaining 16.2 and 19.9% of the variance, was detected on LG2 on the consensus and clementine maps (Figs. 4 and 5). A second QTL accounting for 26.1% of the total variance was located on LG7 on the consensus map in December only. For acidity,

516

517

518

519

520

521

522

509

510

511

512

513

514

515
<figure><img src="image_13.png" /><figcaption>**Fig. 6** QTL location on mandarin genetic map for fruit attributes analyzed during three maturation dates and determined by interval mapping and multiple QTL model. Linkage groups are labelled as LG1–LG9. Names of the markers and QTLs are listed on the right of each linkage group. Distances in cM (Kosombi's function) are in the left of each linkage group. With respect to QTL localization, the high-</figcaption></figure>

est probability is indicated by vertical line within the 1-LOD confidence intervals situated between two vertical lines. For each confidence interval, the trait is followed by the percentage of total variance explained by the QTL. QTLs detected in October, December, and February are drawn in green, blue, and red, respectively

one QTL was collocated with the pH QTL on LG2 on the consensus and clementine maps in October. A single acidity QTL was found in December on LG8 for clementine only.

**Sugars:** In February, QTLs associated with glucose, fructose, and sucrose expressed in DM were mapped in the same linkage group (LG9) on the consensus and clementine maps. The QTL for fructose DM overlapped that for glucose DM. These QTLs showed the greatest effects in this study, contributing more than 30% to the total variance (Additional Table 2). Other QTLs were detected on the consensus map

for sucrose FM on LG5 and fructose DM on LGs 2 and 3 in December, yet only the LG2 QTL was also found on the clementine map. On the mandarin map, while fructose DM and glucose DM QTLs colocalized on LG9 in February, one QTL for sucrose DM was detected on LG6 at the same date (Fig. 6). Two QTLs for fructose DM were found on LG2 and LG3 on the consensus map in December, which accounted for 27.3 and 14.8% of the total variance, respectively. QTLs controlling fructose FM and sucrose FM in December were found on LG9 and LG5 on the consensus maps, respectively,

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542
while only the LG9 QTL was also found on the clementine map. About 18% of the variance was explained on the three maps (Table 3; Additional Table 2).

Organic acids: One QTL for citric acid DM was identified in October on LG2 on the consensus and clementine maps. This QTL overlapped the single QTL for acidity and explained 24.2% of the total variance. Two QTLs were detected for malic acid DM on the consensus and both parental maps, i.e., one in October and the other in December. These two QTLs overlapped on LG8. For malic acid expressed in FM, two QTLs were located on LG 4 (20.1 and 23.2%) and LG9 (15.5 and 15.6%) on the consensus and clementine maps in December (Table 3; Additional Table 2).

## Discussion

### QTL detection accuracy and power

The number of markers is insufficient to saturate the genetic map and to have a large coverage of the genome, but the low number of markers does not decrease the power of detection because the LD is extended in a bi-parental progeny; it only decreases the precision of localization of the QTL in case of too big gap. Charmet carried out simulations to evaluate the effect of marker density on QTL detection for one-QTL models. Detection power and length of confidence intervals of both QTL location and QTL effect were not affected by marker density between 5 and 20 cM for a population size of $N=200$ (Charmet 2000). The number of markers is low because we sorted out the markers that were very close to each other (at the same locus) and therefore were not useful for the localization of QTLs since the locus was tagged by another marker. Moreover, for mandarin, the low number of markers is explained by the fact that this citrus variety is weakly heterozygous and so could explain some large gaps on maps, and thus, it was difficult to obtain markers that were both heterozygous for mandarin and homozygous for clementine, because this cross is a backcross. Nevertheless, we detected and localized QTLs for fruit quality traits with strong effect and a larger population of hybrids would be more profitable to detect more QTLs with smaller effects.

We used MQM to optimize QTL detection, especially in the case of two linked QTLs, since it increases the QTL detection power and the QTL position estimation precision, while allowing us to map additional QTLs located on the same chromosome (Paterson 1997). Twenty-eight QTLs of quality attributes in citrus were found on the consensus map, most of which (24) had a relatively marked effect ($R^2>15\%$). Both parental and consensus maps were used to yield complementary results. Indeed, QTLs with dominant allelic effects like fructose DM identified on chromosomes 2 and 3 and pH mapped on chromosome 7 in December were

detected only on the consensus map. Conversely, the detection accuracy could be higher in parental maps for QTLs with additive effects only, as shown by the 6 additional QTLs identified only on the parental maps and not on the consensus map. Overall, 34 QTLs were identified throughout the maturation period.

The effectiveness of molecular markers associated with detected QTLs should be determined as the percentage of the explained genetic variance, instead of the phenotypic variance, because fluctuations in phenotypic values as a result of environmental variations blurs the marker effects (Nishio et al. 2011). The use of BLUP values thus improved the QTL detection power by removing part of the environmental variance per tree since the variance between fruits might be at slightly different stages of maturity at harvest time. For instance, this variability in fruit maturity was studied for clementine and used to determine the “harvestability window” (Julhia et al. 2019). Indeed, several studies have demonstrated the effectiveness of the BLUP method for species such as apple (Segura et al. 2009) or grapevine (Doligez et al. 2013) where the experimental material had been phenotyped over several years or under different growing conditions.

### QTL detection

In our case, all traits had high heritability ($H^2>0.56$) during maturation, which increased our chances of detecting QTLs. However, the high heritability was also due to the absence of annual variation and the low variance between fruits. A main limitation of this experimentation plot was related to the number of individuals present in the population. With 105 individuals, it could be assumed that the detected QTLs were those with marked and possibly significant effects (Staub et al. 1996; Beavis 1998). The percentage of total genotypic variation explained by QTLs detected throughout maturation ranged from 11.7 to 37%. Fruit mass, equatorial diameter, malic acid FM, and fructose DM were controlled by more than one QTL. The presence of several QTLs showed the complexity of the metabolic pathways. QTLs of fruit mass detected during maturation did not explain 100% of the total variance, which suggests the presence of other undetectable QTLs and/or epistatic effects that could explain the remaining percentage of total variation. To our knowledge, this is the first report of QTL mapping of fruit attributes in citrus at three sampling dates during maturation. This QTL study showed that the traits were probably not controlled by the same QTLs during maturation. In our case, the use of more complete maps and a high number of measurements (replicates) provided more accurate QTL detection results. Indeed, QTL detection is known to be affected by environmental conditions, which represent a major source of variability (Rousseaux et al. 2005; Kenis et al. 2008).

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642
643 Trait correlations and QTL co-location

Several traits were clustered mainly on linkage groups 2, 4, 8, and 9 irrespective of the evaluation dates. These clusters may have reflected a pleiotropic effect of one QTL or tight linkage between at least two QTLs. A QTL with a pleiotropic effect indicates the segregation of a single QTL controlling several traits due to related metabolisms or causal relationships between traits (De Vienne and Causse 1998). Several QTL clusters for fruit maturation and agronomic traits were detected in previous studies in many species, including tomato (Monforte et al. 1999), peach (Etienne et al. 2002), apple (Liebhard et al. 2003), and citrus (Sugiyama et al. 2011). Common or close QTL locations have often been observed for correlated attributes (Paterson et al. 1991). These correlations could suggest candidate regions for future studies to gain further insight into these traits. In this study, QTL co-locations were observed for the majority of the studied fruit traits, including fruit mass, equatorial diameter, pH, acidity, sugar, and acid contents. QTL clusters varied during maturation. Indeed, some of them were stable throughout maturation, while others were identified for only one or two maturation dates. Fruit quality traits vary with the degree of maturation in citrus. Therefore, our QTL analysis results might also have been affected by fruit maturity heterogeneity (Ladanyia 2008). This lack of stability of fruit quality QTLs during maturation thus suggests that some fruit traits are not governed by the same locus during maturation.

Fruit mass and size: In our study, fruit mass QTLs were detected on LGs 2, 3, 4, 5, and 8. Some of them had been detected in other studies. For instance, the QTL for fruit mass located on LG4 may have corresponded to the previously reported FW4.2 QTL (Yu et al. 2016) and FWq3 QTL (Imai et al. 2017). Besides, the fruit mass QTL detected on LG3 may have corresponded to FWq1 detected in 2013 and FWq2 detected in 2013 and 2014 (Imai et al. 2017). Moreover, another fruit mass QTL was mapped in the same region of LG8 in two QTL mapping studies, confirming that this is a single major QTL (Yu et al. 2016; Minamikawa et al. 2017). On another hand, Imai et al. (2018) found four fruit mass QTLs situated on LGs 2, 3, 5, and 7. However, although three of them were detected on the same LG in our study, they were not mapped in the same region (Imai et al., 2018). This comparison with the findings of the four mentioned studies was possible because the QTLs were positioned on the same scaffolds 2, 3, 4, 5, and 8 on the clementine reference map, which presented high synteny with our consensus linkage map.

Ting and Attaway (1971) reported that fruit mass and equatorial diameter variation patterns are interrelated during fruit development and maturation, thus highlighting that it was normal to find overlapping QTLs between these two traits: one QTL co-location on different chromosomes at

two maturation dates on the consensus map, i.e., 2 and 4 in October and 2 and 8 in February. This correlation between fruit mass and fruit diameter was also found in mandarin (Yu et al. 2016). Moreover, common genomic regions may control both fruit mass and size in *C. volkameriana* × *P. trifoliata* (Garcia et al. 2000) and *C. clementina* hybrid × *C. grandis* hybrid (Asins et al. 2015) populations. The involvement of several regions of different chromosomes in the control of these two traits revealed their complex genetic determinism, as already noted in other species such as apple (Kenis et al. 2008) and tomato (Grandillo et al. 1999).

Acidity and fruit mass: QTLs of titratable acidity and fruit mass measured in October were mapped at the end of LG2 on the consensus map, and negative correlations were obtained between acidity and fruit mass. In citrus, the first fruit development stage is characterized by slow fruit growth rates but high cell division (Bain 1958), whereas phase II constitutes a fast growth period where fruit increases in size mostly by cell enlargement and water accumulation (Iglesias et al. 2007; Tadeo et al. 2008). QTLs for fruit mass could thus be associated mainly with the cell expansion process, as shown in tomato (Bertin et al. 2007). Cell growth and enlargement depend on water and carbon compound accumulation (Yakushiji et al. 1996). Admittedly, citrus fruit act as carbohydrate storage sinks during the cell enlargement stage and thereafter (Mehouachi et al. 1995; Cercós et al. 2006). Fruit accumulates a considerable amount of organic acids in juice sac cell vacuoles (Etienne et al. 2013a, b). The high acid concentration could result in enhanced sink strength, thus facilitating carbohydrate accumulation (Hockema and Etxeberria 2001) and increased fruit size (Agustí et al. 2002). Nevertheless, October, i.e., the period when our research began, corresponds to phase III under Mediterranean conditions (Jacquemond and Agostini 2013). During this stage, accumulated organic acids are progressively catabolized, thereby implying acid reduction. Meanwhile, fruits continue to increase in size. If fruit mass is positively linked to acidity during the first half of stage II and negatively from the second half of stage II and during stage III, this may be explained by the high carbohydrate supplies on acidity. In fact, according to Etienne et al. (2013a, b), abundant carbohydrate supplies result in higher fruit mass and increased respiration. Yet, at this physiological stage, sugars stored in the vacuole may no longer be available as a respiratory substrate. Consequently, organic acids sustain respiration, thus leading to a decline in acidity. Antoine et al. (2016) demonstrated that smaller and more acidic fruits were the result of carbohydrate depletion (through water stress or modification of leaf/fruit ratio), which could confirm and explain the negative correlation between acidity and fruit mass in our study.

Furthermore, Nishawy et al. (2015) reported that citrus fruit size was inversely associated with the organic acid

695

696

697

698

699

700

701

702

703

704

705

706

707

708

709

710

711

712

713

714

715

716

717

718

719

720

721

722

723

724

725

726

727

728

729

730

731

732

733

734

735

736

737

738

739

740

741

742

743

744

745

746

747

<figure><img src="image_14.png" /></figure>
level. In fact, they demonstrated that overexpression of the
dehydration responsive element binding gene (*CgDREB*) led
to a dramatic decrease in citrus fruit mass, in turn prompt-
ing higher organic acid accumulation. This gene is located
on scaffold 1 of the clementine reference genome (between
bases 24 667 693 and 24 668 568) (Wu et al. 2014). In the
present study, no QTLs were detected in this scaffold, so
this gene did not co-localize with the fruit mass and acidity
QTL that was positioned on linkage group 2, corresponding
to scaffold 2 of the reference genome. Sadka et al. (2000)
previously showed that the citrus fruit organic acid content
was affected by fruit size. Taken together, these results con-
firmed and explained the negative correlation between acid-
ity and fruit mass.

The negative correlation between acidity and fruit mass
and the colocation of their QTLs had also been observed in
mandarin (Yu et al. 2016). Selection in regions containing
QTLs for several traits should therefore be carefully consid-
ered to avoid raising conflicts between breeding objectives.
Fine mapping in these regions with larger populations could
help distinguish between real pleiotropy and very close
linkage.

Acidity and pH: The fact that a single QTL was found to
control both pH and titratable acidity was an expected result
since titration highlights the quantity of all acid functions
and ions while the pH measures only the hydrogen potential,
thereby indicating that these two features were correlated.
This QTL is also involved in the control of citric acid, which
is the major organic acid in citrus juice (Monselise 1986;
Iglesias et al. 2007; Zhou et al. 2018). A gene encoding pH,
i.e., a pH-like gene (*CrMYB73*), whose expression was found
to be positively correlated with citric acid accumulation, was
isolated from citrus fruit (Li et al. 2015). This gene is located
in scaffold 2 of the reference genome like the mapped acid-
ity QTL, but at the opposite end. Li et al. (2015) identified a
citrus transcription factor (CitERF13), regulating citric acid
accumulation in citrus fruit cells. The location of the Cit-
ERF13 corresponding gene in scaffold 1 did not match the
position of the acidity QTL on the map in this study.

Acidity and organic acids: In the current study, acidity
and citric acid shared a common QTL on the consensus map.
In addition to their high correlation, the malate concentration
range was much lower than that of citric acid. This suggests
that citric acid markedly contributes to the acidity. Indeed,
citrus fruit acidity is primarily determined by the citric acid
concentration, representing 80–90% of total organic acids
(Baldwin 1993). Note that while only one QTL was found
for citric acid and acidity, malic acid seemed to be controlled
by a much more complex biochemical determinism, since
five QTLs located in different linkage groups during matu-
ration were identified. This confirmed the complexity of
malate accumulation in fruit cells, with a large number of
metabolic pathways involved. Malic acid is either converted

via oxaloacetate (OAA) from phosphoenolpyruvic (PEP) in
the cytosol or produced through the cycle in mitochondria
(Etienne et al. 2013a, b).

Fruit size, organic acids, and sugars: PKF-M-186 was
the SNP marker with the highest LOD score for fruit mass,
diameter, and citric acid DM located on LG2. The PKF-M-
186 marker is located in a gene coding for phosphofructoki-
nase, i.e., an enzyme involved in sugar and acid pathways
(Echeverria and Valich, 1989). Organic acids are derived
from sugars (Hussain et al. 2017). Furthermore, low pH,
the main determinant of malate and citrate accumulation,
increases sucrose hydrolysis into fructose and glucose
(Etienne et al. 2013a, b). Cleaving sucrose enables the sink
to amplify the existing sugar gradient between the sink and
phloem, thereby allowing continued sucrose movement
toward the sink cell (Hockema and Etxeberria 2001) and
resulting in increased fruit size (Agustí et al. 2002). More-
over, Lin et al. (2015) demonstrated that the fructokinase
gene was upregulated during maturation, indicating that the
sucrose metabolism to organic acid metabolism flux change
was enhanced. This highlights the close link between sugar
and acid pathways. We hence think that the phosphofruc-
tokinase gene including the PKF-M-186 SNP marker is a
candidate for controlling citric acid and fruit size variations.

On the other hand, our results showed that QTLs for acidity did not co-localize with sugar QTLs. Note that the same observation was reported in studies on tomato (Causse et al. 2001), mandarin varieties (Goldenberg et al. 2014), and a population derived from a cross between “Murfcott” tangor and “Pera” sweet orange, where sugar and acidity QTLs were mapped in two different linkage groups (Curtolo et al. 2017). However, Asins et al. (2015) showed a colocation between QTLs of acidity and sugars on genetic maps developed from a mandarin × pummelo progeny. This discrepancy could be due to differences in parental genotypes assessed in the studies. The different locations of QTLs for sweetness and acidity suggest that it could be possible to improve both traits independently.

TSS and fruit mass: For sugar content (TSS), one QTL
was mapped on LG8 at 49.8 cM with an R² = 19.8%. This
QTL may correspond to that previously detected on linkage
group 8 in a bi-parental QTL mapping study that used a
mandarin F1 population derived from “Fortune” × “Murfcott”
(Yu et al. 2016). On linkage group 8, the QTL for TSS was
very near to but did not overlap a QTL for fruit mass in
February, whereas these traits were not significantly cor-
related. In agreement with our results, these two traits were
also reportedly independent in mandarin (Imai et al. 2017).
However, in tomato, a clear co-localization between QTLs
of fruit mass and TSS was shown, suggesting pleiotropy
(Goldman et al. 1995; Saliba-Colombani et al. 2001). Our
results suggest that TSS and fruit mass could be indepen-
dently modified in citrus.

801

802

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

848

849

850

851

852

853
FM vs DM: It is noteworthy that in the current study, QTLs for sugars and acids expressed in DM and QTLs for sugars and acids expressed in FM did not co-localize despite the high correlation between these traits. Interestingly, QTLs detected for sugars and acids expressed in DM were twice as numerous as QTLs expressed in FM. This difference was also found in a study by Prudent et al. (2009) in tomato. These latter authors detected 11 QTLs for sugar DM vs 6 QTLs for sugar FM with only two overlapping QTLs. This confirms the fact that traits expressed in fresh and dry matter are not always correlated (Woodward and Clearwater 2008). The reason for this difference in QTL detection could be explained by the fact that fresh matter depends on the water content (Bolarin et al. 2001), which is largely influenced by environmental factors such as the soil water status and climatic factors (Jones and Tardieu 1998). Moreover, environmental factors represent a major source of variability that affects QTL detection (Rousseaux et al. 2005; Kenis et al. 2008).

The very high correlation between fructose and glucose DM ($r=0.95$) was consistent with the co-location of QTLs for those traits on linkage group 9 in February, with both QTLs presenting major effects ($R^2>30\%$). In grape berries, one QTL was detected for these two hexoses, which were highly correlated (Chen et al. 2015). These QTLs could facilitate breeding programs by helping control fruit sweetness. While fructose and glucose contribute to total sugars, their QTLs were not co-located with the QTL for TSS, whereas they were expected to be related. In February, both fructose and glucose were negatively correlated with sucrose DM. A QTL for sucrose was found in the same chromosome region as glucose and fructose QTLs. Sucrose is the main form of translocated carbon in citrus (García-Luis et al. 1991). It is transported from leaves to the juice sac head, where it is partitioned into glucose and fructose (Goldschmidt and Koch 1996). The sucrose degradation pathway activated during fruit maturation to generate fructose and glucose (Lin et al. 2015) could explain the negative correlation between sucrose and the two other sugars and the co-localization of their QTLs. To the best of our knowledge, QTLs for organic acids and soluble sugars in this study were the first such QTLs to be mapped in citrus, while not corresponding to any previously reported QTLs. Therefore, at least 15 of the QTLs reported in this paper are novel QTLs.

Coincidence of QTL position with other citrus studies: Our study is the only one in citrus that assessed the position and number of QTLs of a trait over the course of fruit ripening (3 dates spaced about 6 weeks apart and QTLs differed between measurement dates. Mainly the other citrus studies on QTLs of fruit traits were assessed at a single date, without considering the fruit maturity evolution of each hybrids of the progenies (Curtolo et al. 2017; Imai et al. 2017). Therefore, we find very little similarity between the different

studies. Curtolo et al. 2017 studied the QTLs of fruit quality traits by using a progeny combining Murcott tangor and Pera sweet orange and DArTseq™ molecular markers. They did not visualize the position of the QTLs in the genetic map but they gave a physical position on the sweet orange genome of the markers related to the QTLs. The position of QTLs for common quality traits (acidity, TSS and juiciness) between their and our study is different. Only one QTL of fruit diameter localizes on the same linkage group 8. Imai et al. (2017) studied the QTLs of sugar content and fruit diameter of two Japanese mandarins (Imai et al. 2017). Only the position of one of the fruit mass QTLs at the beginning of GL 3 seems to coincides between that study and ours. Comparatively to Yu et al. (2016) among the 48 detected QTLs, only the QTL of TSS seems to be located at the same position in our study at the beginning of the LG 8. None of the other QTLs from their study matches ours. The low coincidence between the genetic maps can be explained by different reasons. Many previous studies based on reduced genome complexity representation (GBS, DartSeq) were based on a sequence mapping on the orange genome with a numbering and orientation of the chromosomes different from those of the reference genome (the clementine tree) used in the present study. In the absence of a pan genome, it is difficult to make a link between our results and those resulting from mapping on other references and many studies did not make the link with the genomic positions on a given reference but only in relation to the genetic map.

## Conclusion

Fruit quality traits showed major variation in progeny during maturation. QTLs related to fruit quality traits were localized on several linkage groups using consensus and parental genetic maps. Many of these traits were correlated. Thirty-four QTLs for the major physical and chemical components of fruit were detected at three different fruit maturation dates. Notably, we detected at least 15 novel QTLs for sugars and acids. Malic acid was controlled by several QTLs during maturation, revealing a more complex genetic determinism than citric acid, for which only one QTL was detected. Several QTL clusters were identified. The majority of QTLs were mapped in three linkage groups (2, 8, and 9). This suggests that some QTLs may have pleiotropic effects. Although fine mapping is required to decipher such clusters, they could be useful in marker-assisted selection and thus increase the efficiency of future breeding programs.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s11295-022-01571-w.

**Acknowledgements** The authors thank Laurent Gomez of INRAE Avignon for giving us access to his laboratory.

<figure><img src="image_15.png" /></figure>
**Funding** This work is part of Hajer Khefifi’s PhD granted by CIRAD and INRA and the University of Carthage (Tunisia). This work received financial support from the European Regional Development Fund under the framework PO FEDER-FSE Corse 2014–2020 number 247SAEUFEDER1A, project called Innov’Agrumes (ARR-18/517 CE, synergie number: CO 0009083).

**Data availability** All the markers used for genetic mapping were previously published by Ollitrault et al. (2012a), and their corresponding GenBank accession numbers can be found in the Additional file 1 of this publication.

## Declarations

**Conflict of interest** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Agustí M, Martinez-Fuentes A, Mesejo C (2002) Citrus fruit quality. Physiological basis and techniques of improvement. Agrociencia VI 2:1–16

Albertini M-V, Carcouet E, Pailly O, Gambotti C, Luro F, Berti L (2006) Changes in organic acids and sugars during early stages of development of acidic and acidless citrus fruit. J Agric Food Chem 54:8335–8339

Antoine S, Pailly O, Gibon Y, Luro F, Santini J, Giannettini J, Berti L (2016) Short- and long-term effects of carbohydrate limitation on sugar and organic acid accumulation during mandarin fruit growth. J Sci Food Agric 96:3906–3914

Ashrafi H, Foolad MR (2015) Characterization of early blight resistance in a recombinant inbred line population of tomato: II. Identification of QTLs and their co-localization with candidate resistance. Genes Adv Stud Biol 7:149–168

Asins MJ, Bernet GP, Ruiz C, Cambra M, Guerri J, Carbonell EA (2004) QTL analysis of citrus tristeza virus-citradia interaction. Theor Appl Genet 108:603–611

Asins MJ, Raga V, Bernet GP, Carbonell EA (2015) Genetic analysis of reproductive, vegetative and fruit quality traits to improve Citrus varieties. Tree Genet Genomes 11:117

Bain JM (1958) Morphological, anatomical, and physiological changes in the developing fruit of the Valencia orange, Citrus sinensis (L.) Osbeck. Aust J Bot 6:1–23. https://doi.org/10.1071/BT9580001

Baldwin EA (1993) Citrus fruit. In: Seymour G, Taylor J, Tucker G (eds) Biochemistry of fruit ripening. Springer Netherlands, 107–149. https://doi.org/10.1007/978-94-011-1584-1_4

Beavis WD (1998) QTL analyses: power, precision, and accuracy. In: Patterson HA (ed) Molecular dissection of complex traits. CRC Press, Boca Raton, Boston, London, New York, Washington, pp 145–162

Bertin N, Lecomte A, Brunel B, Fishman S, Génard M (2007) A model describing cell polyploidization in tissues of growing fruit as related to cessation of cell proliferation. J Exp Bot 58:1903–1913

Bolarin MC, Estan MT, Caro M, Romero-Aranda R, Cuartero J (2001) Relationship between tomato fruit growth and fruit osmotic potential under salinity. Plant Sci 160:1153–1159

Cabodevila VG, Cambiaso V, Rodríguez GR et al (2021) A segregating population from a tomato second cycle hybrid allows the identification of novel QTL for fruit quality traits. Euphytica 217:6

Calenge F, Drouet D, Denancé C, Van de Weg W, Brisset MN, Pau-lin JP, Durel CE (2005) Identification of a major QTL together with several minor additive or epistatic QTLs for resistance to fire blight in apple in two related progenies. Theoret Appl Genetics 111:128–135

Causse M, Saliba-Colombani V, Lesschaeve I, Buret M (2001) Genetic analysis of organoleptic quality in fresh market tomato. 2. Mapping QTLs for sensory attributes. Theor Appl Genet 102:273–283

Cercós M, Soler G, Iglesias DJ, Gadea J, Forment J, Talón M (2006) Global analysis of gene expression during development and ripening of citrus fruit flesh. A proposed mechanism for citric acid utilization. Plant Mol Biol 62:513–527

Charmet G (2000) Power and accuracy of QTL detection: simulation studies of one-QTL models. Agronomie 20:309–323

Chen J, Wang N, Fang LC, Liang ZC, Li SH, Wu BH (2015) Construction of a high-density genetic map and QTLs mapping for sugars and acids in grape berries. BMC Plant Biol 15:1–14

Curtolo M, Cristofani-Yaly M, Gazaffi R, Takita MA, Figueira A, Machado MA (2017) QTL mapping for fruit quality in Citrus using DArTseq markers. BMC Genomics 18(1):289

De Vienne D, Causse M (1998) La cartographie et la caractérisation des locus contrôlant la variation des caractères quantitatifs. In: D. de Vienne (ed) Les marqueurs moléculaires en génétique et bio-technologies végétales. 2nd edn. INRA, Paris pp 89–118

Doligez A, Bertrand Y, Farnos M, Grolier M, Romieu C, Esnault F, Dias S, Berger G, François P, Pons T, Ortigosa P, Roux C, Houel C, Laucou V, Bacilieri R, Péros JP, This P (2013) New stable QTLs for berry weight do not colocalize with QTLs for seed traits in cultivated grapevine (Vitis vinifera L.). BMC Plant Biol 13:217

Doyle JJ, Doyle JL (1987) A Rapid DNA Isolation Procedure for Small Quantities of Fresh Leaf Tissue. Phytochem Bull 19:11–15

Echeverria E, Valich J (1989) Enzymes of sugar and acid metabolism in stored ‘Valencia’ oranges Journal of the American Society for Horticultural Science (USA)

Etienne C, Rothan C, Moing A, Plomion C, Bodénès C, Svanella-Dumas L, Cosson P, Pronier V, Monet R, Dirlewanger E (2002) Candidate genes and QTLs for sugar and organic acid content in peach [Prunus persica (L.) Batsch]. Theor Appl Genet 105:145–159

Etienne A, Génard M, Bancel D, Benoit S, Bugaud C (2013a) A model approach revealed the relationship between banana pulp acidity and composition during growth and post harvest ripening. Sci Hortic 162:125–134

Etienne A, Génard M, Lobit P, Mbeguié-A-Mbéguié D, Bugaud C (2013b) What controls fleshy fruit acidity? A review of malate and citrate accumulation in fruit cells. J Exp Bot 64:1451–1469

Fang DQ, Federici CT, Roosevelt ML (1997) Development of molecular markers linked to a gene controlling fruit acidity in citrus. Genome 40:841–849

Froelicher Y, Dambier D, Bassene JB, Costantino G, Lotfy S, Didout C, Beaumont V, Brottier P, Risterucci AM, Luro F, Ollitrault P (2008) Characterization of microsatellite markers in mandarin orange (Citrus reticulata Blanco). Mol Ecol Resour 8:119–122

García MR, Asís MJ, Carbonell EA (2000) QTL analysis of yield and seed number in Citrus. Theor Appl Genet 101:487–493
1077

Garcia-Lor A, Curk F, Morillon R, Ancillo G, Luro F, Navarro L, Ollitrault P (2012) Nuclear phylogeny within Citrus (Rutaceae) and four related genera. *Ann Bot* 111:1–9

1078

Garcia-Luis A, Didehvar F, Guardiola J, Baker D (1991) The transport of sugars in developing fruits of satsuma mandarin. *Ann Bot* 68:349–357

1080

Gmitter F Jr, Grosser J, Castle W, Moore G, Khan I (2007) A comprehensive citrus genetic improvement programme. *Citrus genetics, breeding and biotechnology. Cabi, Cambridge, pp 9–18

1081

1082

Goldenberg L, Yaniv Y, Kaplunov T, Doron-Faigenboim A, Porat R, Carmi N (2014) Genetic diversity among mandarins in fruit-quality traits. *J Agric Food Chem* 62:4938–4946. https://doi.org/10.1021/jf5002414

1086

1087

1088

1089

Goldman IL, Paran I, Zamir D (1995) Quantitative trait locus analysis of a recombinant inbred line population derived from a Lycopersicon esculentum × Lycopersicon cheesmanii cross. *Theoret Appl Genet* 90:925–932

1090

1091

1092

1093

Goldschmidt EE, Koch KE (1996) Citrus. In: Photoassimilate distribution in plants and crops. 1st edition New York, pp 928–https://doi.org/10.1201/9780203743539

1094

1095

1096

Gomez L, Bancel D, Rubio E, Vercambre G (2007) The microplate reader: an efficient tool for the separate enzymatic analysis of sugars in plant tissues—validation of a micro-method. *J Sci Food Agric* 87:1893–1905

1097

1098

1099

1100

Grandillo S, Ku H, Tanksley S (1999) Identifying the loci responsible for natural variation in fruit size and shape in tomato. *Theor Appl Genet* 99:978–987

1101

1102

1103

Guo F, Yu H, Tang Z, Jiang X, Wang L, Wang X, Xu Q, Deng X (2015) Construction of a SNP-based high-density genetic map for pummelo using RAD sequencing. *Tree Genet Genomes* 11:2

1104

1105

1106

Hockema BR, Etxeberria E (2001) Metabolic contributors to drought-enhanced accumulation of sugars and acids in oranges. *J Amer Soc Hort Sci.* 126:599–605

1107

1108

1109

Houel C, Chatbanyong R, Doligez A, Rienth M, Foria S, Luchaire N, Roux C, Adivèze A, Lopez G, Farnos M, Pellegrino A, This P, Romieu R, Torregrosa L (2015) Identification of stable QTLs for vegetative and reproductive traits in the microvine (Vitis vinifera L.) using the 18 K Infinium chip. *BMC Plant Biol* 15:205

1110

1111

1112

1113

1114

Hussain SB, Shi CY, Guo LX, Kamran HM, Sadka A, Liu YZ (2017) Recent advances in the regulation of citric acid metabolism in citrus fruit. *Crit Rev Plant Sci* 36:241–256

1115

1116

1117

Iglesias DJ, Cercós M, Colmenero-Flores JM, Naranjo MA, Ríos G, Carrera E, Ruiz-Rivero O, Lliso I, Morillon R, Tadeo FR, Talon M (2007) Physiology of citrus fruiting Brazilian. *J Plant Physiol* 19:333–362

1118

1119

1120

1121

Imai A, Yoshioka T, Hayashi T (2017) Quantitative trait locus (QTL) analysis of fruit-quality traits for mandarin breeding in Japan. *Tree Genet Genomes* 13:79

1122

1123

1124

Imai A, Nonaka K, Kuniga T, Yoshioka T, Hayashi T (2018) Genome-wide association mapping of fruit-quality traits using genotyping-by-sequencing approach in citrus landraces, modern cultivars, and breeding lines in Japan. *Tree Genet Genomes* 14:24

1125

1126

1127

1128

Jacquemond C, Agostini D (2013) Connaitre l'arbre et son fonctionnement. In: Les clémentiniers et autres petits agrumes. Eds: Jacquemond C, Curk F, Heuzet M. Quae Versailles, pp.165–182.

1129

1130

1131

Jones HG, Tardieu F (1998) Modelling water relations of horticultural crops: a review. *Sci Hortic* 74:21–46

1132

1133

1134

1135

1136

1137

Julhia L, Belmin R, Meynard JM, Pailly O, Casabianca F (2019) Acidity drop and coloration in clementine: implications for fruit quality and harvesting practices. *Archive Ouverte d'INRAE. https://doi.org/10.3389/fpls.2019.000754*

1138

1139

1140

Kenis K, Keulemans J, Davey MW (2008) Identification and stability of QTLs for fruit quality traits in apple. *Tree Genet Genomes* 4:647–661

1141

1142

Kosambi D (1943) The estimation of map distances from recombination values. *Ann Eugen* 12:172–175

Ladanyia M (2008) Citrus fruit: biology, technology and evaluation. Elsevier, USA

1143

1144

Li S, Liu X, Xie X, Sun C, Grierson D, Yin X, Chena K (2015) CrMYB73, a PH-like gene, contributes to citric acid accumulation in citrus fruit. *Sci Hortic* 197:212–217

1145

1146

1147

Liebhard R, Kellerhals M, Pfammatter W, Jertmini M, Gessler C (2003) Mapping quantitative physiological traits in apple (Malus × domestica Borkh.). *Plant Mol Biol* 52:511–526

1148

1149

1150

Lin PQ, Wang C, Dong W, Jiang Q, Wang D, Li S, Chen M, Liu C, Sun C, Chen K (2015) Transcriptome and metabolome analyses of sugar and organic acid metabolism in Ponkan (Citrus reticulata) fruit during fruit maturation. *Gene* 554:64–74

1151

1152

1153

1154

Luro F, Costantino G, Terol J, Argout X, Allario T, Wincker P, Talon M, Ollitrault P, Morillon R (2008) Transferability of the EST-SSRs developed on Nules clementine (Citrus clementina Hort ex Tan) to other Citrus species and their effectiveness for genetic mapping. *BMC Genomics* 9:287

1155

1156

1157

1158

1159

Mehouachi J, Zaragoza S, Agusti M, Talon M, Primo-Millo E (1995) Defoliation increases fruit abscission and reduces carbohydrate levels in developing fruits and woody tissues of Citrus unshiu. *Plant Sci* 107:189–197

1160

1161

1162

1163

Minamikawa MF, Nonaka K, Kaminuma E, Kajiya-Kanegae H, Onogi A, Goto S, Yoshioka T, Imai A, Hamada H, Hayashi T, Matsumoto M, Katayose Y, Toyoda A, Fujiyama A, Nakamura Y, Shimizu T, Iwata H (2017) Genome-wide association study and genomic prediction in citrus: potential of genomics-assisted breeding for fruit quality traits. *Sci Rep* 7:4721

1164

1165

1166

1167

1168

1169

Monforte AJ, Asins MJ, Carbonell EA (1999) Salt tolerance in Lycopersicon spp. VII. Pleiotropic action of genes controlling earliness on fruit yield. *Theor Appl Genet* 98:593–601

1170

1171

1172

Monselise S (1986) Citrus. In: Monselise S (ed) Handbook of fruit set development. CRC Press, USA, pp 87–108

1173

1174

1175

Mou J, Zhang Z, Qiu H, Lu Y, Zhu X, Fan Z, Zhang Q, Ye J, Fernie AR, Cheng Y, Deng X, Wen W (2021) Multiomics-based dissection of citrus flavonoid metabolism using a citrus reticulata × Poncirus trifoliata population. *Hortic Res* 8:56

1176

1177

1178

Nishawy E, Sun X, Ewas M, Ziaf K, Xu R, Wang D, Amar M, Zeng Z, Cheng Y (2015) Overexpression of Citrus grandis DREB gene in tomato affects fruit size and accumulation of primary metabolites. *Sci Hortic* 192:460–467

1179

1180

1181

1182

Nishio S, Yamada M, Awamura Y, Takada N, Saito T (2011) Environmental variance components of fruit ripening date as used in both phenotypic and marker-assisted selection in Japanese pear breeding. *HortScience* 46:1540–1544

1183

1184

1185

1186

Ollitrault P, Terol J, Chen C, Federici CT, Lotfy S, Hippolyte I, Ollitrault F, Bérard A, Chauveau A, Cuenca J, Costantino G, Kacar Y, Mu L, Garcia-Lor A, Froelicher Y, Aleza P, Boland A, Billot C, Navarro L, Luro F, Roosevelt ML, Gmitter FG, Talon M, Brunel D (2012) A reference genetic map of C. clementina hort. ex Tan.; citrus evolution inferences from comparative mapping. *BMC Genomics* 13:593

1187

1188

1189

1190

1191

1192

1193

Ollitrault P, Terol J, Garcia-Lor A, Bérard A, Chauveau A, Froelicher Y, Belzile C, Morillon R, Navarro L, Brunel D, Talon M (2012) SNP mining in C. clementina BAC end sequences; transferability in the Citrus genus (Rutaceae), phylogenetic inferences and perspectives for genetic mapping. *BMC Genomics* 13:13

1194

1195

1196

1197

1198

Ollitrault P (2019) Mapping and exploiting the citrus genome. In: Yahia EM (ed) Achieving sustainable cultivation of tropical fruits. Burleigh Dodds Science Publishing, Cambridge, pp 3–42. (Burleigh Dodds Series in Agricultural Science)

1199

1200

1201

1202

1203

1204

1205

1206

1207

Ollitrault P and Luro F (1997) Les agrumes. In: Charrier A, Jacquot M, Hamon S, Nicolas D (eds) Amélioration des plantes tropicales. CIRAD, Orstom, pp 13–36

1208

1209

1210

Paterson AH (1997) Molecular dissection of complex traits. CRC-Press, Boca Raton
Paterson AH, Damon S, Hewitt JD, Zamir D, Rabinowitch HD, Lin- coln SE, Lander ES, Tanksley SD (1991) Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments. Genetics 127:181–197
Prudent M, Causse M, Genard M, Tripodi P, Grandillo S, Bertin N (2009) Genetic and physiological analysis of tomato fruit weight and composition: influence of carbon availability on QTL detec- tion. J Exp Bot 60:923–937
Quilot B, Kervella J, Génard M, Lescourret F (2005) Analysing the genetic control of peach fruit quality through an ecophysi- ological model combined with a QTL approach. J Exp Bot 56:3083–3092
Rawandoozi ZJ, Hartmann TP, Carpenedo S et al (2020) Identifica- tion and characterization of QTLs for fruit quality traits in peach through a multi-family approach. BMC Genomics 21:522
Robinson GK (1991) That BLUP is a good thing: the estimation of random effects. Statist Sci 6:15–32. https://doi.org/10.1214/ss/ 1177011926
Roose ML (2007) Mapping and marker-assisted selection. In: Khan I (ed) Citrus genetics, breeding and biotechnology. CABI, UK, pp 275–285
Rousseaux MC, Jones CM, Adams D, Chetelat R, Bennett A, Pow- ell A (2005) QTL analysis of fruit antioxidants in tomato using Lycopersicon pennellii introgression lines. Theor Appl Genet 111:1396–1408
Royston P (1995) Remark AS R94: a remark on algorithm AS 181: the W-test for normality. Appl Stat 44:547–551
Rymenants M, Van de Weg E, Auwerkerken A, De Wit I, Czech A, Nij- land B, Heuven H, De Storme N, Keulemans W (2020) Detection of QTL for apple fruit acidity and sweetness using sensorial evalu- ation in multiple pedigreed full-sib families. Tree Genet Genomes 16:71. https://doi.org/10.1007/s11295-020-01466-8
Sadka A, Dahan E, Or E, Cohen L (2000) NADP+-isocitrate dehydro- genase gene expression and isozyme activity during citrus fruit development. Plant Sci 158:173–181
Saliba-Colombani V, Causse M, Langlois D, Philouze J, Buret M (2001) Genetic analysis of organoleptic quality in fresh market tomato. 1. Mapping QTLs for physical and chemical traits. Theo- ret Appl Genetics 102:259–272
Segura V, Durel C-E, Costes E (2009) Dissecting apple tree archi- tecture into genetic, ontogenetic and environmental effects: QTL mapping. Tree Genet Genomes 5:165–179
Shimada T, Fujii H, Endo T, Ueda T, Sugiyama A, Nakano M, Kita M, Yoshioka T, Shimizu T, Nesumi H, Ikoma Y, Moriguchi T, Omura M (2014) Construction of a citrus framework genetic map anchored by 708 gene-based markers. Tree Genet Genomes 10(4):1001–1013
Siviero A, Cristofani M, Furtado E, Garcia AF, Coelho AG, Machado M (2006) Identification of QTLs associated with citrus resistance to Phytophthora gummosis. J Appl Genet 47:23–28
Spiegel-Roy P, Goldschmidt EE (1996) The biology of citrus. Cam- bridge University Press, Israel
Stam P (1993) Construction of integrated genetic linkage maps by means of a new computer package: Join Map. Plant J 3:739–744
Staub JE, Serquen FC, Gupta M (1996) Genetic markers, map con- struction, and their application in plant breeding. HortScience 31:729–741
Sugiyama A, Omura M, Matsumoto H (2011) Quantitative trait loci (QTL) analysis of carotenoid content in citrus fruit. J Jpn Soc Hortic Sci 80:136–144

Tadeo F, Cercos M, Colmenero-Flores JM, Iglesias D, Naranjo MA, Rios G, Carrera E, Ruiz-Rivero O, Lliso I, Morillon R, Ollitrault P, Talon M (2008) Molecular physiology of development and quality of citrus. Adv Bot Res 4:147–223
Terol J, Naranjo MA, Ollitrault P, Talon M (2008) Development of genomic resources for Citrus clementina: characterization of three deep-coverage BAC libraries and analysis of 46,000 BAC end sequences. BMC Genomics 9:423
Ting S, Attaway J (1971) Citrus fruits. In: The biochemistry of fruits and their products 2 Academic Press, London, New York, pp 107–169
Tozlu I, Guy CL, Moore GA (1999) QTL analysis of Na+ and Cl- accumulation related traits in an intergeneric BC1 progeny of Citrus and Poncirus under saline and nonsaline environments. Genome 42:692–705
Van Ooijen J (2006) JoinMap® 4, Software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, Wageningen, Netherlands
Van Ooijen J (2009) MapQTL 6 Software for the mapping of quan- titative trait loci in experimental populations of diploid species. Kyazma BV, Wageningen, Netherlands
Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78
Woodward TJ, Clearwater MJ (2008) Relationships between ‘Hayward’ kiwifruit weight and dry matter content. Postharvest Biol Technol 48:378–382
Wu GA, Prochnik S, Jenkins J, Salse J, Hellsten U, Murat FX, Ruiz M, Scalabrin SJ, Takita MA, Labadie K, Poulain J, Couloux A, Jabbari I, Cattonaro F, Fabbro CD, Pinosio S, Zuccolo A, Chap- man J, Grimwood J, Tadeo FR, Estornell LH, Muñoz-Sanz JV, Ibanez V, Herrero-Ortega A, Aleza P, Pérez-Pérez J, Ramón D, Brunel D, Luro F, Chen C, Farmerie WG, Desany B, Kodira C, Mohiuddin M, Harkins T, Fredrikson K, Burns P, Lomsadze A, Borodovsky M, Reforgiato G, Freitas-Astúa J, Quetier F, Navarro L, Roosevelt M, Wincker P, Schmutz J, Morgante M, Machado MA, Talon M, Jaillon O, Ollitrault P, Gmitter G, Rokhsa D (2014) Sequencing of diverse mandarin, pummelo and orange genomes reveals complex history of admixture during citrus domestication. Nat Biotechnol 32:656–662
Xu Q, Chen LL, Ruan X, Chen D, Zhu A, Chen C, Bertrand D, Jiao WB, Hao BH, Lyon MP et al (2013) The draft genome of sweet orange (Citrus sinensis). Nat Genet 45(1):59–66
Yakushiji H, Nonami H, Fukuyama T, Ono S, Takagi N, Hashimoto Y (1996) Sugar Accumulation Enhanced by Osmoregulation in Satsuma Mandarin Fruit. J Amer Soc Hort Sci. 121(3):466–472
Yu Y, Chen C, Gmitter FG (2016) QTL mapping of mandarin (Cit- rus reticulata) fruit characters using high-throughput SNP mark- ers. Tree Genet and Genomes 12:77. https://doi.org/10.1007/ s11295-016-1034-7
Zhou Y, He W, Zheng W, Tan Q, Xie Z, Zheng C, Hu C (2018) Fruit sugar and organic acid were significantly related to fruit Mg of six citrus cultivars. Food Chem 259:278–285

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322