Identification of two QTLs associated with high fruit acidity in apple using pooled genome sequencing analysis

Abstract

Acidity is a critical component determining apple fruit quality. Previous studies reported two major acidity quantitative trait loci (QTLs) on linkage groups (LGs) 16 (Ma) and 8 (Ma3), respectively, and their homozygous genotypes mama and ma3ma3 usually confer low titratable acidity (TA) (<3.0 mg ml−1) to apple fruit. However, apples of genotypes Ma- (MaMa and Mama) or Ma3- (Ma3Ma3 and Ma3ma3) frequently show an acidity range spanning both regular (TA 3.0–10.0 mg ml−1) and high (TA > 10 mg ml−1) acidity levels. To date, the genetic control for high-acidity apples remains essentially unknown. In order to map QTLs associated with high acidity, two genomic DNA pools, one for high acidity and the other for regular acidity, were created in an interspecific F1 population Royal Gala (Malus domestica) × PI 613988 (M. sieversii) of 191 fruit-bearing progenies. By Illumina paired-end sequencing of the high and regular acidity pools, 1,261,640 single-nucleotide variants (SNVs) commonly present in both pools were detected. Using allele frequency directional difference and density (AFDDD) mapping approach, one region on chromosome 4 and another on chromosome 6 were identified to be putatively associated with high acidity, and were named Ma6 and Ma4, respectively. Trait association analysis of DNA markers independently developed from the Ma6 and Ma4 regions confirmed the mapping of Ma6 and Ma4. In the background of MaMa, 20.6% of acidity variation could be explained by Ma6, 28.5% by Ma4, and 50.7% by the combination of both. The effects of Ma6 and Ma4 in the background of Mama were also significant, but lower. These findings provide important genetic insight into high acidity in apple.

Introduction

Apples are a favorite fruit worldwide and are consumed fresh or as processed products such as apple juice, contributing important nutrients to human health. Consumer preference in choosing apples is largely influenced by fruit acidity levels although other fruit quality attributes such as color, crispness, juiciness, and sugar content also play a considerable role1. Several organic acids are detectable in mature apples, including malic acid, citric acid, fumaric acid, succinic acid, maleic acid, and others2. However, malic acid is predominant and largely determines fruit acidity levels. Apples are classified into three groups according to titratable acidity (TA) and consumer acceptance: low (<3.0 mg ml−1 in malic acid equivalent), regular (3.0–10.0 mg ml−1), and high (>10.0 mg ml−1)3,4. Apples in low and high-acidity ranges usually are not suitable for fresh consumption. Due to its essential role in fruit quality, fruit acidity is routinely measured with TA to ensure fruit taste and flavor in apple breeding programs5,6,7. Meanwhile, fruit acidity also has been investigated intensively in apple genetics studies.

Early inheritance studies concluded that the low-acidity trait was governed by a single recessive gene ma3,8,9. The Ma locus had been genetically mapped to linkage group (LG) 16 in the apple genome10. This finding was confirmed in multiple studies using quantitative trait locus (QTL) mapping approach4,11,12,13. The Ma QTL has been characterized in detail, leading to identification of a strong candidate gene Ma1 (MDP0000252114) encoding a protein closely related to the tonoplast localized aluminum-activated malate transporter (ALMT) 9 in Arabidopsis14,15,16. The genetic cause for low acidity had been attributed to the premature stop codon leading mutation from G to A at base 1455 in the Ma1 open reading frame (ORF), which effectively truncates 84 amino acids at the MA1 C-terminus, presumably creating a malfunctioned MA114. Indeed, a latest study demonstrates that the C-terminus of MA1 is essential for malate transport as the Ma1-1455A (allele ma1) protein showed significantly lower malate transport activity than the Ma1-1455G (Ma1) protein when expressed in both Xenopus laevis oocytes and Nicotiana benthamiana cells17.

Another important QTL for fruit acidity was identified on LG 812,13,18,19,20,21, which was named Ma320 following Ma1 and Ma2 (MDP0000244249), the two ALMT-like genes identified under QTL Ma14. Candidate genes for the Ma3 QTL were proposed as well, such as MDP0000294924 encoding a ring finger and CHY zinc finger domain-containing protein18, MDP0000582174 encoding a MYB transcription factor and MDP0000239624 encoding a malic enzyme19, and MdPP2Ch (MDP0000141481) and MdSAUR37 (MDP0000153382)22. However, Ma3 also has been shown to be less or none of a factor in other studies. For example, the QTL was detected only in one of the 2 years studied, and was regarded only one of the six minor QTLs for TA in the Telamon × Braeburn cross11. In addition to the major QTLs Ma and Ma3, minor fruit acidity QTLs were identified on LGs 2, 10, 13, 15, and 1711, and LGs 1 and 64. These studies demonstrate that the genetic control of apple fruit acidity involves multiple loci with multiple alleles that can differ by genetic background of the germplasm considered.

In our effort to identify and characterize Ma1 in population Royal Gala (Malus domestica) × PI 613988 (M. sieversii), we observed huge variation in fruit acidity in progenies of genotypes MaMa (4.1–18.7 mg ml−1) and Mama (3.2–14.1 mg ml−1) due to the presence of high-acidity progenies4. Interestingly, the effect of Ma3 was undetectable in the population4, suggesting there are other unknown genes involved in high fruit acidity in the background of Ma- (MaMa and Mama). The objective of this study was to identify QTLs associated with high acidity in the background of Ma-.

Materials and methods

Plant materials

The F1 population GMAL 4595 of 222 individuals and their associated fruit acidity data used in this study were obtained from an interspecific cross between Royal Gala (Malus domestica; Mama) and PI 613988 (M. sieversii; Mama) and were reported previously4. The seed parent Royal Gala is a widely grown cultivar, and the pollen parent is an elite selection of M. sieversii collected from Kazakhstan, which is a major progenitor species of domestic apples23,24. The fruit titratable acidity (TA) data from 191 of the 222 seedling trees were evaluated at harvest in 20104 (Fig. 1a, b). Briefly, fruit were harvested when their Cornell Starch Index (CSI)25 were scored between CSIs 4 and 6. For each progeny, five to ten fruit were combined into a single pool and used for juice extraction. The measurement of juice TA was conducted using an autotitrator (Metrohm 848 Titrino Plus and Metrohm 869 Compact Sample Changer, Herisau, Switzerland), which showed a wide range of fruit acidity from 1.0 to 18.7 (mg ml−1) in malic acid equivalent. Based on the preference for fresh consumption of apples, fruit TA below, within and above the acceptable range (3–10 mg ml−1) were called low, regular, and high acidity, respectively.

Fig. 1: Schematic representation of the pooled genome-sequencing-based allele frequency directional difference and density (AFDDD) mapping of recessive high-acidity QTLs.
figure1

a Creation of population GMAL 4595 (Royal Gala × PI 613988) of 222 F1 plants. The two parents were assumed to be heterozygous at the high-acidity locus. The vertical lines in blue represent homolog chromosomes. The red and purple short bars represent the high-acidity allele and SNVs in relation to the reference genome, respectively. b Evaluation of fruit titratable acidity (TA) in 191 F1 plants. The low (1.0–2.8 mg ml−1), regular (3.2–10.0 mg ml−1), and high (10.0–18.7 mg ml−1) acidity ranges were shown in gray, green, and red scales, respectively. The number of progenies in each TA range is given in parenthesis. c Creation of the high and regular acidity pools with 18 and 20 F1 segregants, respectively. The segregants were assumed to be homozygous at the high-acidity locus in the high-acidity pool while comprising three different genotypes (two heterozygotes and one homozygote without the high-acidity allele) in the regular acidity pool. As such, the expected informative SNV allele frequencies (AF) were 100% in the high-acidity pool and 33.3% in the regular acidity pool. d Sequencing the two genome pools and mapping the sequence reads against the apple reference genome26. e Calling out SNVs and identifying those informative for mapping, which were characterized with AF > 85% in the high-acidity pool and AFDD > 41.7 percentage points between high and regular acidity pools, lower than the expected difference of 51.7 (85–33.3%) percentage points to buffer variation. f AFDDD mapping of high-acidity QTLs by examining the genome-wide distributions of the informative SNVs. There were two highly significant SNV density peaks on chromosomes 4 and 6, designated Ma6 and Ma4, respectively

Genotyping at loci Ma, Ma3, and others

Genotyping at the Ma locus was conducted using a cleaved amplified polymorphic sequence (CAPS) marker CAPS1455 (Table S1)14. The associated restriction enzyme BspHI (New England Biolabs, Ipswich, MA, USA) directly detects the premature stop codon mutation from TGG1455 (allele Ma1) to TGA1455 (low-acidity allele ma1) at the 1455th base in the Ma1 ORF14. Genotyping at the Ma3 locus was carried out using the sequence-tagged site (STS) markers MdPP2CH and MdSAUR37 (Table S1) developed previously22, which were located at the 8.7th and 11.6th Mb on chromosome 8 in the GDDH13 apple reference genome26, respectively. Markers CAPS1455, MdPP2CH, and MdSAUR37 were analyzed using agarose (1.5%) gel electrophoresis. In addition, the genotypic data of simple sequence repeat (SSR) markers CH02g09 (in the Ma3 region at the 10.0th Mb on Chromosome 8), C1902 (at the 1.5th Mb on Chr 17), and C14087 (at the 4.9th Mb on Chr 6) from a previous study27 were also used to help determine the effect of other putative acidity QTLs (Table S1).

Construction and sequencing of two genomic DNA pools

Two genomic DNA pools, one for high acidity (12.6 ± 2.5 mg ml−1) and the other for regular acidity (5.7 ± 0.6 mg ml−1), were created using 18 and 20 progenies from the GMAL 4595 population, respectively (Table S2, Fig. 1c). Since Ma3 was a non-factor in this population and the progenies in the high-acidity pool comprising five MaMa and 13 Mama progenies, similar to those in the regular acidity pool, i.e., 5 in MaMa and 15 in Mama, the acidity differences between the two pools were assumed to be caused by genetic factors other than Ma and Ma3. Genomic DNA samples were extracted from young leaves, and the DNA samples were assessed on 1% agarose gel for integrity, and were quantified with a Nanodrop 1000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). For each pool, an equal amount of DNA (1 µg) from each progeny was combined to produce the pooled genomes (Fig. 1c). Construction of the pooled genomic libraries was conducted using NEBNEXT Ultra DNA library prep kit for Illumina sequencing (New England Biolabs, E7370) with a targeted insert size of 500 bp. The libraries were sequenced in paired-end (2 × 151 bp) on an Illumina NextSeq 500 platform (Fig. 1d) at the Genomics Facility of Cornell University (Ithaca, NY, USA).

Reads mapping against the apple reference genome and calling of DNA variants

The Illumina raw reads were trimmed and filtered to remove adaptors and low quality (0.01 as quality score p limit) reads. The cleaned reads from both pools were mapped to the apple reference genome GDDH1326 using software CLC Genomic Workbench (v7.5, CLC Bio, Cambridge, MA, USA) with the following parameters: similarity 0.98, length fraction 0.8, insertion cost 3, deletion cost 3, and mismatch cost 2. DNA variants from both pools were detected using fixed ploidy (diploid) variant detection tool available in CLC Genomic Workbench. Single-nucleotide variants (SNVs) that were associated with a read coverage range outside 20–200×, or with a complex genotype were all removed. Next, SNVs that were reference alleles were removed in order to avoid double counting in our downstream analyses. Only were the SNVs that passed these filters used for further analyses.

Allele frequency directional difference and density (AFDDD) mapping

The AFDDD mapping approach28 was adapted in this study for mapping the high-acidity phenotype in the background of Ma- (Ma and Mama). Based on the approximate 3:1 (p = 0.3173) segregation between regular (105) and high (42) acidity in the Ma- progeny (Fig. 1b), the primary working hypothesis was that the high-acidity phenotype was controlled by one or few recessive genes and both parents were heterozygous at the gene loci. Alternative hypotheses were also considered to cover two possible cases: (1) the high-acidity phenotype was dominant over regular acidity but their segregation was distorted; and (2) the cross was aabb (Gala) × AaBb (PI 613988) or Aabb (Gala) × aaBb (PI 613988) and the high-acidity phenotype was controlled by two complementary genes in A-B-. With the high-acidity recessive hypothesis, the DNA variants of segregation type <hk > x <hk > would be most informative among the others28, where each letter represents one of the four DNA bases and the letters in bold represent variants in relation to the reference genome26. The alleles in each first and third positions are assumed linked in coupling phase to the recessive high-acidity allele in the two parents, respectively. We further inferred that the variants would be homozygous in the high-acidity pool, and heterozygous in the regular acidity pool. Considering the case involving a single recessive gene, the expected variant allele frequency (AF) in the high-acidity pool would be 100%, and that in the regular acidity pool would be 33.3% (Fig. 1c). Consequently, the expected variant allele frequency directional difference (AFDD) between the high and regular acidity pools would be 66.7 (100–33.3%) percentage points. In practice, we defined homozygous variants were those that were associated with AF > 85% and heterozygous variants with AF 15–85%, and informative variants were referred to those of AFDD > 41.7 percentage points between the high and regular acidity pools, a ten-percentage-point lower than the expected 51.7 (85–33.3%) percentage points to accommodate fluctuations (Fig. 1e). For mapping the high-acidity phenotype, the density of such informative DNA variants in 1-Mb moving windows was examined based on their distributions along each chromosome. Genomic regions of significantly higher SNV density than the genome average in the z-score tests were considered linked to the high-acidity phenotype (Fig. 1f). The cutoff was LODz (−log10P(z)) > 6.0.

Marker development

High-resolution melting (HRM) and SSR markers were designed using program Primer3Plus29 to target SNVs and SSRs in the genomic regions (Ma6 and Ma4) that were detected to be putatively associated with high acidity in AFDDD mapping (Table S1). The PCR and HRM analysis were performed using the CFX96-real time system (Bio-Rad Laboratories, Hercules, CA, USA) with Precision Melt Supermix (Bio-Rad Laboratories, Hercules, CA, USA) according to the manufacturer’s protocol. Briefly, 10 µl reactions with 20 ng of template DNA, 5 µl of Precision Melt Supermix, and 200 nM of forward and reverse primers were used. Two-step PCR and high-resolution melting analyses were carried out according to Precision Melt Supermix protocol. The thermo-cycling conditions included an initial DNA denaturation at 95 °C for 2 min, 40 cycles of 95 °C for 10 s, and 60 °C for 30, and a high-resolution melting analysis step comprising heteroduplex formation at 95 °C for 30 s and 60 °C for 1 min, and high-resolution melting and plate reading from 65 to 95 °C (heating rate 0.2 °C/10 s). Plate reads were incorporated after each PCR cycle and each melting step to record sample amplification and DNA melting information. Data were processed using the Bio-Rad CFX Maestro 1.0 and Bio-Rad Precision Melt Analysis 1.3 software packages. Analyses of SSR markers were performed as described previously27.

Statistical analysis

One- or two-way analysis of variance (ANOVA) and Tukey’s honest significant difference (HSD) test were performed using R version 3.3.130.

Results

Segregation of high acidity and associated markers

The high-acidity phenotype clearly segregated in population GMAL 4595. Among the 191 fruit-bearing progenies, 42, 105, and 44 were recorded with high (TA 10.0–18.7 mg ml−1), regular (3.2–10.0 mg ml−1), and low acidity (1.1–2.8 mg ml−1), indicating high fruit acidity was under the control of a few major genes. The segregation of the Ma (CAPS1455) alleles fit 1:2:1, i.e., 49 (MaMa):98 (Mama):44 (mama) in Chi-square test (p = 0.8217) (Fig. 2a, Fig. S1A). One-way ANOVA analysis of the genetic effect of Ma showed that Ma explained 61.9% (adjusted R2, p = 2.2E−16) of the fruit acidity variation in the population (Fig. 2b), confirming that Ma was a major genetic factor for apple fruit acidity in population GMAL 4595.

Fig. 2: Analysis of the genetic effect of marker CAPS1455 (Ma) in population GMAL 4595.
figure2

a Distribution of fruit acidity in the 191 F1 plants and their associated Ma genotypes. b ANOVA analysis of the genotypic effect of Ma. Alleles Ma and ma correspond to the relatively high and low acidity, respectively. c Fruit acidity levels of the F1 segregants used in the high and regular acidity pools

The effect of Ma3 could not be detected when accessed using markers/genes MdPP2CH22, CH02g0927, and MdSAUR3722 that were located at the 8.7th, 10.0th, and 11.6th Mb in the Ma3 region on chromosome 8 in the apple reference genome26. Based on how the MdPP2CH alleles were defined in relation to acidity (Fig. S1C)22, the genotypes of Gala and PI 613988 were homozygous low-acidity ma3ma3 (AA) and homozygous high-acidity Ma3Ma3 (GG), respectively, and the genotype of their F1 progeny (GMAL 4595) was heterozygous Ma3ma3 (AG) (Fig. S1C), implicating a non-segregation population for Ma3. However, both MdSAUR37 and CH02g09 segregated in the population.

Marker MdSAUR37 detected the two alleles (bands) in the population as expected (Fig. S1B), corresponding to the high (Ma3) and low (ma3) acidity alleles, respectively22. Based on their allelic profiles, the genotype of Gala would be Ma3ma3, and that of PI 613988 would be ma3ma3 (Fig. S1B). Therefore, non-parental genotypes would not be expected present in the population. However, the observed segregation was 50 (Ma3Ma3):39 (Ma3ma3):102 (ma3ma3). To account for the puzzling presence of the 50 non-parental genotype progenies, the pollen parent PI 613988 was assumed to carry a null allele of MdSAUR37, thereby having a ma3-null genotype. Under this hypothesis, the observed segregation would be 50 (Ma3-null):39 (Ma3ma3):102 (ma3ma3 & ma3-null), fitting the expected segregation ratio 1:1:2 (p = 0.3410), seemingly in support of the null-allele hypothesis.

To resolve the compound genotype (ma3ma3 & ma3-null) of MdSAUR37, SSR marker CH02g09 was used, which amplified three distinct alleles (bands) a, b, and c of approximate sizes 80-, 70-, and 65-bp, respectively, from the two parents, including alleles a and b from Gala and allele c from PI 613988 (Fig. S1D)27. However, allele c was present in only 54 of the 165 progenies of fruit that were genotyped27, indicating PI 613988 also carried a null allele (d) that could not be amplified (Fig. S1D), similar to what was inferred in the case of MdSAUR37. Since CH02g09 and MdSAUR37 were physically apart by ~1.6 Mb, and both showed a null allele in PI 613988, the c allele of CH02g09 was reasoned to be linked to the low acid allele (ma3) of MdSAUR37 in coupling phase. A genotype comparison between CH02g09 and MdSAUR37 suggested that alleles a and b in CH02g09 corresponded to the high (Ma3) and low (ma3) acidity alleles of MdSAUR37, respectively (Table S3). The segregation of the four CH02g09 alleles resulted in 25 (Ma3ma3 = ac):55 (Ma3-null = ad):29 (ma3ma3 = bc):56 (ma3-null = bd), indicating a significant distortion (p = 3.40E−4) from the expected 1:1:1:1 segregation (Table S3). ANOVA analyses demonstrated that MdSAUR37 and CH02g09 did not detect any significant effect of Ma3 in any background of Ma (Figs. S2, S3). These analyses suggested that Ma3 likely segregated as indicated by MdPP2CH in the population, i.e., Gala and PI 613988 had a homozygous low-acidity ma3ma3 and homozygous high-acidity genotype Ma3Ma3, respectively. Therefore, the role of Ma3 in fruit acidity variation was negligible among the F1 plants in the family.

A close look at the acidity distributions indicated that the segregation of low acidity was exclusively controlled by Ma as all 44 low-acidity progenies had the mama genotype (Fig. 2a). However, the segregation of high acidity could not be explained well by genotypes MaMa and Mama although fruit acidity was significantly higher (p = 8.5E−4) in MaMa (4.1–18.7 mg ml−1, n = 49) than Mama (3.2–14.1 mg ml−1, n = 98) (Fig. 2a, b). This was because the 42 high-acidity progenies comprised 20 in MaMa and 22 in Mama, suggesting that there be other fruit acidity genes in addition to Ma and Ma3. Since the segregation between regular acidity (105) and high acidity (42) fit 3:1 (P = 0.3173 in Chi-square test) in the progeny of genotypes MaMa and Mama (Fig. 2a), the high-acidity phenotype was reasoned to be recessive to regular acidity in the background of Ma-.

AFDDD mapping of high acidity

To genetically map the recessive high-acidity phenotype, the high (12.6 ± 2.5 mg ml−1) and regular (5.7 ± 0.6 mg ml−1) acidity genomic DNA pools (Fig. 2c, Table S2) were sequenced and generated 163.2 and 228.0 million 2 × 151 bp paired-end raw reads (NCBI SRA accession PRJNA604459), respectively. Removing low quality reads, 157.3 million reads in the high-acidity pool and 224.9 million in the regular acidity pool were used for reads alignment onto the apple reference genome26, respectively (Table S4). As a result, 110.7 million reads (70.4%) in the high-acidity pool and 163.0 million reads (72.5%) in the regular acidity pool were mapped to the reference genome, equivalent to genome coverages 20.1× and 30.1×, respectively (Table S4). Using the variant detection tool available in the CLC Genomics Workbench, 1,261,640 SNVs that were present in both high and regular acidity pools were identified. Filtering these DNA variants that were not only homozygous in the high-acidity pool (AF > 85%), but also characterized with allele frequency directional difference (AFDD) > 41.7 percentage points between the high and regular acidity pools identified 1,017 SNVs. These SNVs were considered informative and useful in variant AFDDD mapping of the high-acidity phenotype under the working hypothesis that the trait was controlled by one or few recessive genes.

Examining the distribution of the 1017 SNVs in 1-Mb moving windows along the reference genome uncovered that there were two significant (LODz > 6.0) and highest peaks in variant density, designated Ma6 and Ma4 on chromosomes 4 and 6, respectively (Fig. 3a), indicating putative mapping of high acidity. The peak of Ma6 (LODz = 29.9) was located in the 1 Mb region between the 8th and 9th Mb on chromosome 4 (Fig. 3b), whereas the peak of Ma4 (LODz = 39.4) was positioned between the 1st and the 2nd Mb on chromosome 6 (Fig. 3c). The second highest peak on chromosome 4 was located between the 3rd and 4th Mb region (LODz = 23.4), physically close (5 Mb) to the Ma6 peak, making it less likely an independent fruit acidity QTL from what was represented by Ma6.

Fig. 3: AFDDD mapping of high-acidity QTLs.
figure3

a Genome-wide distribution of the 1017 informative SNVs for mapping high-acidity QTLs. The dashed line indicates the cutoff LODz (−log10P(z)) 6.0. The genomic regions under the two significant peaks on chromosomes 4 and 6 were dubbed Ma6 (LODz = 29.9) and Ma4 (LODz = 39.4) (c), respectively. b, c Close-up views of the informative SNV distribution on chromosomes 4 (b) and 6 (c), respectively

Confirmation of the mapping of Ma6

To confirm the mapping of Ma6, a high-resolution melting (HRM) marker targeting SNP T>C at Chr4_08,022,967, called HRM-Ma6, and an SSR marker located at 111 kb downstream of HRM-Ma6, named SSR-Ma6 were developed and used to genotype the entire population (Fig. 4a–c). HRM analysis of the marker revealed that there were three clusters of melting curves as expected, which correspond to genotypes ma6ma6 (CC), Ma6ma6 (CT), and Ma6Ma6 (TT), respectively. The segregation of marker HRM-Ma6 was recorded with 54 ma6ma6 (CC):84 Ma6ma6 (CT):53 Ma6Ma6 (TT), fitting the expected ratio 1:2:1 in Chi-square test (p = 0.2821), suggesting HRM-Ma6 segregated normally. ANOVA analysis of the effect of marker HRM-Ma6 demonstrated that it could account for 20.6% (p = 0.0019) of TA variation in the background of MaMa (n = 49) according to adjusted R2, and 8.3% (p = 0.0062) in the background of Mama (n = 98) (Fig. 5a, b), which were highly significant, thereby confirming the mapping of Ma6 although its effect was not significant in mama (n = 44) and the entire population (Fig. S4A, B). In the background of MaMa, the progeny of genotype ma6ma6 (CC) had significantly higher TA levels (12.9 ± 3.8 mg ml−1, n = 8) than those of genotypes Ma6ma6 (CT) (8.8 ± 2.4 mg ml−1, n = 27, p = 0.0012) and Ma6Ma6 (TT) (9.6 ± 2.6 mg ml−1, n = 14, p = 0.0239) (Fig. 5a) while the difference between genotypes Ma6ma6 (CT) and Ma6Ma6 (TT) was non-significant (p = 0.5993). A similar trend, despite a lessened degree, was also observed in the background of Mama (Fig. 5b). These results indicated that homozygous genotype ma6ma6 (CC) confers high fruit acidity, supporting that a recessive gene under Ma6 is relevant for high acidity. Marker SSR-Ma6 could detect all four possible distinct alleles of the two parents, segregating the progeny into four genotype groups (Fig. 4c). The estimated recombination frequency between HRM-Ma6 and SSR-Ma6 was 3.66% (14/382).

Fig. 4: Development of HRM and SSR markers under QTLs Ma6 and Ma4 and their assays in population GMAL 4595.
figure4

a, d Screen snapshot of high-resolution melting curves for markers HRM-Ma6 under Ma6 (a) and HRM-Ma4 under Ma4 (d). Each cluster of the melting curves (in red, green, and blue) represents one of the three genotypes at the targeting SNV site as indicated. b, e Flanking sequences of the SNV targeted by markers HRM-Ma6 (b) and HRM-Ma4 (e). Their chromosome coordinates were given and the targeting SNVs were shown red font with Y = T or C and R = A or G. The nucleotides corresponding to the HRM marker forward and reverse primers were underlined in black and blue, respectively. c, f Allelic profile of markers SSR-Ma6 (c) and SSR-Ma4 (d) as determined by 6% polyacrylamide gel electrophoresis (PAGE). Lane M stands for DNA ladders

Fig. 5
figure5

ANOVA analysis of the genetic effect of HRM-Ma6 (a, b) and HRM-Ma4 (c, d) on fruit acidity in the background of MaMa (a, c) and Mama (b, d). Alleles Ma6/Ma4 and ma6/ma4 correspond to the relatively low and high acidity, respectively. M6 = Ma6; M4 = Ma4. Different letters indicate significant difference (p < 0.05) between the genotype groups in Tukey’s HSD test. See also the legend in Fig. 1b

To test if the peak between the 3rd and 4th Mb on chromosome 4 represented an independent QTL, another HRM marker (HRM2-Ma6) was developed under the peak region (Fig. S5A, B). The recombination frequency between this marker and HRM-Ma6 was 8.64% (33/382). ANOVA analysis demonstrated that the effect of this marker was significant in both MaMa (adjusted R2 = 0.190; p = 0.0029) and Mama (adjusted R2 = 0.0595; p = 0.0202) (Fig. S5C, D), but to a lower degree if compared with the effect of HRM-Ma6 in the same background of MaMa (adjusted R2 = 0.206; p = 0.0019) and Mama (adjusted R2 = 0.083; p = 0.0062) (Fig. 5a, b), respectively. Moreover, the marker also did not show a significant effect in the background of mama and in the entire population (Fig. S5E, F), similar to HRM-Ma6. These observations suggested that the lower peak region was unlikely to represent a QTL independent from Ma6.

Confirmation of the mapping of Ma4

Confirmation of the mapping of Ma4 was conducted similarly as described above. In brief, another HRM marker, called HRM-Ma4, which detected SNP genotypes ma4ma4 (AA), Ma4ma4 (AG), and Ma4Ma4 (GG) at base Chr6_1,614,796 was developed for estimating the effect of Ma4 on high acidity in population GMAL 4595 (Fig. 4d, e). Based on Chi-square test, marker HRM-Ma4 also showed a normal 1:2:1 segregation (p = 0.2925) for the three genotypes: 50 ma4ma4 (AA), 85 Ma4ma4 (AG), and 56 Ma4Ma4 (GG). In the background of MaMa (n = 49), the segregation of marker HRM-Ma4 explained 28.5% (adjusted R2; p = 1.67E−4) of the TA variation while 9.4% (p = 0.0034) in the background of Mama (n = 98), both of which were significant, confirming the mapping of Ma4 (Fig. 5c, d). Interestingly, the effect of Ma4 was also significant in mama (n = 44) and the entire population (Fig. S4C, D). A close look revealed a similar trend as observed for Ma6, i.e., the mean TA values of genotype ma4ma4 (AA) were significantly higher than those values of genotypes Ma4ma4 (AG) and Ma4Ma4 (GG) in the background of both MaMa and Mama while the differences between genotypes Ma4ma4 (AG) and Ma4Ma4 (GG) were not significant (Fig. 5c, d), similarly supporting that a recessive gene under Ma4 is critical for high acidity. An SSR marker positioned 120 kb upstream of HRM-Ma4, designated SSR-Ma4, was also developed, which was capable of detecting two distinct alleles from each of the two parents and segregating the progeny into four genotype groups (Fig. 4f). The recombination frequency between HRM-Ma4 and SSR-Ma4 was estimated 1.83% (7/382), lower than 3.66% calculated between HRM-Ma6 and SSR-Ma6 above.

Genetic interactions between Ma6 and Ma4

A two-way ANOVA analysis based on the combined genotypes between markers HRM-Ma6 and HRM-Ma4 in population GMAL 4595 (Fig. 6) showed that the two markers explained 50.7% (adjusted R2, p = 7.58E−06) of the TA variation in the background of MaMa (n = 49), and 16.1% (p = 0.0022) in the background of Mama (n = 98), indicating that Ma6 and Ma4 had a strong additive effect on fruit acidity. The direct evidence in support of the large effect of Ma6 and Ma4 in the background of MaMa could be drawn from the following observations: a double homozygous recessive genotype ma6ma6ma4ma4 was exclusively noted in the three progenies that had top three TA readings (15.0–18.7 mg ml−1) (Fig. 6a), whereas a double homozygous dominant genotype Ma6Ma6Ma4Ma4 was associated with the four progenies that were within a TA range (4.6–9.2 mg ml−1) exclusively below 10 mg ml−1 (Fig. 6a). Notably, the expected frequency for these two homozygous genotypes in the background of MaMa was 0.0156 (0.25 × 0.25 × 0.25), equivalent to 2.98 out of the 191 progenies, close to what was observed for genotypes MaMama6ma6ma4ma4 (3) and MaMaMa6Ma6Ma4Ma4 (4). These results provided important genetic insight into how the interactions between Ma6 and Ma4 would determine fruit acidity and why ultrahigh and regular acidity progeny were co-present in the background of Ma-.

Fig. 6
figure6

ANOVA analysis of the genetic effect of the interactions between HRM-Ma6 and HRM-Ma4 alleles on fruit acidity in the background of MaMa (a) and Mama (b). See also the legends in Figs. 1b and 5

Genes annotated in the Ma6 and Ma4 regions

In the 1 (8th to 9th)-Mb peak region of Ma6 on chromosome 4 (Fig. 3b) and the 1 (1st to 2nd)-Mb peak region of Ma4 on chromosome 6 (Fig. 3c), 61 and 73 genes (Tables S5 and S6) were annotated in the apple reference genome26, respectively. As a preliminary step toward the identification of candidate genes, the already annotated genes were also annotated in MAPMAN31 terms to better understand their potential functions. The 61 genes under the peak region of Ma6 included 56 protein-encoding genes (the other five encode rRNAs and tRNAs), of which 48 were annotated with a putative function while the remainder eight were not. Below are 16 of the 48 genes deemed of various regulatory roles in important biological process, including (1) four transcription factors (MD04G1061200-homeobox protein 2, MD04G1063600-a C2H2-like zinc finger protein, MD04G1064500-a myb domain protein 3r-4, and MD04G1066800-a GRAS family transcription factor); (2) two transcription regulators (MD04G1061900-a CCHC-type zinc finger and MD04G1062700-a methyltransferase MT-A70 family protein); (3) two signal transducers (MD04G1065600-a IQ-domain 22, and MD04G1066000-a PAS domain-containing protein tyrosine kinase family protein); (4) four protein posttranslational modifiers (MD06G1014800, MD06G1009600, and MD06G1009700, which all encode a protein kinase superfamily protein, and MD06G1013000 for a RING/U-box superfamily protein); and (5) four protein degradation proteins (MD04G1064900-a C3HC4 type-ring finger family protein, MD04G1060900-a ubiquitin-protein ligases, MD04G1065500-a RHOMBOID-like 2, and MD04G1063000-a matrixin family protein).

In the Ma4 peak region, the 73 annotated genes included one (MD06G1011800) encoding a miRNA (miR168), 56 encoding a protein of putative function, and 16 others without an annotation (13) or encoding an rRNA (3). Among the 56 genes of a putative function, ten genes were related to transcription regulation, including four encoding a MADS-box transcription factor (MD06G1013100, MD06G1013200, MD06G1013500, and MD06G1013600), two encoding a plant regulator RWP-RK family protein (MD06G1009200 and MD06G1009300), and MD06G1010000-a DNA-binding bromodomain-containing protein, MD06G1012500-a transcription factor Jumanji domain-containing protein, MD06G1014300-a Mob1/phocein family protein, and MD06G1014700-a growth-regulating factor 5. The other genes of interest included three encoding a protein kinase superfamily protein of roles in posttranslational protein modification (MD06G1014800, MD06G1009600, and MD06G1009700), MD06G1012300 encoding a signaling protein (RHO guanyl-nucleotide exchange factor 14), and MD06G1013000 encoding a RING/U-box superfamily protein involved in protein degradation.

Investigation of alternative hypothesis in genetic control of high acidity

Two hypotheses not considering high-acidity recessive to regular acidity were tested to see how the high-acidity phenotype would be mapped by AFFDD mapping. The first hypothesis was that the high-acidity phenotype was dominant over regular acidity, but the segregation was distorted to 3:1 as observed (Figs. 1b and 2a). Under this scenario, the strategy for mapping a dominant phenotype28 was directly employed by identifying the most commonly used type of informative SNVs (type I), which were characterized by being specific to the high-acidity pool and by an expected SNV allele frequency of 50% or a range of 40–60%. Consequently, 8277 such SNVs were identified and were plotted against the apple reference genome (Fig. S6A). It showed that the most significant SNV density peak was reported between the 1st and 2nd Mb on chromosome 17 although a significant peak was also detected at the 3rd to 4th Mb on chromosome 4 (same as the second peak in the Ma6 region) and at the 1st to 2nd Mb on chromosome 6 (same location as Ma4) (Fig. S6B-D). However, the putative mapping by the highest peak on chromosome 17 could not be validated in MaMa (p = 0.1450, n = 46) and in Mama (p = 0.1084, n = 85) based on ANOVA analysis of an SSR marker C1902 (Table S1, Fig. S6E, F) that was polymorphic for both Gala and PI 61398827, and was located at the 1.5th Mb within the peak region on chromosome 1726. These results demonstrated that the hypothesis led to a false positive mapping of a locus with the most significant p-value in the test, although the Ma6 and Ma4 regions were also detected.

The second hypothesis was that the high-acidity phenotype required two complementary genes and the progeny was derived from a cross of aabb (Gala) × AaBb (PI 613988) or Aabb (Gala) × aaBb (PI 613988). In either case, the progeny would segregate into 3:1 between regular acidity (1 Aabb, 1aaBb, and 1 aabb) and high acidity (1 AaBb), the same as what was observed (Figs. 1b and 2a). Further, the expected frequency of SNVs linked to A or B would be 50% in the high-acidity pool and 16.7% (1/6) in the regular acidity pool. Based on such expected SNV frequencies, we selected SNVs that had allele frequencies ranging from 40 to 60% in the high acid pool and from 6.7 to 26.7% in the regular acid pool, and that they differed by at least 23.3 percentage points, ten-percentage points below the expected 33.3 (50–16.7%) to accommodate variation. Applying these filters identified 6595 SNVs informative for AFDDD mapping, detecting a single significant peak between the 4th and 5th Mb on chromosome 6 (Fig. S7A, B), close to the peak of Ma4 (between the 1st and 2nd Mb). However, confirmation of the mapping of this peak region with SSR marker C14087 (Table S1, Fig. S7C, D), which segregated for the PI 613988 alleles27 and located at the 4.9th Mb on chromosome 626, showed that the marker effect was non-significant in MaMa (p = 0.0804, n = 44) while significant in Mama (p = 0.0466, n = 83), much lower than the effect of Ma4 (Fig. 5c, d). Moreover, the Ma6 region was never detected under this hypothesis.

Discussion

Population characteristics in GMAL 4595 and the AFDDD mapping approach

This study reported the identification of two recessive gene loci Ma6 and Ma4 that largely determine high acidity in apple in the background of MaMa as well as Mama. The success could be attributed to the choice of the mapping population GMAL 4595 and the adaptation to the AFDDD mapping approach. Population GMAL 4595 was previously used for fine genetic mapping of Ma, leading to the identification of the critical premature stop codon mutation in Ma1 that was causal for low acidity4,14,17. One of the important characteristics of population GMAL 4595 was the non-detectable effect of Ma3 on chromosome 8, making it highly suitable for detection of other genes controlling high acidity in the background of Ma-. The non-detectable effect of Ma3 was likely caused by a homozygous low-acidity genotype ma3ma3 in the seed parent Gala and a homozygous high-acidity genotype Ma3Ma3 in the pollen parent PI 613988 as they would produce a non-segregation genotype Ma3ma3 in the mapping population GMAL 4595. The three markers/genes studied in the Ma3 region were MdPP2CH22, CH02g0927, and MdSAUR3722, which were located at the 8.7th, 10.0th, and 11.6th Mb on chromosome 8 in the apple reference genome26, respectively. Based on the published allelic profile of marker MdPP2CH22, the genotypes of Gala, PI 613988, and their F1 progeny were inferred to be ma3ma3 (AA), Ma3Ma3 (GG), and Ma3ma3 (AG), respectively (Fig. S1C), directly in support of the notion that the non-detectable effect of Ma3 was caused by an identical genotype (Ma3ma3) in population GMAL 4595. In addition, no significant effect on TA was detected for the MdSAUR37 and CH02g09 alleles segregated in both parents and the population (Figs. S2 and S3), which indirectly supported that the genes underlying Ma3 did not segregate in GMAL 4595. Notably, both genes MdPP2CH and MdSAUR37 were implicated to be associated with malic acid accumulation in apple fruit22. The non-detectable effect by MdSAUR37 contradicted to its supposed role in fruit acidity in their study. This was because the inability to separate the low-acidity genotype ma3ma3, a signature genotype–phenotype association for Ma3, from the compounded genotype group of ma3ma3 & ma3-null in MdSAUR37 (Fig. S2, Table S3) was largely resolved in CH02g09 and still CH02g09 showed a non-significant effect on acidity (Fig. S3, Table S3). Another useful character of GMAL 4595 was that the two parents were heterozygous at both loci Ma6 and Ma4. Such type of segregation maximized the frequency of ma6ma6 and ma4ma4 and their effect on high acidity, which helped explain the wide acidity ranges in the background of Ma-.

The AFDDD mapping approach was initially developed for mapping a dominant trait (weeping) in apple based on pooled genome-sequencing analysis28. A major advantage of AFDDD mapping approach over the conventional QTL mapping approach in apple is its capacity for mapping without the need of construction of a genome linkage map and its effectiveness in identification of informative SNVs in the causal gene regions from a large pool of DNA variants that are usually under the control various segregation types due to its highly heterozygous genome28. In this study, the AFDDD mapping approach was adapted successfully in mapping high-acidity QTLs Ma6 and Ma4 in the background of Ma-, demonstrating its utility in detecting QTLs for recessive phenotype in apple, a self-incompatible heterozygous woody species, even when a major gene was involved. In self-compatible crops, the power of the pooled genome-sequencing-based approaches has been documented not only in QTL or mutation mapping, such as rice32, wheat33, and ryegrass34, but also in direct identification of causal genes or mutations, such as tomato35, lettuce36, peach37,38, and others.

Effect of Ma6 and Ma4 on fruit acidity and marker-assisted selection (MAS)

The presence of low-acidity seedlings in apple breeding populations could be explained well with the major QTLs Ma4 and Ma322. However, the genetic factors associated with the segregation of (ultra) high-acidity seedlings have been largely unknown. The identification of Ma6 and Ma4 by this study shed light on the genetic control of high acidity in the background of MaMa and Mama in apple. The estimated genetic effect of Ma6 was 20.6% in MaMa and 8.3% in Mama, slightly lower than those of Ma4, which were 28.5% and 9.4%, respectively. Notably, the effect of Ma6 and Ma4 was much lower in the background of Mama than in that of MaMa. A most likely factor contributing to the observation is the documented highly unequal effect (on TA) of the Ma alleles between Gala (42.3%) and PI 613988 (17.0%)4. The other possible factors may include genetic recombinations between the markers used and the causal genes underlying Ma6 or Ma4, and random experimental errors in TA data collections, which was conducted in 1 year. Ma6 was a novel QTL relevant for fruit acidity as fruit acidity QTLs have not been reported on chromosome 4. However, Ma4 likely represented the minor QTL previously detected on chromosome 6 near marker C123604 as the marker is physically located at the 0.576th Mb, close to the peak of Ma4 (1–2 Mb) on chromosome 6 in the reference genome26. The detection of a larger effect of Ma4 in this study was probably benefited from the fact that HRM-Ma4 was an informative marker for both parents while C12360 was informative for Royal Gala only4,27. Another difference between the two QTLs was that Ma6 showed a non-detectable effect in mama (Fig. S4A) while Ma4 was also a significant factor (Fig. S4C). One possible explanation would be that the function of Ma6 might require the presence of Ma, while that of Ma4 could be independent of Ma.

The combined genetic effect of Ma6 and Ma4 was high (50.7%) in the background of MaMa while moderate (16.1%) in Mama (Fig. 6), suggesting that the associated markers HRM-Ma6/SSR-Ma6 and HRM-Ma4/SSR-Ma4 would be useful for removal of high-acidity seedlings that have a MaMa genotype. Based on the TA readings in the background of MaMa, all the three seedling trees of genotype ma6ma6ma4ma4 were shown with ultrahigh acidity (15.0–18.7 mg ml−1) (Fig. 6a), indicating that such double homozygous high acid progenies could be safely discarded without risk. Remarkably, the observed frequency for the ultrahigh acidity genotype MaMama6ma6ma4ma4 was 0.0157 (3/191), nearly equal to what was expected, i.e., 0.0156 (0.25 × 0.25 × 0.25). It is probably also a low-risk practice to rid of seedlings of genotypes (ma6ma6Ma4ma4; 10.1–13.8 mg ml−1, n = 3) and Ma6ma6ma4ma4 (10.6–14.3 mg ml−1 in four of the five progenies while the remainder was of TA 5.9 mg ml−1), which are homozygous high-acidity genotype at one locus while heterozygous at the other. In theory, these three genotype groups may account for 7.8% (5/64) of the entire F1 population derived from a typical cross involving two heterozygous parents, i.e., MamaMa6ma6Ma4ma4 × MamaMa6ma6Ma4ma4. Therefore, combining markers CAPS145514, HRM-Ma6/SSR-Ma6, and HRM-Ma4/SSR-Ma4 could increase breading efficiency by 32.8% (21/64), of which 25.0% was contributed by the low acid (mama) seedlings and 7.8% by the high acid seedlings. These markers could also be used in planning of new crosses to minimize the frequency of the three high-acidity genotypes (ma6ma6ma4ma4, ma6ma6Ma4ma4, and Ma6ma6ma4ma4). One possible approach would be to choose one parent of homozygous genotype Ma6Ma6 and the other parent of Ma4Ma4. However, more studies are needed to test if and how the Ma6 and Ma4 associated markers could be used in diverse apple breeding populations in which Ma3 is or is not a factor. After all, the identification of Ma6 and Ma4 by this study was accomplished using a single interspecific cross (GMAL 4595) in which all progenies had the same genotype Ma3ma3.

Alternative genetic models considered for mapping high acidity

The mapping of QTLs Ma6 and Ma4 was accomplished under the hypothesis that the high-acidity phenotype was recessive to regular acidity, which was a straightforward interpretation of the observed 3:1 ratio (p = 0.3173) between the regular acidity progeny (105) and the high-acidity progeny (42) in genotypes MaMa and Mama (Figs. 1b and 2a). However, such segregation could also be possible under the two hypotheses: (1) the high-acidity phenotype was dominant over regular acidity, but the normal segregation was distorted; (2) the cross was aabb (Gala) × AaBb (PI 613988) or Aabb (Gala) × aaBb (PI 613988) and the complementary interactions between genes (alleles) A and B were required for high acidity. AFDDD mapping analyses under the two hypotheses demonstrated that the results were less desirable, if not unacceptable, due to either a false positive mapping of the supposedly most significant peak on chromosome 17 under the first hypothesis (Fig. S6), or the complete miss of Ma6 under the second hypothesis (Fig. S7), indirectly endorsing the high-acidity recessive hypothesis, under which the Ma6 and Ma4 QTLs were identified.

Candidate genes under Ma6 and Ma4

There were 61 genes annotated under the 1-Mb peak region of Ma6 and 48 of them were indicated with a putative function (Table S5). Similarly, in the 1-Mb peak region of Ma4, 73 genes annotated, of which a miR168 encoding gene and 56 protein-encoding genes were given a putative function (Table S6). Although it would be difficult, if not impossible, to confidently suggest any candidate genes due to the large number of genes involved, the listed genes provided an important starting point to search for the candidate causal genes underpinning QTLs Ma6 and Ma4 that were associated high acidity in apple. Certainly, a much more dedicated study is needed to accomplish this goal in the future.

In conclusion, using pooled genome-sequencing-based AFDDD mapping approach, we identified two QTLs associated with recessive high-acidity phenotype in the background of MaMa and Mama in apple, which were localized on chromosomes 4 (Ma6) and 6 (Ma4), respectively. Their recessive genotypes ma6ma6 and ma4ma4 confer high acidity, which is in contrast with the recessive genotypes mama and ma3ma3 that control low acidity. These findings not only provide important insight into genetic control of high acidity in apple, but also help enhance MAS by parental line selection and by removal of (ultra) high-acidity seedlings while low-acidity seedlings can be discarded according to mama. The genes annotated under Ma6 and Ma4 offer a list of candidates that might be genetically causal for high acidity.

Additional notes

The two QTLs on chromosomes 4 and 6 had been named Ma4 and Ma5, respectively, until the production proofreading stage of this manuscript. However, the same names (Ma4 and Ma5) were also used in a relevant article (ref. 39) that was just published online on 8 September 2020. To avoid confusions, the original name Ma4 on chromosome 4 was simply changed to Ma6 and that on chromosome 6 to Ma4, and so Ma6 appears before Ma4 throughout this manuscript.

References

  1. 1.

    Harker, F. R., Kupferman, E. M., Marin, A. B., Gunson, F. A. & Triggs, C. M. Eating quality standards for apples based on consumer preferences. Postharvest Biol. Technol. 50, 70–78 (2008).

    Google Scholar 

  2. 2.

    Bai, Y., Dougherty, L., Cheng, L., Zhong, G.-Y. & Xu, K. Uncovering co-expression gene network modules regulating fruit acidity in diverse apples. BMC Genomics 16, 612 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Visser, T. & Verhaegh, J. J. Inheritance and selection of some fruit characters of apple. 1. Inheritance of low and high acidity. Euphytica 27, 753–760 (1978).

    Google Scholar 

  4. 4.

    Xu, K., Wang, A. & Brown, S. Genetic characterization of the Ma locus with pH and titratable acidity in apple. Mol. Breed. 30, 899–912 (2012).

    CAS  Google Scholar 

  5. 5.

    Guerra, M., Sanz, M. A. & Casquero, P. A. Influence of storage conditions on the sensory quality of a high acid apple. Int. J. Food Sci. Technol. 45, 2352–2357 (2010).

    CAS  Google Scholar 

  6. 6.

    Harker, F. R. et al. Sensory interpretation of instrumental measurements 2: sweet and acid taste of apple fruit. Postharvest Biol. Technol. 24, 241–250 (2002).

    Google Scholar 

  7. 7.

    Oraguzie, N. et al. Postharvest assessment of fruit quality parameters in apple using both instruments and an expert panel. Postharvest Biol. Technol. 52, 279–287 (2009).

    Google Scholar 

  8. 8.

    Brown, A. G. & Harvey, D. M. Nature and inheritance of sweetness and acidity in cultivated apple. Euphytica 20, 68–80 (1971).

    CAS  Google Scholar 

  9. 9.

    Nybom, N. On the inheritance of acidity in cultivated apples. Hereditas 45, 332–350 (1959).

    Google Scholar 

  10. 10.

    Maliepaard, C. et al. Aligning male and female linkage maps of apple (Malus pumila Mill.) using multi-allelic markers. Theor. Appl. Genet. 97, 60–73 (1998).

    CAS  Google Scholar 

  11. 11.

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

    Google Scholar 

  12. 12.

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

    CAS  PubMed  Google Scholar 

  13. 13.

    Ma, B. et al. Construction of a high density linkage map and its application in the identification of QTLs for soluble sugar and organic acid components in apple. Tree Genet. Genomes 12, 1–10 (2016).

    Google Scholar 

  14. 14.

    Bai, Y. et al. A natural mutation-led truncation in one of the two aluminum-activated malate transporter-like genes at the Ma locus is associated with low fruit acidity in apple. Mol. Genet. Genomics 287, 663–678 (2012).

    CAS  PubMed  Google Scholar 

  15. 15.

    Khan, S. et al. Differences in acidity of apples are probably mainly caused by a malic acid transporter gene on LG16. Tree Genet. Genomes 9, 475–487 (2013).

    Google Scholar 

  16. 16.

    Kovermann, P. et al. The Arabidopsis vacuolar malate channel is a member of the ALMT family. Plant J. 52, 1169–1180 (2007).

    CAS  PubMed  Google Scholar 

  17. 17.

    Li, C. et al. Apple ALMT9 requires a conserved C-terminal domain for malate transport underlying fruit acidity. Plant Physiol. 182, 992–1006 (2020).

    CAS  PubMed  Google Scholar 

  18. 18.

    Kumar, S. et al. Genomic selection for fruit quality traits in apple (Malus x domestica Borkh.). PLoS ONE 7, e36674 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Sun, R. et al. A dense SNP genetic map constructed using restriction site-associated DNA sequencing enables detection of QTLs controlling apple fruit quality. BMC Genomics 16, 747 (2015).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Verma, S. et al. Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study. Tree Genet. Genomes 15, 18 (2019).

    Google Scholar 

  21. 21.

    Zhang, Q. et al. Identification, characterization, and utilization of genome-wide simple sequence repeats to identify a QTL for acidity in apple. BMC Genomics 13, 537 (2012).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Jia, D. et al. Apple fruit acidity is genetically diversified by natural variations in three hierarchical epistatic genes MdSAUR37, MdPP2CH and MdALMTII. Plant J. 95, 427–443 (2018).

    CAS  PubMed  Google Scholar 

  23. 23.

    Duan, N. et al. Genome re-sequencing reveals the history of apple and supports a two-stage model for fruit enlargement. Nat. Commun. 8, 249 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Velasco, R. et al. The genome of the domesticated apple (Malus x domestica Borkh.). Nat. Genet. 42, 833–839 (2010).

    CAS  PubMed  Google Scholar 

  25. 25.

    Blanpied, G. D. & Silsby, K. J. In Information Bulletin 221 (Cornell Cooperative Extension, Cornell University, Ithaca, 1992).

  26. 26.

    Daccord, N. et al. High-quality de novo assembly of the apple genome and methylome dynamics of early fruit development. Nat. Genet. 49, 1099–1106 (2017).

    CAS  PubMed  Google Scholar 

  27. 27.

    Wang, A. et al. EST contig-based SSR linkage maps for Malus × domestica cv Royal Gala and an apple scab resistant accession of M. sieversii, the progenitor species of domestic apple. Mol. Breed. 29, 379–397 (2012).

    CAS  Google Scholar 

  28. 28.

    Dougherty, L., Singh, R., Brown, S., Dardick, C., Xu, K. & Exploring, D. N. A. variant segregation types in pooled genome sequencing enables effective mapping of weeping trait in Malus. J. Exp. Bot. 69, 1499–1516 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40, e115–e115 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    R-Core-Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org (2012).

  31. 31.

    Thimm, O. et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37, 914–939 (2004).

    CAS  PubMed  Google Scholar 

  32. 32.

    Abe, A. et al. Genome sequencing reveals agronomically important loci in rice using MutMap. Nat. Biotechnol. 30, 174–178 (2012).

    CAS  PubMed  Google Scholar 

  33. 33.

    Trick, M. et al. Combining SNP discovery from next-generation sequencing data with bulked segregant analysis (BSA) to fine-map genes in polyploid wheat. BMC Plant Biol. 12, 14 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Knorst, V. et al. Pooled DNA sequencing to identify SNPs associated with a major QTL for bacterial wilt resistance in Italian ryegrass (Lolium multiflorum Lam.). Theor. Appl. Genet. 132, 947–958 (2019).

    CAS  PubMed  Google Scholar 

  35. 35.

    Petit, J. et al. The glycerol-3-phosphate acyltransferase GPAT6 from tomato plays a central role in fruit cutin biosynthesis. Plant Physiol. 171, 894–913 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Huo, H. et al. Rapid identification of lettuce seed germination mutants by bulked segregant analysis and whole genome sequencing. Plant J. 88, 345–360 (2016).

    CAS  PubMed  Google Scholar 

  37. 37.

    Dardick, C. et al. PpeTAC1 promotes the horizontal growth of branches in peach trees and is a member of a functionally conserved gene family found in diverse plants species. Plant J. 75, 618–630 (2013).

    CAS  PubMed  Google Scholar 

  38. 38.

    Hollender, C. A. et al. Loss of a highly conserved sterile alpha motif domain gene (WEEP) results in pendulous branch growth in peach trees. Proc. Natl Acad. Sci. USA 115, E4690–E4699 (2018).

    CAS  PubMed  Google Scholar 

  39. 39.

    Rymenants, M. et al. Detection of QTL for apple fruit acidity and sweetness using sensorial evaluation in multiple pedigreed full-sib families. Tree Genet. Genomes 16, 71 (2020).

    Google Scholar 

Download references

Acknowledgements

This study was supported in part by the Agriculture and Food Research Initiative competitive grant no. 2014-67013-21660, National Institute of Food and Agriculture, US Department of Agriculture (NIFA, USDA).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Kenong Xu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary information

Rights and permissions

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ban, S., Xu, K. Identification of two QTLs associated with high fruit acidity in apple using pooled genome sequencing analysis. Hortic Res 7, 171 (2020). https://doi.org/10.1038/s41438-020-00393-y

Download citation

Search