A roadmap for research in octoploid strawberry


The cultivated strawberry (Fragaria × ananassa) is an allo-octoploid species, originating nearly 300 years ago from wild progenitors from the Americas. Since that time the strawberry has become the most widely cultivated fruit crop in the world, universally appealing due to its sensory qualities and health benefits. The recent publication of the first high-quality chromosome-scale octoploid strawberry genome (cv. Camarosa) is enabling rapid advances in genetics, stimulating scientific debate and provoking new research questions. In this forward-looking review we propose avenues of research toward new biological insights and applications to agriculture. Among these are the origins of the genome, characterization of genetic variants, and big data approaches to breeding. Key areas of research in molecular biology will include the control of flowering, fruit development, fruit quality, and plant–pathogen interactions. In order to realize this potential as a global community, investments in genome resources must be continually augmented.

Origin and organization of the octoploid strawberry genome

The earliest cultivars of allo-octoploid (2n = 8× = 56) garden strawberry (Fragaria × ananassa Duchesne ex Rozier) originated approximately 300 years ago from spontaneous hybrids between ecotypes of non-sympatric wild octoploid species: Fragaria chiloensis subsp. chiloensis from South America and Fragaria virginiana subsp. virginiana from North America1,2,3,4,5. Several additional wild octoploid subspecies have since been used as parents in breeding, creating an admixed population of F. × ananassa individuals with genomes that are mosaics of phylogenetically and demographically diverse progenitor genomes4,6,7,8,9,10,11,12,13.

The origin of octoploid strawberry has been intensely studied and widely debated10,14,15,16,17,18,19,20,21. While several subgenome origin hypotheses have emerged from cytogenetic, phylogenetic, and comparative genetic mapping studies6,7,9,15,22,23,24,25, a complete hypothesis for the origin and evolution of the octoploid genome was only recently proposed with the publication of the “Camarosa” reference genome10. Through phylogenetic analyses of the transcriptomes of all described extant diploid species, including four subspecies of Fragaria vesca, the putative subgenome donors found in the octoploid were identified as F. vesca subsp. bracteata, Fragaria iinumae, Fragaria viridis, and Fragaria nipponica10.

Edger et al.10 provided strong support for earlier hypotheses that F. vesca and F. iinumae were two of the four subgenome donors7,9,19,24,26. Until the octoploid reference genome was published, the origin of the other diploid subgenome donors had remained unclear, although multiple hypotheses had been proposed7,9,26. Liston et al.20 then reasoned that Edger et al.10 may have misidentified two of the progenitors due to bias from excluding in-paralogs in their phylogenetic analyses. To address this concern, Edger et al.21 developed a chromosome-scale assembly of the F. iinumae genome and reanalyzed the original data with in-paralogs. The revised analysis supported the original model that the genome of octoploid strawberry originated through successive stages of polyploidization involving four progenitor species: diploid × diploid (F. nipponica × F. innumae) → tetraploid × diploid (tetraploid ancestor × F. viridis) → hexaploid × diploid (hexaploid ancestor × F. vesca subsp. bracteata) → octoploid ancestor10.

In addition, the chromosome-scale genome assembly showed that the diploid subgenomes were not static building blocks (e.g., A–D) walled off from one another. Rather they have dynamically evolved through homoeologous exchanges, which are well-known in neopolyploids27,28,29. Homoeologous exchanges in octoploid strawberry were found to be highly biased toward the F. vesca subsp. bracteata subgenome replacing substantial portions of the other subgenomes10. However, homoeologous exchanges are not unidirectional. Although the chromosomes are architectural mosaics of the four diploid subgenome donors and their octoploid descendants, F. × ananassa is strongly allo-octoploid6,9,14,22,23. Because the F. × ananassa chromosomes are complex admixtures of genes with different phylogenetic histories via homoeologous exchanges10,11,30, Edger et al.10 developed a nomenclature that precludes oversimplified one-to-one assignments to a specific diploid progenitor.

The F. × ananassa genome has not only been reshaped by polyploidization events, especially homeologous exchanges, gene-conversion, and selection (e.g., subgenome dominance), but by repeated interspecific hybridization in breeding that has resulted in the introgression of alleles from phylogenetically and demographically diverse F. chiloensis and F. virginiana ecotypes2,4,6,7,8,10,11,19. At this point in time, the decades long debate among geneticists and evolutionary biologists about the origin of the F. × ananassa genome7,9,16,19,31 seems to have reached an initial zenith. Remaining disagreements might only be settled when chromosome-scale assemblies of the other hypothesized diploid progenitors (F. nipponica and F. viridis) are assembled and analyzed.

Aside from the question of subgenome origin, what other evolutionary questions might be worthy of exploration at this juncture? First, while the four extant relatives of the diploid progenitors have been putatively identified, the history and timing of the intermediate polyploids remain poorly understood. When and where were the tetraploid and hexaploid ancestors formed? Are any of the known wild polyploids endemic to Asia descendants from these intermediate polyploids? Which subgenome is dominant in these polyploids? Second, a single dominant subgenome was uncovered in Fragaria×ananassa that controls many important traits including fruit quality10. Just how deterministic is subgenome dominance? In other words, is it possible to resynthesize the octoploid with a different degree of subgenome dominance, or with a different subgenome becoming dominant? The answer to this question could have implications for genetic improvement of the cultivated species.

Whole-genome genotyping and genetic mapping

Genotyping advances in strawberry have naturally followed advances in humans, model organisms and row crops. The development of the Affymetrix Axiom® iStraw90 single-nucleotide polymophism (SNP) genotyping array was a significant advance that enabled the facile production and exchange of genotypic information across laboratories with high reliability, minor amounts of missing data, and negligible genotyping errors31,32,33. The ease-of-use, speed of analysis, simplicity of data management, and outstanding reproducibility of SNP genotyping arrays have been important factors in their continued adoption in strawberry and other plant species with complex genomes11,31,32,34,35.

Underlying computational challenges associated with genotyping by sequencing (GBS) and other next-generation sequencing (NGS) facilitated approaches have limited their widespread application in octoploid strawberry thus far36,37. The challenges are similar across species, but obviously exacerbated in allogamous polyploids: uneven and inadequate sequencing depth, copy number uncertainty, heterozygote miscalling, missing data, sequencing errors, etc., all of which challenge the integration of DNA variant information across studies38,39,40. As with the other DNA marker genotyping approaches reviewed here, the first GBS study in octoploid strawberry utilized the diploid F. vesca reference genome in combination with a phylogenetic approach (POLiMAPS) for aligning, classifying, and calling DNA variants9,36.

Recently, Hardigan et al.11 whole-genome shotgun (WGS) sequenced 88 F. × ananassa, 23 F. chiloensis, and 22 F. virginiana germplasm accessions. Strikingly, 80% of the short-read DNA sequences uniquely mapped to single subgenomes in the octoploid reference. Approximately, 90M putative DNA variants were identified among F. × ananassa, F. chiloensis, and F. virginiana individuals, whereas 45M putative DNA variants were identified among F. × ananassa individuals. An ultra-dense framework was then developed of genetically mapped DNA variants across the octoploid genome by WGS sequencing 182 full-sib individuals from a cross between F. × ananassa “Camarosa” and F. chiloensis subsp. lucida “Del Norte”. Large expanses of homozygosity within the commercial hybrid parent prevented complete end-to-end mapping of all 28 octoploid chromosomes in F. × ananassa as was accomplished with the wild parent, further demonstrating the value of dense NGS data for understanding sources of genotyping and mapping challenges in the octoploids. As these WGS-GBS and GBS mapping results demonstrate10,37, several NGS-based genotyping approaches41,42,43,44 should work well in combination with the octoploid reference genome10.

In summary, while the complexity of the octoploid genome has historically complicated DNA variant genotyping and genetic mapping in strawberry9,14,19,24,31,45,46,47, the chief technical challenges were addressed with: (a) the development of a high-quality octoploid genome assembly; (b) WGS resequencing of numerous octoploid individuals that shed light on the extent of intra- and inter-homoeologous nucleotide variation; (c) identification and physical mapping of DNA variants across the octoploid genome; and (d) comparative genetic mapping of the wild octoploid progenitors of F. × ananassa using SNPs anchored to the octoploid reference genome10,11.

DNA variants genotyped with different platforms and approaches predating the octoploid reference genome9,14,31,45,47 were independent and disconnected, resulting in the proliferation of linkage group nomenclatures, absence of a universal linkage group nomenclature, uncertainty in the completeness of genome coverage, and inability to cross-reference physical and genetic mapping information across studies, populations, and laboratories. The DNA marker sequences from many of the previously published mapping experiments were either not readily available or too short or nonspecific to enable unambiguous mapping to the octoploid reference genome10,11. The one exception was the genetically mapped double digest restriction-associated DNA sequence (ddRAD) markers described by Davik et al.47, which were used by Edger et al.10 for scaffolding the octoploid reference genome. Most F. vesca DNA probe sequences used to assay SNPs on the iStraw35 and iStraw90 SNP arrays were too short and nonspecific to unambiguously determine their physical marker locations in the octoploid genome11. Hence, genotypes produced with these SNP arrays could not always be effectively utilized for genome-wide association studies or other applications requiring subgenome resolution. Moreover, none of the previously published iStraw90 (or iStraw35) based genetic mapping studies have shared SNP marker genetic locations, complete genetic maps, or other critical enabling information needed to identify corresponding linkage groups across laboratories31,32.

These long-standing issues were resolved with the development of a new 850,000-SNP genotyping array populated exclusively with DNA variants and reference DNA sequences that unambiguously mapped to single-homoeologous chromosomes in the octoploid reference genome11. Using the 850,000 SNP array, a second array (“FanaSNP”) with 50,000 subgenome specific SNPs, including 5819 genetically mapped SNPs from the iStraw35 array was developed11 facilitating the integration of genetic and physical mapping information across studies. These new arrays provide telomere-to-telomere coverage and target common DNA variants within and among domesticated populations. Although the full set of iStraw SNP probe DNA sequences could not be unambiguously aligned to a single octoploid subgenome11, the true physical position for 97% of the retained iStraw probes were identified using linkage disequilibrium with the newly developed SNPs probes anchored to the octoploid reference genome11. Comparative mapping of SNPs in several wild and domesticated populations facilitated the integration of earlier linkage group nomenclatures and the development of a universal linkage group nomenclature substantiated by the observation of genome-wide synteny among diverse octoploid genetic backgrounds10,11,30.

These recent advances in genotyping and mapping are expected to have tremendous and immediate impacts on applied research in genetics and breeding of strawberry. But other research questions arise which have bearing on the utility of these new tools and resources, particularly with regard to diversity among genomes that is currently undescribed. For example, what large-scale structural variations exist in octoploid Fragaria germplasm? Recent advances in long read sequencing platforms (e.g., PacBio and MinION) resulted in significant decreases in costs and increases in read lengths and should soon permit inexpensive assessments of structural variants across the cultivated strawberry pangenome. On a smaller scale, what percentage of genes in cultivated strawberry exhibit presence–absence variation? Recent pangenome studies in plants have revealed that a significant proportion of gene content exhibits presence–absence variation48,49,50. For example, nearly 20% of the genes in Brassica oleracea are found in only certain genotypes and are enriched with functions encoding major agronomic traits. This suggests that genes in strawberry will be missed when utilizing a single octoploid reference genome and genotyping resources based on that genome alone. To construct a useful pangenome, how many individuals need to be included to capture most variation in gene content? These questions will soon be addressed as additional octoploid genomes become available.

Genome-assisted breeding in strawberry

For many years genome-assisted breeding in strawberry lagged behind agronomic crops and even many specialty crops. However, surveys conducted by the RosBREED consortium and funded by the NIFA Specialty Crop Research Initiative have documented the rapid rise in the use of DNA information in strawberry breeding in the last decade. In 2010, only 43% of surveyed strawberry breeders had employed DNA markers or other genomics-based tools. By early 2019, data on 12 of the 14 active strawberry breeding programs in the U.S. indicated that all but one of these 12 programs (92%) had used DNA information for at least one of four purposes. The most common application was for verifying the identity or better understanding the lineage of plant materials used in the program. Two-thirds of the programs had used DNA markers or other genomics-based tools to choose parents and plan crosses, and seven of the 12 (58%) had used DNA information for seedling selection. Two-thirds of the programs were involved in upstream research of direct relevance to their programs, e.g., creating or validating DNA tests of particular applicability for their plant materials and breeding goals. Some of these were one-time or infrequent applications; however, seven of the 12 programs (59%) reported using at least one application of DNA information “on an ongoing, routine basis” (Michael T. Coe, personal communication).

Among the many breeding-relevant loci discovered in the cultivated strawberry genome, flowering, and fruit quality loci have been prominent, as would be expected in a high-value fruit commodity. These, include discovery of the locus controlling day-neutrality or PF51 and its subgenome localization52 as well as multiple loci controlling volatile compounds such as gamma decalactone, mesifurane, and methyl anthranilate53,54,55,56. For uncovering disease resistance loci, quantitative trait locus (QTL) mapping has been the most prominent approach. While traditional biparental populations have been effective for QTL discovery57, pedigree-based analysis in multiparental populations using FlexQTL has been increasingly applied58,59, as pedigree breeding and maintenance of clones across generations are common in strawberry. Pedigree-based analysis in complex family structures has allowed the simultaneous detection of multiple QTL alleles and the quantification of their phenotypic effects across diverse genetic backgrounds, as demonstrated for the FaRPc2 locus60.

The use of DNA tests in breeding has been greatly enhanced by RosBREED efforts in marker development and validation61. Assays for SNP detection such as kompetitive allele-specific polymerase chain reaction (KASP) and high-resolution melting have become the tests of choice for breeding applications due to an abundance of SNP information from array genotyping, accuracy and ease of scoring, and resilience to crude strawberry DNA extracts62,63. The Strawberry DNA Testing Handbook was recently developed to assist breeders in identifying published DNA tests and understanding how to apply them64. This community resource is available at the Genome Database for Rosaceae65 and will be continually updated as existing tests are improved and new tests are published (Table 1).

Table 1 Published strawberry DNA tests included in the Strawberry DNA Testing Handbook64, and the loci targeted

While locus-specific DNA tests are highly useful in parent and seedling selection for traits with simple genetics, genome-wide prediction has become the strategy of choice for improving genetically complex traits in crop species. The goal is predictive, and the utility of this strategy has been demonstrated in strawberry for parent selection for yield and quality traits where it was shown that: (a) markers are more effective than pedigrees for estimating breeding values, even when phenotypic information is present; (b) phenotyping effort can be reduced by using trials of advanced selections as training populations; and (c) individuals with high predicted performance can be used as parents one year early in the breeding cycle66 (Fig. 1).

Fig. 1: Genome-wide prediction (GWP) can reduce the strawberry breeding cycle from a minimum of 3 to 2 years.

In this example, a 2016 replicated trial of advanced selections (training population) was phenotyped and genotyped and a model generated to predict parental performance (genomic estimated breeding values) for seedlings from the same year (test population) for which phenotypic data has not yet been collected. Some of the untested seedlings with high predicted performance for predicted traits of interest were used in 2017 crosses, one year before they would be used as parents without genome-wide prediction. Combining GWP for complex traits with marker-assisted seedling selection (MASS) for traits controlled by one or few genes results in a comprehensive strategy for genetic improvement

The future of strawberry breeding research is rife with opportunities in the genomics era. In particular, candidate-gene approaches will be dramatically enhanced by the “Camarosa” reference and other octoploid genomic resources, given the ability to pinpoint sequence variations among subgenomes and thus distinguish among homoeologous alleles. Genes involved in fruit volatile compound biosynthesis are particularly attractive targets given the importance of aroma to flavor and sweetness perception67. Gene identification will, in turn, fuel the development of new DNA tests and enhance existing tests. Questions for the future include the following: What level of genetic gains can be achieved simply by developing markers in causal genes, eliminating problems of recombination between marker and gene? Will functional characterization of genes lead to gene edited strawberries in the commercial realm?

A tool that may help to uncover gene/trait associations in strawberry and help identify “missing heritability” is expression QTL (eQTL). In essence, eQTL are segregating genomic regions influencing differential gene expression. With RNAseq alone, it is often difficult to discern whether changes in transcript accumulation are due to genetics, environment or stochastic effects. A recent eQTL analysis identified a subset of strawberry fruit genes whose differential expression is determined by genotype, the extent of that genetic influence, and markers that can be used for selection of desired gene-expression ranges68. Thus, eQTL analyses may reveal marker/trait associations in cases where strawberry phenotypes are influenced by transcript abundance. In other cases, eQTL controlling transcripts of undetermined function can support candidate-gene discovery and trait-based gene cloning. In a recent example, simple cross-referencing of trait-QTL and eQTL markers identified a causal aroma biosynthesis gene in melon69.

For complex traits controlled by many loci, the area of genome-wide prediction presents a number of practically important research questions for the future. Will the newest SNP array, with its whole-genome coverage and wealth of subgenome-specific markers, help increase prediction accuracies? How large should training populations be to achieve maximum predictive power, and how many breeding cycles can be included? Given that the vast majority of octoploid strawberry cultivars are asexually propagated, can non-additive effects be modeled to enhance predictions of clonal performance? When will low-density genotyping be affordable enough to select for complex traits in seedling populations, as opposed to selection only among parents? These questions are important and yet are very practical and applied in nature. They have been answered in other crop species, and we expect that they will soon be answered in strawberry as well.

The genetics of flowering, fruit development, and fruit quality traits

Because strawberry is a highly perishable fruit commodity, flowering and fruit traits are highly important from both biological and commercial standpoints. Recent genetic insights into these traits highlight several research topics of future importance.

Flowering and runnering

Strawberries are perennial rosette plants that form a determinate inflorescence from the apical meristem of the crown. Their axillary meristems can differentiate into either branch crowns, that are able to bear additional inflorescences, or runners. Because of these alternative fates of axillary meristems, there is a strong trade-off between flowering and runnering70,71. Strawberries can be divided into two main groups according to their flowering habits. Seasonal flowering (SF) strawberries produce flower initials under day lengths below a critical limit (variable but often defined as <12 h), whereas perpetual flowering (PF) strawberries produce new inflorescences continuously once induced to flower.

In the diploid woodland strawberry F. vesca, the dominant progenitor of the octoploid cultivated strawberry10, two classical mutants affecting flowering and runnering are known. Recessive mutations in the SF Locus (SFL) and Runnering (R) locus cause PF and runnerless phenotypes, respectively72. The F. vesca homolog of TERMINAL FLOWER1 (FvTFL1) was found as a candidate gene for SFL independently by two groups73,74, and Koskela et al.73 demonstrated the function of FvTFL1 as a major floral repressor that causes the seasonal flowering habit. The R locus was also recently mapped, and a mutation in a gene encoding gibberellin (GA) biosynthetic enzyme GA20-oxidase (FvGA20ox4) was found. This gene is highly expressed in axillary buds, and the mutated enzyme is not able to convert GA12 to GA20, a precursor of bioactive GA175.

Studies in cultivated strawberry76,77 indicate at least partial conservation of the genetic pathway in woodland strawberry73,77,78,79,80. Based on available data in woodland strawberry, a genetic model can be proposed (Fig. 2). In SF genotypes, FvTFL1 integrates environmental signals to control flowering, and flower induction only occurs after the downregulation of this gene by cool temperatures below 13 °C or by short days at temperatures of 13–20 °C, whereas higher temperatures prevent flower induction by activating FvTFL179. Genes involved in the temperature regulation of FvTFL1 await elucidation, but the photoperiodic pathway is quite well understood. Under long days, the woodland strawberry homolog of CONSTANS (FvCO) activates FLOWERING LOCUS T1 (FvFT1) in leaves, which leads to the upregulation of SUPPRESSOR OF THE OVEREXPRESSION OF CONSTANS1 (FvSOC1) in the shoot apex78,80. In PF woodland strawberries that are lacking a functional FvTFL1, this FvCO-FvFT1-FvSOC1 pathway promotes flowering, whereas in SF genotypes upregulation of FvTFL1 by FvSOC1 reverses the outcome of the pathway78,80. Actual flower induction is poorly understood, but the role of FvFT3, APETALA1 (FvAP1), and FRUITFULL (FvFUL) genes that are activated in the shoot apex after the downregulation of FvTFL1 by short days or cool temperature should be further explored73,76,77,81.

Fig. 2: A proposed model of the regulation of flowering and runnering in strawberry.

Arrows indicate activation and bars indicate repression

Another important challenge is to understand the flowering process in the context of the yearly growth cycle and to identify allelic variation that can be used for breeding new cultivars better adapted to diverse climates. Open questions include, for example, how is flower initiation and differentiation regulated? How is floral development connected to dormancy? PF cultivars are commercially quite important, but the genetic control of the trait clearly differs from PF in woodland strawberry. A major locus controlling PF was identified, named perpetual flowering and runnering (PFRU) because the PF allele also reduced runnering51. Several additional studies in different crossing populations have confirmed PFRU and narrowed the chromosome region52,82,83,84,85. The causal gene is not known, but several candidate genes have been suggested84. Interestingly, a QTL controlling flowering time in woodland strawberry was mapped to the same region of chromosome four86. These data suggest the presence of either two important flowering genes in this region or different alleles of the same gene that control PF and flowering time. Identification of the PF gene or genes in cultivated strawberry is obviously a research question of high importance both scientifically and commercially.

Better understanding the trade-off between flowering and runnering is also an important area of inquiry, because it might assist plant breeders and growers in controlling the balance between vegetative and sexual reproduction. Several lines of evidence suggest that GA controls the fate of axillary meristems in strawberries. Guttridge and Thompson87 found that runnerless woodland strawberry mutants began to form runners after GA treatment, and similar reversion was observed in a recent mutant screen that led to the identification of suppressor of runnerless, a gene that encodes a DELLA growth repressor of the GA signaling pathway88. Inhibitors of GA biosynthesis, in contrast, enhance crown branching and yield in cultivated strawberry89,90. Furthermore, FvSOC1 was found to control runner formation and regulate the expression of several GA biosynthetic genes, including the recently identified FvGA20ox4, which likely encodes a rate limiting enzyme of the GA biosynthetic pathway in axillary buds75,78. These data suggest a model in which FvSOC1 activates FvGA20ox4 and possibly other GA biosynthetic genes in axillary buds, leading to high bioactive GA1 levels, degradation of SLR proteins, and runner formation (Fig. 2).

Fruit development

In contrast to most fruits91, the fleshy tissue of Fragaria is a modified stem tip called the receptacle. The true fruits of Fragaria are dried ovaries called achenes, each of which contains a single seed. The receptacle together with attached achenes are what is typically refered to as the “fruit”. Although connected by fibrovascular strands4, molecular analysis using microarrays92 and RNA-seq93,94 show that the achenes and receptacle exhibit asynchronous transcriptional programs that reflect differences in timing of maturation of the two tissues. Fruit set requires a sufficient percentage of fertilized achenes due to their production of auxin95. Early studies showed that strawberry is non-climacteric, not appearing to respond to exogenous ethylene. However, the role of ethylene in strawberry maturation is reassessed later in this section in light of more recent data.

Fig. 3: Fruit set and early fruit development set the stage for a symphony of color, flavor, and sweetness that accompanies ripening.

Aspects of hormone homeostasis, transport, and signaling resulting in fruit set and early fruit development in diploid or octoploid strawberry have been implied from multiple transcriptome studies. Hormone metabolism genes are indicated in red, hormone transporters in blue, and hormone signaling components are indicated in purple. However, many question remain to be resolved (dashed lines) regarding pathway components for the two major hormones, auxin and gibberellin. These components need to be known before we can ask questions about how the genes involved in the process are regulated. Over 50 years have passed since auxin was identified as being required for receptacle enlargement, yet a fundamental question remains: what is it about fertilization that turns on the production of auxin? What form(s) of the hormones are transported from the ovule to stimulate growth of the receptacle, and what are the transporters involved? Which of the auxin and gibberellin responsive genes are critical to fruit enlargement?

The control of fruit set and development by plant hormones that interact and synchronize signals between the developing seed and surrounding tissues is graphically described in McAtee et al.96, and a comprehensive discussion of hormonal regulation of fruit ripening in non-climacteric as compared to climacteric fruit can be found in Cherian et al.97. Although a complete picture of how hormonal regulation and crosstalk underlie the molecular mechanisms of development and ripening has not yet emerged in either diploid or octoploid strawberry, considerable progress has been achieved due to gene-expression analyses using microarrays92,98,99, RNA-seq93,94,100,101, agroinfiltration for transient gene silencing, stable transformation with reporter genes or altered expression102, and virus-induced gene silencing (VIGS)103. The high-quality diploid and octoploid strawberry genomes and associated resources now available will greatly accelerate discovery. There is still much to learn from the diploid model, and fruit development in the octoploid is likely to involve a more complex interplay of homoeologous genes104. In addition, due to potential for interactions among products of homoeologs in the octoploid, careful holistic analysis of octoploid fruit development in achenes and during development remains to be accomplished by combining highly sensitive and accurate transcriptomic, proteomic, and metabolomic methods.

That auxin (IAA) and GAs are primary hormonal players during early development of both the receptacle and achenes is well supported by hormonal analyses of F. ×ananassa105 and transcriptome data from F. vesca94. F. vesca transcriptome data from fertilization to the large green fruit stage (when the embryo is fully developed), were analyzed for evidence of biosynthesis and activity of most of the major hormones, as well as for IAA transport94. Genes in the IAA biosynthesis pathway are actively expressed by the endosperm, and perhaps integuments, of the newly fertilized ovary, closely followed by expression of GA biosynthesis genes94. This transcriptome foundation now needs to be expanded with detailed hormone metabolic and transport studies, perhaps more easily accomplished by feeding studies with the correct reference compounds and mass spectrometric analyses using larger octoploid fruit. As illustrated in Fig. 3, there are many steps in the process of fruit set and early development that still require direct study.

We know that IAA levels in intact F. ×ananassa fruit rise rapidly following fertilization and peak at the small green fruit stage, thereafter decreasing to low levels at the white stage and to very low, but homeostatically regulated, levels in red ripe fruit105. Interestingly, separate analysis of white fruits and achenes showed that almost all the IAA measured in the intact fruit is in the achenes, with barely detectable levels in the receptacle. It has been suggested that low receptacle IAA levels are required for ABA biosynthesis to start and ripening to commence. GA1, likely the only bioactive GA in fruit105, also increased after fertilization, reaching a peak in the large green stage. Like IAA, GA1 levels are low during ripening stages. Analysis of spatial expression in F. vesca showed that genes encoding AUX/IAA transcriptional coregulators and auxin response factors (ARFs), are highly expressed in the expanding receptacle94, as expected if IAA is being released from the achenes to stimulate receptacle growth prior to ripening. How, and in what form, auxin travels from the fertilized achenes to the receptacle is still unknown, as are the signals that turn on auxin biosynthesis in precise tissues in response to fertilization.

Auxin transport might be most easily addressed in the larger, octoploid organs. As indicated by the diploid transcriptome studies, such analyses should include measurements of conjugated IAA biosynthesis and possible movement using mass labeled compounds. GA receptor protein genes (FvGID1a and FvGID1b) are also highly expressed in expanding F. vesca fruit, as are DELLA repressor genes94. Transcripts from genes encoding homologs of GA transporter proteins (PTR) were found in achenes postfertilization94, however, direct evidence of GA transport and responses is lacking.

Other hormones likely play roles in early fruit development, and their contributions require further study in octoploid strawberry. Cytokinin signaling genes appear to be active in young seed tissue of F. vesca94. Castasterone, a bioactive brassinosteroid (BR), was detected in young developing octoploid fruit105 in concordance with transcriptome data indicating biosynthesis and signaling in the receptacle of the diploid94. The transferase that methylates Jasmonic Acid (JA) to form the volatile compound Methyl Jasmonate was recently characterized from F. vesca and F. ×ananassa106 but may not be important for ripening107. Clearly, there is a need for further investigations into the possible roles of these compounds in the developing achenes and receptacle in order to understand fruit set and growth.

Unlike what is found in climacteric fruits, experiments with F. ×ananassa have shown that ABA is clearly a promoter of ripening in strawberry108. VIGS of FaNCED1, encoding a key enzyme in ABA biosynthesis, produced white fruits, and this phenotype was rescued by exogenous ABA109. RNAi mediated downregulation of an ABA receptor, FaCHLH/ABAR in fruits, resulted in upregulation of the negative signaling regulator ABI1 and downregulation of positive ABA signaling regulators (ABI3, ABI4, ABI5, and SnRK2)110. Sucrose appears to act as a signal upstream of the ABA signaling pathway in regulating strawberry fruit ripening110,111.

Recently, auxin was unambiguously detected in the ripe receptacles of “Camarosa”112 at about the same low levels reported earlier in “Red Gauntlet”105. These levels are about tenfold lower than what is commonly found in leaves. However, the increase in expression of FaTAR2 encoding the auxin biosynthesis enzyme tryptophan amino transferase, and genes encoding proteins involved in auxin perception (FaAux/IAA11, FaAux/IAA14b, and FaAux/IAA33) together with expression of genes involved in auxin signaling (FaARF6a and FaARF16c) in ripening receptacles strongly suggests cell-autonomous auxin synthesis and cell-specific response in the receptacle at ripening112. Laser capture microdissection and newer methodologies in mass spectral analysis of very small amounts of tissues113, for example, are needed to specify which types of cells (cortex, pith, and vasculature) are engaged in hormone metabolism in the ripening receptacle.

The critical importance of studying development and ripening in achene and receptacle separately is discussed in Merchante et al.114, who found that in F. ×ananassa, expression of ethylene biosynthesis gene families (ACC SYNTHASE and ACC-OXIDASE) was temporally and organ specific, and this applied as well to which members of a given gene family were expressed. Their results support many earlier reports implicating ethylene as playing a role in strawberry ripening. The most recent support for a role for ethylene in strawberry ripening comes from global analysis of transcriptomic changes in the achene and receptacle during ripening100. Analysis of the FaERF gene family identified three members, FaERF3, FaERF6, and FaERF71a, that are significantly expressed in the receptacle and upregulated upon ripening. Downregulating FaSAMS1 or FaCTR1 using the VIGS technique in the receptacle inhibited fruit red color formation. Ethephon application promoted natural red color development in white (VIGS) fruits and partially rescued FaSAM1-RNAi and FaCTR1-RNAi fruit. The results implicate FaCTR1 as a positive regulator and ethylene as a required signaling molecule in strawberry fruit ripening. Therefore, ethylene appears to be required for the normal development of the strawberry fruit, where it acts differently in the achenes and the receptacle. In achenes, it acts at the green and red stages, while in the receptacle it acts at the green/white stages. In these organs, ethylene selectively appears to influence the expression of genes involved in ethylene reception, phenylpropanoid metabolism, cell wall degradation, and strawberry aroma production.

The expression pattern of the gene encoding the BR receptor, FaBRI1, in F. ×ananassa receptacle suggests that BR may also play a role in ripening, and VIGS of this gene results in failure to redden115. However, a direct role for this class of hormone in strawberry ripening is in question, as neither active BR castasterone nor brassinolide were detected by the end of the white stage using unambiguous and sensitive analytical techniques105.

Epigenomic aspects of strawberry fruit ripening also deserve future consideration. Transcriptional regulation of fruit ripening in tomato is well characterized116, and although strawberry is non-climacteric it is likely that there are conserved pathways and regulatory mechanisms in common with the ripening achene or ripening receptacle. In tomato fruit, chromatin remodeling activities as well as changes in DNA methylation influence normal ripening in maturing fruit tissues116. Such studies in strawberry lag well behind. Recently, 71 genes encoding enzymes responsible for histone lysine methylation modifications were identified and characterized in the F. vesca genome117. qPCR showed that, in the receptacle, expression of some of the SET methyltransferase genes peak at turning stage; an indication of a role for chromatin remodeling in strawberry fruit ripening. In addition, nine DNA methyltransferase genes and four demethylase genes were identified in the F. vesca genome118. These reports indicate that DNA methylation changes dramatically at the onset of ripening, warranting a detailed investigation of the roles of epigenomics in development and ripening in octoploid fruit.

Recent comparative transcriptome analysis of developing fruit of two wild selections of F. pentaphylla that differ in ripe fruit color (white vs. red) demonstrated a key role of long noncoding RNAs (lnRNAs) in fruit development and fruit color formation119. Future studies must elucidate the functions of the genes targeted by these differentially expressed lnRNAs and their roles in the cultivated strawberry. Superimposing transcriptomics, proteomics, and metabolomics in the same tissues will allow for a more precise determination of how fruit set, development, and ripening are regulated, pointing to the most productive areas for genetic manipulation to improve fruit growth and quality.

Fruit quality

External quality characteristics of ripe strawberry fruit including size, color, and absence of surface defects have always been a focus of research and genetic improvement. In the last decade, flavor has gained increased importance as a quality attribute demanded by consumers. Strawberry flavor is imparted by sugars (primarily glucose, fructose, and sucrose), acids (citric and malic acids), and an unknown number of over 360 reported volatiles. A comprehensive study, using psychophysics to determine attributes that influence pleasure and sensory perception of strawberry fruit, found that overall liking was most greatly influenced by sweetness and strawberry flavor intensity67, which are affected by environmental pressures that reduce sucrose and total volatile content. While sucrose was the single metabolite with the most significant contribution to overall liking, it was found that volatiles influence perception of both flavor and sweetness through retronasal olfaction67. Thirty-eight volatile compounds significantly enhanced the perceived intensity of sweetness and may be worthwhile targets for molecular study67. Four of these volatiles are common to most studies: 3,7-dimethyl-1,6-octadien-3-ol (linalool); the methyl and ethyl esters of butanoic acid; and 2,5-dimethyl-4-methoxy-3(2H)-furanone (mesifurane). Linalool imparts a sweet, floral, citrus-like note to strawberries, while the closely related terpene nerolidol imparts a rose/apple/green note. Mesifurane is said to have a sherry-like or fusty aroma, while its precursor, furaneol (2,5-dimethyl-4-hydroxy-3(2 H)-furanone) imparts caramel and sweet notes at high concentrations. How confident can we be in these results, and are there other fruit compounds with measurable effects on consumer perception and liking? Further research combining sensory and fruit chemical analyses, including more germplasm and environments, would be valuable for answering these questions.

Several strawberry genes involved with production of compounds contributing to flavor/aroma were identified relatively early, including FaSAAT, encoding a fruit-specific ALCOHOL ACYLTRANSFERASE that is exclusively expressed in receptacle tissue120; FaOMT, encoding an O-methyltransferase catalyzing the formation of mesifurane from furaneol121; and FaNES1 encoding a nerolidol synthase capable of generating linalool or nerolidol with geranyl diphosphate or farnesyl diphosphate, respectively, as substrates122. FaNES1 is present and highly expressed in the fruit of 112 F. ×ananassa cultivars as well as in all but three of 46 octoploid wild F. virginiana and F. chiloensis progenitor species accessions123. Conversely, FaNES1 was not present in any diploid, tetraploid, or hexaploid accession tested. Instead, the olefinic monoterpenes, namely, α-pinene, β-phellandrene, and β-myrcene are produced, which contribute to turpentine-like, woody, resinous, and unpleasant odors that are selected against by commercial strawberry breeders123.

QTL analysis in a population segregating for production of mesifurane and other volatiles identified a homoeolog of FaOMT as the locus responsible for natural variation of mesifurane content124. Mesifurane nonproducers lack a 30 bp promoter sequence containing putative binding sites for basic/helix–loop–helix, MYB and BZIP transcription factors. This polymorphism fully cosegregates with both the presence of mesifurane and the high expression of FaOMT during ripening124.

The amount of the volatile γ-decalactone, which is associated with “peach-like” aroma in strawberry fruit, is highly environmentally influenced. Using a metabolomics approach combined with RNAseq, Chambers et al.53 identified the fatty acid desaturase gene (FaFAD1) essential to its biosynthesis. In parallel, Sánchez-Sevilla et al.55 discovered the same locus by combining transcriptome analysis with a map-based approach. Interestingly, about half of cultivars tested were nonproducers of this volatile and had a deletion of this gene, pointing to FaFAD1 as a potential target for breeding or engineering back into desirable cultivars. Methyl anthranilate (MA) contributes to the fruity, flowery, and aromatic flavor of the woodland strawberry, F. vesca. MA was only found in a few old strawberry cultivars such as “Mieze Schindler” and “Mara des Bois”125, and a gene encoding ANTHRANILIC ACID METHYL TRANSFERASE (FanAAMT) was recently isolated from the latter using transcriptome bulk-segregant analysis54. While FanAAMT modulates the amplitude of MA accumulation, additional genes hypothesized to be required for basal MA production have yet to be identified. Consumer preference and yield penalties or advantages will help determine the balance of volatiles in cultivars of the future. The availability of high-quality octoploid and diploid strawberry genomes opens opportunities to manipulate known genes involved in volatile production and identify genes required for production of the many other volatiles involved in strawberry aroma.

Texture is another strawberry fruit quality attribute of great importance, both for consumer sensory preference and quality after cold storage. Several strawberry genes encoding enzymes involved in the disassembly of fruit cell walls and the solubilization of pectins in the middle lamella that results in softening during fruit ripening have been identified. In the past 5 years, genes encoding pectin solubilizing enzymes: pectate lyase (FaplC); endo-β-1,4-glucanase (FaEG3)126; β-galactosidase (FaβGal1)127; and polygalacturonase (FaPG1)128 have been implicated in fruit softening during ripening. Nardi et al.129,130 have demonstrated a role for expansins, the nonenzymatic cell wall proteins that are associated with cell wall loosening, in strawberry ripening by functional analysis of the promoter of Expansin 2 (FaEXP2). Recently, the xyloglucan endotransglycolase/hydrolase gene family was characterized in F. vesca (FveXTHs)131. These enzymes modify xyloglucans that cross-link cellulose microfibrils. Most of the 26 genes identified are expressed in fruit, but expression of a subset of four genes increases during fruit softening. Fruit texture affects not only sensory perception, but also the ability of strawberry to withstand packing and long distance shipping without bruising. Additional information regarding the identity and regulation of genes encoding enzymes that contribute to fruit softening in strawberry can be mined from the existing Fragaria transcriptome data, and their function(s) analyzed using stable transformation, transient expression, and CRISPR technology. Cultivars or wild accessions with the desired firmness qualities that satisfy producers and consumers could help mitigate fruit losses postharvest.

Not to be ignored is the role of small noncoding RNAs such as microRNAs (miRNAs) in fruit quality, since these are known effectors of regulatory pathways underlying plant development including fruit ripening. In tomato, miRNAs were differentially expressed in the developing fruit, and one miRNA, sly-miR1917, targets a transcription factor that negatively regulates ethylene responses during ripening132. Almost 200 miRNAs were identified in F. ×ananassa133,134. In F. vesca, one of these, miR399, appears to be involved in soluble solids content and in fructose and glucose content135. The molecular mechanism of miRNA399 action is not known and roles of miRNAs in regulating various aspects of F. × ananassa fruit quality require further investigation.

Other important quality factors include fruit color and phytochemical compounds important to human health. FaMYB10 is one of the known transcription factors involved in F. ×ananassa fruit ripening (Table 2).

Table 2 Known transcription factors involved in octoploid Fragaria fruit ripening

This transcription factor is the master regulator of flavonoid/phenylpropanoid metabolism resulting in the red color of the dessert strawberry. Throughout the world consumer preferences vary, with some preferring deeper, more purple coloration, and others a brighter or lighter red. Likewise, preferences for fruit shape differ. Some producers are now promoting a small cigar-shaped fruit rather than fruit with the familiar heart-shape because of ease of packaging. Recently reported results implicate auxin and GA in fruit width and length, respectively, in F. vesca136. The molecular underpinnings of color variation and fruit shape in Fragaria are mostly unknown or unreported, although clearly of interest for development of molecular markers for breeding purposes to meet changing consumer tastes.

In strawberry, antioxidant compounds such as polyphenols and ascorbic acid (vitamin C) are important nutritional traits137,138. Yet these are difficult traits to assess as they are influenced not only by genotype, but by the growing environment and by developmental stage. For example, levels of the bioactive nonflavonoid polyphenol, ellagic acid (EA), is higher in achenes from ripe fruit of the F. vesca cultivar Yellow Wonder than in achenes from ripe fruit of F. ×ananassa cultivar Calypso. A complication for improving fruit nutrient quality is that EA levels are higher in achenes than in receptacles of all cultivars tested, and EA is found primarily at small green stage. In addition, the mode of inheritance of EA content is yet to be elucidated139. Previously unidentified bioactive compounds, such as the acylphloroglucinol glucosides discovered in F. ×ananassa while examining the enzymatic properties of recombinant F. vesca chalcone synthases140, may also exist in Fragaria species. With the availability of modern methods in metabolomics and allied fields, discovery of additional bioactive compounds is likely, and these methods can be applied to direct molecular approaches to improving fruit quality. In the future, metabolic flux analysis should also enhance our ability to delineate what biochemical pathways are good targets for fruit quality improvement as well and to predict what modifications may influence fruit quality parameters.

Plant–pathogen interactions

There has been ample progress in recent years in describing the genetic architecture of disease resistance in cultivated strawberry. Many resistances appear to be primarily conferred by one or two major loci or large-effect QTL, including to Phytophthora fragariae141, Xanthomonas fragariae57, Phytophthora cactorum60, Fusarium oxysporum f.sp fragariae142, Colletotrichum gloeosporoides58, and Colletotrichum acutatum59,143. For P. cactorum, additional minor loci have recently been identified144. On the other hand, resistances to Verticillium dahaliae145 and Podosphaera aphanis146 appear to be quite complex, with no major loci identified to date. This suggests that genomic prediction approaches for these two diseases would be most effective. However, with the advent of the “Camarosa” genome, an opportunity exists to characterize Mildew Locus O (MLO) genes in strawberry toward potential gene editing solutions.

The genetic architecture of resistance to charcoal rot (Macrophomina phaseolina) has not yet been reported in strawberry. Elucidating the genetics of resistance to M. phaseolina should be a high priority in the future, given the recent spread of this pathogen in important production regions and the lack of effective controls for this disease147. In addition, no resistance genes have been reported against gray mold caused by Botrytis cinerea148. Instead, it seems most likely that any small differences in tolerance to this disease among cultivars results from morphological variations in flower structures, fruit firmness, etc. Because strong resistance to B. cinerea is not likely to result from conventional breeding, a gene editing solution may be most viable.

Where disease resistances are conferred by one or a few genes, genetic and breeding approaches to characterize and increase resistance are straightforward. In the cases where classical R genes are involved, the development of custom-capture libraries and single-molecule resequencing of captured target sequences has been quite effective for identifying causal gene variants149. In fact, such a resource has now been developed for cultivated strawberry in the form of a RenSeq library based on the “Camarosa” reference and resequencing of a number of elite cultivars and breeding lines150. Combining this resource with mapping and association genetics approaches should help uncover subgenome-specific variants underlying known loci and lead to the cloning of R genes in octoploid strawberry. Given the tremendous allelic diversity present in strawberry and the large copy numbers and highly repetitive coding sequences typical of R genes, assembling long reads from single-molecule real-time sequencing should be helpful to this endeavor.

Hand in hand with characterization of R genes, we recommend the characterization of pathogen populations in order to understand the durability of resistances. The paradigm of a gene-for-gene arms race has been long established, but a more accurate assessment of the durability of resistance could arise from an understanding of the selective forces operating on pathogen effectors. Dual RNA-seq technology can help uncover the dynamic interactions of pathogen and host151,152,153. Both the pathogen and the host transcriptomes are simultaneously captured and analyzed in silico to distinguish species-specific transcripts. For some complex interactions, single cell transcriptomics coupled with protein and metabolite analysis may be helpful.

What new insights into disease resistance in strawberry could be gained simply from studying the population structures of causal pathogens? Would identifying and characterizing pathogen effectors give us meaningful insights into the control of pathogens through breeding and other means? It is intriguing that some recently discovered resistance loci in strawberry confer very strong resistances and yet have apparently been durably effective in commercial production for many decades59,142. Cloning the first R genes and pathogen effectors involved these interactions will help us to understand why.

The Genome Database For Rosaceae: a vital resource for strawberry research

The Genome Database for Rosaceae (GDR, https://www.rosaceae.org)154 is the central repository and data-mining resource for genomics, genetics, and breeding data of Rosaceae, including strawberry and related crops such as almond, apple, apricot, blackberry, cherry, peach, pear, plum, raspberry, and rose. The volume and type of data generated for strawberry research has markedly increased in the past ten years. This includes whole-genome assembly data, RNA-seq data, multiple SNP arrays, increased numbers of QTL, and more genotypic and phenotypic data. The massive volume of data generated by the strawberry research community, combined with active curation, integration, further analyses and tool development by the GDR team has resulted in marked expansion in the data and functionality available for strawberry.

In addition to the near-complete chromosome-scale assembly for F. ×ananassa10, two draft genome assemblies for F. ×ananassa155 are available. Four genome assemblies, including the newest v4.030, are also available for F. vesca. New and much improved annotation v4.0.a2156, including 34,007 protein-coding genes with 98.1% complete Benchmarking Universal Single-Copy Orthologs (BUSCOs), is available. For older assemblies F. vesca genome v1.1157 and v2.09, additional annotations are also available: v1.1.a2158 and v2.0.a2156, respectively. The draft genome assemblies of four wild diploid Fragaria species155 and of Potentilla micrantha159 a species that does not develop fleshy fruit but is closely related to Fragaria, are also available. In addition, the whole genome of F. iinumae21 has recently become available.

GDR now provides a reference transcriptome (F. ×ananassa RefTrans V1) that combines published RNA-Seq and EST data sets. The GDR team provides additional computational annotation for both predicted genes of whole-genome assemblies and RefTran datasets with homology to genes of closely related or model plant species and assignment of InterPro protein domains160 and GO terms161,162. The genome assembly and transcript data can be accessed through the Fragaria genus and species pages, Gene/Transcript search page, JBrowse163 and BLASTX164.

The octoploid “Camarosa” genome, F. iinumae v1.0, and both annotation versions of F. vesca Genome v4.0, are used in a synteny analysis with whole-genome assemblies from 18 Rosaceae species using MCScanX165 with results available to view and search through the Synteny Viewer. GDR hosts 29 genetic maps for Fragaria species, most of which contain trait loci and can be viewed and compared through the MapViewer. Detailed data on 505 QTLs and 5 MTLs for 124 horticultural traits, and 171,115 genetic markers for Fragaria that includes 154,739 SNPs are available, as well as SNP data from the iStraw 90 K array for cultivated strawberry32. The SNP data is accessible through JBrowse tracks, downloadable files and can be searched and downloaded from the SNP Marker and All Marker search pages. The Marker search page now includes filtering by trait name, which allows users to search for markers that are near and/or within QTLs using the associated trait name. Phenotyping data from the public projects such as RosBREED61 are available from GDR. In addition to the “Search Trait Evaluation” page, the public breeding data can be queried and downloaded using the Breeders toolbox. A new module in GDR, the Breeding Information Management System (BIMS), now provides breeders and breeding project teams with tools to easily store, manage, archive and analyze their private or public breeding data.

The availability of whole-genome assembly and SNP array data for the cultivated octoploid strawberry, along with wealth of QTL data that are integrated in the community database with data from other related crops are expected to accelerate research and practical tools such as DNA tests. BIMS in GDR will help breeders not only to organize their data but also to utilize the tools and resources that are available for strawberry improvement.


The first high-quality chromosome-scale assembly of cultivated strawberry is the starting point for an exciting research roadmap for the future (Fig. 4). The debate over the origins of the octoploid genome will continue to evolve. Meanwhile, the new “FanaSNP” array will enable genetic and breeding advances, including increased DNA test availability. We expect that the use of genomic prediction for complex traits will rapidly increase, in proportion to the increased quality of array genotyping in strawberry and advances in prediction methods. Basic research on gene function will accelerate discoveries on the biology of many phenotypes, especially fruit development, fruit quality and plant-pathogen interaction traits. In particular, advances in understanding the biosynthesis of aromatic volatile compounds could help form the basis for more flavorful strawberries. One of the most exciting prospects is the development of an octoploid pangenome that reveals the DNA variation underlying the striking phenotypic variability in cultivated strawberry and its immediate progenitors. In order to maximize these opportunities, it is critical that the strawberry research community continue to invest in the Genome Database for Rosaceae and other collaborative genomic resources and endeavors at a global scale.

Fig. 4

A roadmap for future research in octoploid strawberry that begins with high-quality genome resources and leads to genetic improvement, through both basic and applied avenues


  1. 1.

    Staudt, G. Taxonomic studies in the genus Fragaria typification of Fragaria species known at the time of Linnaeus. Can. J. Bot. 40, 869–886 (1962).

    Article  Google Scholar 

  2. 2.

    Staudt, G. The species of Fragaria, their taxonomy and geographical distribution. Acta Hort. 265, 23–34 (1989).

    Google Scholar 

  3. 3.

    Staudt, G. Strawberry biogeography, genetics and systematics. Acta Hortic. 842, 71–84 (2009).

    Article  Google Scholar 

  4. 4.

    Darrow G. M. The Strawberry. https://www.cabdirect.org/cabdirect/abstract/19680300487 (1966).

  5. 5.

    Finn, C. E., Retamales, J. B., Lobos, G. A. & Hancock, J. F. The Chilean Strawberry (Fragaria chiloensis): over 1000 years of domestication. Hort. Sci. 48, 418–421 (2013).

    Article  Google Scholar 

  6. 6.

    Bringhurst, R. S. Cytogenetics and evolution of American Fragaria. Hort. Sci. 25, 879–881 (1990).

  7. 7.

    Yang, Y. & Davis, T. M. A new perspective on polyploid Fragaria (strawberry) genome composition based on large-scale, multi-locus phylogenetic analysis. Genome Biol. Evol. 9, 3433–3448 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Hardigan, M. A. et al. Domestication of temperate and coastal hybrids with distinct ancestral gene selection in octoploid strawberry. The Plant Genome https://doi.org/10.3835/plantgenome2018.07.0049 (2018).

  9. 9.

    Tennessen, J. A., Govindarajulu, R., Ashman, T.-L. & Liston, A. Evolutionary origins and dynamics of octoploid strawberry subgenomes revealed by dense targeted capture linkage maps. Genome Biol. Evol. 6, 3295–3313 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Edger, P. P. et al. Origin and evolution of the octoploid strawberry genome. Nat. Genet. 51, 541–547 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hardigan, M. A. et al. Genome synteny has been conserved among the octoploid progenitors of cultivated strawberry over millions of years of evolution. Front. Genet. 10, 1789 (2020).

  12. 12.

    Dillenberger, M. S., Wei, N., Tennessen, J. A., Ashman, T.-L. & Liston, A. Plastid genomes reveal recurrent formation of allopolyploid Fragaria. Am. J. Bot. 105, 862–874 (2018).

    Article  Google Scholar 

  13. 13.

    Sjulin T. M., Dale A. Genetic diversity of North American strawberry cultivars. J. Am. Soc. Hortic. Sci. USA http://agris.fao.org/agris-search/search.do?recordID=US8725249 (1987).

  14. 14.

    Rousseau-Gueutin, M. et al. Comparative genetic mapping between octoploid and diploid Fragaria species reveals a high level of colinearity between their genomes and the essentially disomic behavior of the cultivated octoploid strawberry. Genetics 179, 2045–2060 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Rousseau Gueutin M. et al. Tracking the evolutionary history of polyploidy in Fragaria L. (strawberry): new insights from phylogenetic analyses of low-copy nuclear genes. Mol. Phylogenet. Evol. https://doi.org/10.1016/j.ympev.2008.12.024 (2009).

  16. 16.

    Folta, K. M. & Davis, T. M. Strawberry genes and genomics. Crit. Rev. Plant Sci. 25, 399–415 (2006).

    Article  CAS  Google Scholar 

  17. 17.

    Sargent, D. J. et al. A genetic linkage map of the cultivated strawberry (Fragaria ×ananassa) and its comparison to the diploid Fragaria reference map. Mol. Breed. 24, 293–303 (2009).

    Article  CAS  Google Scholar 

  18. 18.

    Njuguna, W., Liston, A., Cronn, R., Ashman, T.-L. & Bassil, N. Insights into phylogeny, sex function and age of Fragaria based on whole chloroplast genome sequencing. Mol. Phylogenet. Evol. 66, 17–29 (2013).

    Article  Google Scholar 

  19. 19.

    Liston, A., Cronn, R. & Ashman, T.-L. Fragaria: a genus with deep historical roots and ripe for evolutionary and ecological insights. Am. J. Bot. 101, 1686–1699 (2014).

    Article  Google Scholar 

  20. 20.

    Liston, A. et al. Revisiting the origin of the octoploid strawberry. Nat. Genet. 52, 2–4 (2019).

  21. 21.

    Edger, P. P. et al. Reply to: revisiting the origin of octoploid strawberry. Nat. Genet. 52, 5–7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Byrne, D. & Jelenkovic, G. Cytological diploidization in the cultivated octoploid strawberry Fragaria ×ananassa. Can. J. Genet. Cytol. 18, 653–659 (1976).

    Article  Google Scholar 

  23. 23.

    Arulsekar, S. & Bringhurst, R. S. Genetic model for the enzyme marker PGI in diploid California Fragaria vesca LIts variability and use in elucidating the mating system. J. Hered. 72, 117–120 (1981).

    Article  Google Scholar 

  24. 24.

    Sargent, D. J. et al. A microsatellite linkage map for the cultivated strawberry (Fragaria ×ananassa) suggests extensive regions of homozygosity in the genome that may have resulted from breeding and selection. Theor. Appl. Genet. 124, 1229–1240 (2012).

    Article  CAS  Google Scholar 

  25. 25.

    DiMeglio L. M., Staudt G., Yu H., Davis T. M. A phylogenetic analysis of the genus Fragaria (strawberry) using intron-containing sequence from the ADH-1 gene. PloS ONE https://doi.org/10.1371/journal.pone.0102237 (2014).

  26. 26.

    Kamneva, O. K., Syring, J., Liston, A. & Rosenberg, N. A. Evaluating allopolyploid origins in strawberries (Fragaria) using haplotypes generated from target capture sequencing. BMC Evolut. Biol. 17, 180 (2017).

    Article  CAS  Google Scholar 

  27. 27.

    Lim, K. Y. et al. Rapid chromosome evolution in recently formed polyploids in tragopogon (Asteraceae). PLOS ONE 3, e3353 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Salmon, A., Flagel, L., Ying, B., Udall, J. A. & Wendel, J. F. Homoeologous nonreciprocal recombination in polyploid cotton. N. Phytol. 186, 123–134 (2010).

    Article  CAS  Google Scholar 

  29. 29.

    Xiong, Z., Gaeta, R. T. & Pires, J. C. Homoeologous shuffling and chromosome compensation maintain genome balance in resynthesized allopolyploid Brassica napus. Proc. Natl Acad. Sci. USA 108, 7908–7913 (2011).

    Article  Google Scholar 

  30. 30.

    Edger P. P. et al. Single-molecule sequencing and optical mapping yields an improved genome of woodland strawberry (Fragaria vesca) with chromosome-scale contiguity. Gigascience https://doi.org/10.1093/gigascience/gix124 (2018).

  31. 31.

    Sargent, D. J. et al. HaploSNP affinities and linkage map positions illuminate subgenome composition in the octoploid, cultivated strawberry (Fragaria ×ananassa). Plant Sci. 242, 140–150 (2016).

    Article  CAS  Google Scholar 

  32. 32.

    Bassil N. V. et al. Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria ×ananassa. BMC Genomics https://doi.org/10.1186/s12864-015-1310-1 (2015).

  33. 33.

    Verma, S. et al. Development and evaluation of the Axiom ® IStraw35 384HT array for the allo-octoploid cultivated strawberry Fragaria ×ananassa. Acta Horticult. 1156, 75–82 (2017).

    Article  Google Scholar 

  34. 34.

    Ganal, M. W. et al. Large SNP arrays for genotyping in crop plants. J. Biosci. 37, 821–828 (2012).

    Article  CAS  Google Scholar 

  35. 35.

    Rasheed, A. et al. Development and validation of KASP assays for genes underpinning key economic traits in bread wheat. Theor. Appl. Genet. 129, 1843–1860 (2016).

    Article  CAS  Google Scholar 

  36. 36.

    Vining, K. J. et al. Genotyping-by-sequencing enables linkage mapping in three octoploid cultivated strawberry families. PeerJ 5, e3731 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Feldmann M. J. et al. Genotyping-By-Sequencing and Reference Genome Enabled Variant Discovery in Octoploid Strawberry. https://doi.org/10.5281/zenodo.3576540 (2019).

  38. 38.

    Clark, L. V., Lipka, A. E. & Sacks, E. J. polyRAD: Genotype calling with uncertainty from sequencing data in polyploids and diploids. Genes Genomes Genet. 9, 663–673 (2019).

    CAS  Google Scholar 

  39. 39.

    Gerard, D., Ferrão, L. F. V., Garcia, A. A. F. & Stephens, M. Genotyping polyploids from messy sequencing data. Genetics 210, 789–807 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Schwarz, U. I., Gulilat, M. & Kim, R. B. The role of next-generation sequencing in pharmacogenetics and pharmacogenomics. Cold Spring Harb. Perspect. Med. 9, a03 (2019).

    Article  CAS  Google Scholar 

  41. 41.

    Davey, J. W. et al. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat. Rev. Genet. 12, 499–510 (2011).

    Article  CAS  Google Scholar 

  42. 42.

    Davey, J. W. et al. Special features of RAD Sequencing data: implications for genotyping. Mol. Ecol. 22, 3151–3164 (2013).

    Article  CAS  Google Scholar 

  43. 43.

    Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. & Johnson, E. A. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 17, 240–248 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Ali, O. A. et al. RAD capture (rapture): flexible and efficient sequence-based genotyping. Genetics 202, 389–400 (2016).

    Article  CAS  Google Scholar 

  45. 45.

    Dijk T. et al. Genomic rearrangements and signatures of breeding in the allo-octoploid strawberry as revealed through an allele dose based SSR linkage map. BMC Plant Biol. https://doi.org/10.1186/1471-2229-14-55 (2014).

  46. 46.

    Sánchez-Sevilla, J. F. et al. Diversity arrays technology (DArT) marker platforms for diversity analysis and linkage mapping in a complex crop, the octoploid cultivated strawberry (Fragaria ×ananassa). PLOS ONE 10, e0144960 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Davik, J. et al. A ddRAD based linkage map of the cultivated strawberry, Fragaria ×ananassa. PLOS ONE 10, e0137746 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Gordon, S. P. et al. Extensive gene content variation in the Brachypodium distachyon pan-genome correlates with population structure. Nat. Commun. 8, 1–13 (2017).

    Article  CAS  Google Scholar 

  49. 49.

    Hardigan, M. A. et al. Genome reduction uncovers a large dispensable genome and adaptive role for copy number variation in asexually propagated Solanum tuberosum. Plant Cell 28, 388–405 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Golicz, A. A. et al. The pangenome of an agronomically important crop plant Brassica oleracea. Nat. Commun. 7, 1–8 (2016).

    Article  CAS  Google Scholar 

  51. 51.

    Gaston, A. et al. PFRU, a single dominant locus regulates the balance between sexual and asexual plant reproduction in cultivated strawberry. J. Exp. Bot. 64, 1837–1848 (2013).

    Article  CAS  Google Scholar 

  52. 52.

    Verma, S. et al. Clarifying sub-genomic positions of QTLs for flowering habit and fruit quality in U.S. strawberry (Fragaria ×ananassa) breeding populations using pedigree-based QTL analysis. Hortic. Res. 4, 17062 (2017).

  53. 53.

    Chambers, A. H. et al. Identification of a strawberry flavor gene candidate using an integrated genetic-genomic-analytical chemistry approach. BMC Genomics 15, 217 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Pillet, J. et al. Identification of a methyltransferase catalyzing the final step of methyl anthranilate synthesis in cultivated strawberry. BMC Plant Biol. 17, 147 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Sánchez-Sevilla, J. F., Cruz-Rus, E., Valpuesta, V., Botella, M. A. & Amaya, I. Deciphering gamma-decalactone biosynthesis in strawberry fruit using a combination of genetic mapping, RNA-Seq and eQTL analyses. BMC Genomics 15, 1 (2014).

    Article  CAS  Google Scholar 

  56. 56.

    Zorrilla-Fontanesi, Y. et al. Quantitative trait loci and underlying candidate genes controlling agronomical and fruit quality traits in octoploid strawberry (Fragaria ×ananassa). Theor. Appl. Genet. 123, 755–778 (2011).

    Article  Google Scholar 

  57. 57.

    Roach, J. A., Verma, S., Peres, N. A., Jamieson, A. R. & van de Weg, W. E. Bink MCAM et al. FaRXf1: a locus conferring resistance to angular leaf spot caused by Xanthomonas fragariae in octoploid strawberry. Theor. Appl. Genet. 129, 1191–1201 (2016).

    Article  CAS  Google Scholar 

  58. 58.

    Anciro, A. et al. FaRCg1: a quantitative trait locus conferring resistance to Colletotrichum crown rot caused by Colletotrichum gloeosporioides in octoploid strawberry. Theor. Appl. Genet. 131, 2167–2177 (2018).

    Article  CAS  Google Scholar 

  59. 59.

    Salinas, N., Verma, S., Peres, N. & Whitaker, V. M. FaRCa1: a major subgenome-specific locus conferring resistance to Colletotrichum acutatum in strawberry. Theor. Appl. Genet. 132, 1109–1120 (2019).

    Article  CAS  Google Scholar 

  60. 60.

    Mangandi, J. et al. Pedigree-based analysis in a multiparental population of octoploid strawberry reveals QTL alleles conferring resistance to Phytophthora cactorum. Genes Genomes Genetics 7, 1707–1719 (2017).

    CAS  PubMed  Google Scholar 

  61. 61.

    Iezzoni, A. et al. RosBREED2: progress and future plans to enable DNA-informed breeding in the Rosaceae. Acta Hortic. 1172, 115–118 (2017).

    Article  Google Scholar 

  62. 62.

    Noh, Y.-H., Lee, S., Whitaker, V. M., Cearley, K. R. & Cha, J.-S. A high-throughput marker-assisted selection system combining rapid DNA extraction high-resolution melting and simple sequence repeat analysis: strawberry as a model for fruit crops. J. Berry Res. 7, 23–31 (2017).

    Article  CAS  Google Scholar 

  63. 63.

    Noh Y.-H. et al. High-throughput marker assays for FaRPc2-mediated resistance to Phytophthora crown rot in octoploid strawberry. Mol. Breed. https://doi.org/10.1007/s11032-018-0861-7 (2018).

  64. 64.

    Oh, Y. et al. The strawberry DNA testing handbook. Hort. Sci. 54, 2267–2270 (2019).

    Article  Google Scholar 

  65. 65.

    Fragaria ×ananassa | GDR. https://www.rosaceae.org/organism/Fragaria/x-ananassa?pane=resource-4 (2019).

  66. 66.

    Gezan, S. A., Osorio, L. F., Verma, S. & Whitaker, V. M. An experimental validation of genomic selection in octoploid strawberry. Hortic. Res. 4, 16070 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Schwieterman, M. L. et al. Strawberry flavor: diverse chemical compositions, a seasonal influence, and effects on sensory perception. PLoS ONE 9, e88446 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Barbey, C. R. et al. The genetics of differential gene expression related to fruit traits in strawberry (Fragaria ×ananassa). Front. Genet. 10, 1317 (2019).

  69. 69.

    Galpaz, N. et al. Deciphering genetic factors that determine melon fruit-quality traits using RNA-Seq-based high-resolution QTL and eQTL mapping. Plant J. 94, 169–191 (2018).

    Article  CAS  Google Scholar 

  70. 70.

    Heide, O. M., Stavang, J. A. & Sønsteby, A. Physiology and genetics of flowering in cultivated and wild strawberries—a review. J. Hortic. Sci. Biotechnol. 88, 1–18 (2013).

    Article  CAS  Google Scholar 

  71. 71.

    Costes E. et al. Bud structure, position and fate generate various branching patterns along shoots of closely related Rosaceae species: a review. Front. Plant Sci. https://doi.org/10.3389/fpls.2014.00666. (2014).

  72. 72.

    Brown, T. & Wareing, P. F. The genetical control of the everbearing habit and three other characters in varieties of Fragaria vesca. Euphytica 14, 97–112 (1965).

    Google Scholar 

  73. 73.

    Koskela, E. A. et al. Mutation in TERMINAL FLOWER1 reverses the photoperiodic requirement for flowering in the wild strawberry Fragaria vesca. Plant Physiol. 159, 1043–1054 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Iwata, H. et al. The TFL1 homologue KSN is a regulator of continuous flowering in rose and strawberry. Plant J. 69, 116–125 (2012).

    Article  CAS  Google Scholar 

  75. 75.

    Tenreira, T. et al. A specific gibberellin 20-oxidase dictates the flowering-runnering decision in diploid strawberry. Plant Cell 29, 2168–2182 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Nakano, Y., Higuchi, Y., Yoshida, Y. & Hisamatsu, T. Environmental responses of the FT/TFL1 gene family and their involvement in flower induction in Fragaria ×ananassa. J. Plant Physiol. 177, 60–66 (2015).

    Article  CAS  Google Scholar 

  77. 77.

    Koskela, E. A. et al. TERMINAL FLOWER1 is a breeding target for a novel everbearing trait and tailored flowering responses in cultivated strawberry (Fragaria ×ananassa Duch.). Plant Biotechnol. J. 14, 1852–1861 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Mouhu, K. et al. The Fragaria vesca homolog of SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 represses flowering and promotes vegetative growth. Plant Cell 25, 3296–3310 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Rantanen, M., Kurokura, T., Jiang, P., Mouhu, K. & Hytönen, T. Strawberry homologue of TERMINAL FLOWER1 integrates photoperiod and temperature signals to inhibit flowering. Plant J. 82, 163–173 (2015).

    Article  CAS  Google Scholar 

  80. 80.

    Kurokura, T., Samad, S., Koskela, E., Mouhu, K. & Hytönen, T. Fragaria vesca CONSTANS controls photoperiodic flowering and vegetative development. J. Exp. Bot. 68, 4839–4850 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Mouhu, K. et al. Identification of flowering genes in strawberry, a perennial SD plant. BMC Plant Biol. 9, 122 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Castro P. et al. Genetic mapping of day-neutrality in cultivated strawberry. Mol. Breed. https://doi.org/10.1007/s11032-015-0250-4 (2015).

  83. 83.

    Honjo, M. et al. Simple sequence repeat markers linked to the everbearing flowering gene in long-day and day-neutral cultivars of the octoploid cultivated strawberry Fragaria ×ananassa. Euphytica 209, 291–303 (2016).

    Article  Google Scholar 

  84. 84.

    Perrotte, J. et al. Narrowing down the single homoeologous FaPFRU locus controlling flowering in cultivated octoploid strawberry using a selective mapping strategy. Plant Biotechnol. J. 14, 2176–2189 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Salinas, N. R. et al. Validation of molecular markers associated with perpetual flowering in octoploid Fragaria germplasm. Mol. Breed. https://doi.org/10.1007/s11032-017-0672-2 (2017).

  86. 86.

    Samad, S. et al. Additive QTLs on three chromosomes control flowering time in woodland strawberry (Fragaria vesca L.). Hortic. Res. 4, 17020 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Guttridge, C. G. & Thompson, P. A. The effect of gibberellins on growth and flowering of Fragaria and Duchesnea. J. Exp. Bot. 15, 631–646 (1964).

    Article  CAS  Google Scholar 

  88. 88.

    Caruana, J. C., Sittmann, J. W., Wang, W. & Liu, Z. Suppressor of Runnerless encodes a DELLA protein that controls runner formation for asexual reproduction in strawberry. Mol. Plant 11, 230–233 (2018).

    Article  CAS  Google Scholar 

  89. 89.

    Hytönen, T., Mouhu, K., Koivu, I., Elomaa, P., Junttila, O. Planting year prohexadione-calcium treatment increases the cropping potential and yield of strawberry. Acta Hortic. 842, 741–744 (2009).

  90. 90.

    Hytönen, T., Elomaa, P., Moritz, T. & Junttila, O. Gibberellin mediates daylength-controlled differentiation of vegetative meristems in strawberry (Fragaria ×ananassa Duch). BMC Plant Biol. 9, 18 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Seymour, G. B., Østergaard, L., Chapman, N. H., Knapp, S. & Martin, C. Fruit development and ripening. Annu. Rev. Plant Biol. 64, 219–241 (2013).

    Article  CAS  Google Scholar 

  92. 92.

    Aharoni A., Vorst O. DNA microarrays for functional plant genomics. in Functional Genomics (ed Town C.) 99–118 (Springer Netherlands, Dordrecht, 2002).

  93. 93.

    Härtl, K. et al. Early metabolic and transcriptional variations in fruit of natural white-fruited Fragaria vesca genotypes. Sci. Rep. 7, 1–16 (2017).

    Article  CAS  Google Scholar 

  94. 94.

    Kang, C. et al. Genome-scale transcriptomic insights into early-stage fruit development in woodland strawberry Fragaria vesca. Plant Cell 25, 1960–1978 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Nitsch, J. P. Growth and morphogenesis of the strawberry as related to auxin. Am. J. Bot. 37, 211–215 (1950).

    Article  CAS  Google Scholar 

  96. 96.

    McAtee P., Karim S., Schaffer R. J., David K. A dynamic interplay between phytohormones is required for fruit development, maturation, and ripening. Front. Plant Sci. https://doi.org/10.3389/fpls.2013.00079 (2013).

  97. 97.

    Cherian, S., Figueroa, C. R. & Nair, H. ‘Movers and shakers’ in the regulation of fruit ripening: a cross-dissection of climacteric versus non-climacteric fruit. J. Exp. Bot. 65, 4705–4722 (2014).

    Article  CAS  Google Scholar 

  98. 98.

    Medina-Puche, L. et al. An R2R3-MYB transcription factor regulates eugenol production in ripe strawberry fruit receptacles. Plant Physiol. 168, 598–614 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Medina-Puche, L. et al. Extensive transcriptomic studies on the roles played by abscisic acid and auxins in the development and ripening of strawberry fruits. Funct. Integr. Genomics 16, 671–692 (2016).

    Article  CAS  Google Scholar 

  100. 100.

    Sánchez-Sevilla, J. F. et al. Gene expression atlas of fruit ripening and transcriptome assembly from RNA-seq data in octoploid strawberry (Fragaria ×ananassa). Sci. Rep. 7, 1–13 (2017).

    Article  CAS  Google Scholar 

  101. 101.

    Wang, Q.-H. et al. Transcriptome analysis around the onset of strawberry fruit ripening uncovers an important role of oxidative phosphorylation in ripening. Sci. Rep. 7, 1–11 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Feng L., Xia R., Liu Y. Comprehensive characterization of miRNA and PHAS loci in the diploid strawberry (Fragaria vesca) genome. Hortic. Plant J. https://doi.org/10.1016/j.hpj.2019.11.004 (2019).

  103. 103.

    Carvalho, R. F., Carvalho, S. D., O’Grady, K. & Folta, K. M. Agroinfiltration of strawberry fruit—a powerful transient expression system for gene validation. Curr. Plant Biol. 6, 19–37 (2016).

    Article  Google Scholar 

  104. 104.

    Glover, N. M., Redestig, H. & Dessimoz, C. Homoeologs: what are they and how do we infer them? Trends Plant Sci. 21, 609–621 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Symons, G. M. et al. Hormonal changes during non-climacteric ripening in strawberry. J. Exp. Bot. 63, 4741–4750 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Preuß, A. et al. Expression of a functional jasmonic acid carboxyl methyltransferase is negatively correlated with strawberry fruit development. J. Plant Physiol. 171, 1315–1324 (2014).

    Article  CAS  Google Scholar 

  107. 107.

    Wasternack, C. & Xie, D. The genuine ligand of a jasmonic acid receptor. Plant Signal. Behav. 5, 337–340 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Li, C., Jia, H., Chai, Y. & Shen, Y. Abscisic acid perception and signaling transduction in strawberry: a model for non-climacteric fruit ripening. Plant Signal. Behav. 6, 1950–1953 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Jia, H.-F. et al. Abscisic acid plays an important role in the regulation of strawberry fruit ripening. Plant Physiol. 157, 188–199 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    Jia, H. et al. Type 2C protein phosphatase ABI1 is a negative regulator of strawberry fruit ripening. J. Exp. Bot. 64, 1677–1687 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Jia, H. et al. Abscisic acid and sucrose regulate tomato and strawberry fruit ripening through the abscisic acid-stress-ripening transcription factor. Plant Biotechnol. J. 14, 2045–2065 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Estrada-Johnson E. et al. Transcriptomic analysis in strawberry fruits reveals active auxin biosynthesis and signaling in the ripe receptacle. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.00889 (2017).

  113. 113.

    Muñoz-Sanhueza, L. G., Lee, Y., Tillmann, M., Cohen, J. D. & Hvoslef-Eide, A. K. Auxin analysis using laser microdissected plant tissues sections. BMC Plant Biol. 18, 133 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Merchante, C. et al. Ethylene is involved in strawberry fruit ripening in an organ-specific manner. J. Exp. Bot. 64, 4421–4439 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Chai, Y. et al. Brassinosteroid is involved in strawberry fruit ripening. Plant Growth Regul. 69, 63–69 (2013).

    Article  CAS  Google Scholar 

  116. 116.

    Giovannoni, J., Nguyen, C., Ampofo, B., Zhong, S. & Fei, Z. The epigenome and transcriptional dynamics of fruit ripening. Annu. Rev. Plant Biol. 68, 61–84 (2017).

    Article  CAS  Google Scholar 

  117. 117.

    Gu, T., Han, Y., Huang, R., McAvoy, R. J. & Li, Y. Identification and characterization of histone lysine methylation modifiers in Fragaria vesca. Sci. Rep. 6, 1–13 (2016).

    Article  CAS  Google Scholar 

  118. 118.

    Gu, T., Ren, S., Wang, Y., Han, Y. & Li, Y. Characterization of DNA methyltransferase and demethylase genes in Fragaria vesca. Mol. Genet. Genomics 291, 1333–1345 (2016).

    Article  CAS  Google Scholar 

  119. 119.

    Bai, L. et al. Comparative transcriptome analysis uncovers the regulatory functions of long noncoding RNAs in fruit development and color changes of Fragaria pentaphylla. Hortic. Res. 6, 1–15 (2019).

    Article  CAS  Google Scholar 

  120. 120.

    Aharoni, A. et al. Identification of the SAAT gene involved in strawberry flavor biogenesis by use of DNA microarrays. Plant Cell 12, 647–661 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Wein, M. et al. Isolation, cloning and expression of a multifunctional O-methyltransferase capable of forming 2,5-dimethyl-4-methoxy-3(2H)-furanone, one of the key aroma compounds in strawberry fruits. Plant J. 31, 755–765 (2002).

    Article  CAS  Google Scholar 

  122. 122.

    Aharoni, A. et al. Gain and loss of fruit flavor compounds produced by wild and cultivated strawberry species. Plant Cell 16, 3110–3131 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Chambers, A., Whitaker, V. M., Gibbs, B., Plotto, A. & Folta, K. M. Detection of the linalool-producing NES1 variant across diverse strawberry (Fragaria spp.) accessions. Plant Breed. 131, 437–443 (2012).

    Article  CAS  Google Scholar 

  124. 124.

    Zorrilla-Fontanesi, Y. et al. Genetic analysis of strawberry fruit aroma and identification of o-methyltransferase FaOMT as the locus controlling natural variation in mesifurane content. Plant Physiol. 159, 851–870 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Olbricht, K., Grafe, C., Weiss, K. & Ulrich, D. Inheritance of aroma compounds in a model population of Fragaria ×ananassa Duch. Plant Breed. 127, 87–93 (2008).

    Google Scholar 

  126. 126.

    Youssef, S. M. et al. Effect of simultaneous down-regulation of pectate lyase and endo-β-1,4-glucanase genes on strawberry fruit softening. Mol. Breed. 31, 313–322 (2013).

    Article  CAS  Google Scholar 

  127. 127.

    Paniagua, C. et al. Antisense down-regulation of the strawberry β-galactosidase gene FaβGal4 increases cell wall galactose levels and reduces fruit softening. J. Exp. Bot. 67, 619–631 (2016).

    Article  CAS  Google Scholar 

  128. 128.

    Posé, S. et al. Insights into the effects of polygalacturonase FaPG1 gene silencing on pectin matrix disassembly, enhanced tissue integrity, and firmness in ripe strawberry fruits. J. Exp. Bot. 64, 3803–3815 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Nardi, C. F. et al. Influence of plant growth regulators on Expansin2 expression in strawberry fruit. Cloning and functional analysis of FaEXP2 promoter region. Postharvest Biol. Technol. 114, 17–28 (2016).

    Article  CAS  Google Scholar 

  130. 130.

    Nardi, C. F. et al. Overexpression of the carbohydrate binding module of strawberry expansin2 in Arabidopsis thaliana modifies plant growth and cell wall metabolism. Plant Mol. Biol. 88, 101–117 (2015).

    Article  CAS  Google Scholar 

  131. 131.

    Opazo, M. C. et al. XTHs from Fragaria vesca: genomic structure and transcriptomic analysis in ripening fruit and other tissues. BMC Genomics 18, 852 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Moxon, S. et al. Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening. Genome Res. 18, 1602–1609 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Ge, A. et al. Deep sequencing discovery of novel and conserved microRNAs in strawberry (Fragaria ×ananassa). Physiol. Plant. 148, 387–396 (2013).

    Article  CAS  Google Scholar 

  134. 134.

    Han, J. et al. Computational identification of microRNAs in the strawberry (Fragaria ×ananassa) genome sequence and validation of their precise sequences by miR-RACE. Gene. 536, 151–162 (2014).

    Article  CAS  Google Scholar 

  135. 135.

    Wang, Y. et al. Improvement in fruit quality by overexpressing miR399a in woodland strawberry. J. Agric. Food Chem. 65, 7361–7370 (2017).

    Article  CAS  Google Scholar 

  136. 136.

    Liao, X. et al. Interlinked regulatory loops of ABA catabolism and biosynthesis coordinate fruit growth and ripening in woodland strawberry. Proc. Natl Acad. Sci. USA 115, E11542–E11550 (2018).

    Article  CAS  Google Scholar 

  137. 137.

    Mazzoni, L. et al. The genetic aspects of berries: from field to health. J. Sci. Food Agric. 96, 365–371 (2016).

    Article  Google Scholar 

  138. 138.

    Zhong, C. F. et al. Evaluation of vitamin C content in fruit and leaves of different strawberry genotypes. Acta Hortic. 1156, 371–378 (2017).

    Article  Google Scholar 

  139. 139.

    Muthukumaran, S., Tranchant, C., Shi, J., Ye, X. & Xue, S. J. Ellagic acid in strawberry (Fragaria spp.): biological, technological, stability, and human health aspects. Food Qual. Saf. 1, 227–252 (2017).

    Article  CAS  Google Scholar 

  140. 140.

    Song, C. et al. Acylphloroglucinol biosynthesis in strawberry fruit. Plant Physiol. 169, 1656–1670 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Haymes, K. M., Henken, B., Davis, T. M., Van & de Weg, W. E. Identification of RAPD markers linked to a Phytophthora fragariae resistance gene (Rpf1) in the cultivated strawberry. Theor. Appl. Genet. 94, 1097–1101 (1997).

    Article  CAS  Google Scholar 

  142. 142.

    Pincot, D. D. A. et al. Genome-wide association mapping uncovers Fw1, a dominant gene conferring resistance to fusarium wilt in strawberry. Genes Genomes Genetics 8, 1817–1828 (2018).

    CAS  PubMed  Google Scholar 

  143. 143.

    Denoyes-Rothan, B., Guérin, G., Lerceteau-Köhler, E. & Risser, G. Inheritance of resistance to Colletotrichum acutatum in Fragaria ×ananassa. Phytopathology 95, 405–412 (2005).

    Article  Google Scholar 

  144. 144.

    Nellist, C. F. et al. Quantitative trait loci controlling Phytophthora cactorum resistance in the cultivated octoploid strawberry (Fragaria ×ananassa). Hortic. Res. 6, 1–14 (2019).

    Article  CAS  Google Scholar 

  145. 145.

    Antanaviciute, L. et al. Mapping QTL associated with Verticillium dahliae resistance in the cultivated strawberry (Fragaria ×ananassa). Hortic. Res. 2, 15009 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. 146.

    Cockerton, H. M. et al. Identification of powdery mildew resistance QTL in strawberry (Fragaria ×ananassa). Theor. Appl. Genet. 131, 1995–2007 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Koike S. T., Bolda M. Charcoal rot of strawberry: increasing problem in California. ANR Blogs. https://ucanr.edu/blogs/blogcore/postdetail.cfm?postnum=11043 (2019).

  148. 148.

    Petrasch, S., Knapp, S. J., Kan, J. A. L. & van, Blanco‐Ulate, B. Grey mould of strawberry, a devastating disease caused by the ubiquitous necrotrophic fungal pathogen Botrytis cinerea. Mol. Plant Pathol. 20, 877–892 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  149. 149.

    Witek, K. et al. Accelerated cloning of a potato late blight–resistance gene using RenSeq and SMRT sequencing. Nat. Biotechnol. 34, 656–660 (2016).

    Article  CAS  Google Scholar 

  150. 150.

    Barbey, C. R. et al. Disease resistance genetics and genomics in octoploid strawberry. Genes Genomes Genet. 9, 3315–3332 (2019).

    Google Scholar 

  151. 151.

    Cloney, R. Dual RNA-seq for host–pathogen transcriptomics. Nat. Rev. Genet. 17, 126–127 (2016).

    Article  CAS  Google Scholar 

  152. 152.

    Nuss, A. M. et al. Tissue dual RNA-seq allows fast discovery of infection-specific functions and riboregulators shaping host–pathogen transcriptomes. Proc. Natl Acad. Sci. USA 114, E791–E800 (2017).

    Article  CAS  Google Scholar 

  153. 153.

    Naidoo, S. et al. Dual RNA-sequencing to elucidate the plant-pathogen duel. Curr. Issues Mol. Biol. 27, 127–142 (2018).

    Article  Google Scholar 

  154. 154.

    Jung, S. et al. 15 years of GDR: new data and functionality in the genome database for Rosaceae. Nucleic Acids Res. 47, D1137–D1145 (2019).

    Article  Google Scholar 

  155. 155.

    Hirakawa, H. et al. Dissection of the octoploid strawberry genome by deep sequencing of the genomes of Fragaria species. DNA Res. 21, 169–181 (2014).

    Article  CAS  Google Scholar 

  156. 156.

    Li, Y. et al. Genome re-annotation of the wild strawberry Fragaria vesca using extensive Illumina- and SMRT-based RNA-seq datasets. DNA Res. 25, 61–70 (2018).

    Article  CAS  Google Scholar 

  157. 157.

    Shulaev, V. et al. The genome of woodland strawberry (Fragaria vesca). Nat. Genet. 43, 109–116 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. 158.

    Darwish, O., Shahan, R., Liu, Z., Slovin, J. P. & Alkharouf, N. W. Re-annotation of the woodland strawberry (Fragaria vesca) genome. BMC Genomics 16, 29 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. 159.

    Buti, M. et al. The genome sequence and transcriptome of Potentilla micrantha and their comparison to Fragaria vesca (the woodland strawberry). Gigascience https://doi.org/10.1093/gigascience/giy010 (2018).

  160. 160.

    Finn, R. D. et al. InterPro in 2017—beyond protein family and domain annotations. Nucleic Acids Res. 45, D190–D199 (2017).

    Article  CAS  Google Scholar 

  161. 161.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. 162.

    The gene ontology consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).

    Article  CAS  Google Scholar 

  163. 163.

    Buels, R. et al. JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol. 17, 66 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma. 10, 421 (2009).

    Article  CAS  Google Scholar 

  165. 165.

    Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49–e49 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. 166.

    van de Weg, W. E. A gene-for-gene model to explain interactions between cultivars of strawberry and races of Phytophthora fragariae var. fragariae. Theor. Appl. Genet. 94, 445–451 (1997).

    Article  Google Scholar 

  167. 167.

    Haymes, K. M. et al. Development of SCAR markers linked to a Phytophthora fragariae resistance gene and their assessment in European and North American strawberry genotypes. J. Am. Soc. Hortic. Sci. 125, 330–339 (2000).

    Article  CAS  Google Scholar 

  168. 168.

    Rugienius, R., Siksnianas, T., Stanys, V., Gelvonauskiene, D. & Bendokas, V. Use of RAPD and SCAR markers for identification of strawberry genotypes carrying red stele (Phytophtora fragariae) resistance gene Rpf1. Agron. Res. 4, 335–339 (2006).

    Google Scholar 

  169. 169.

    Sasnauskas, A. et al. Screening of strawberries with the red stele (Phytophthora fragariae) resistance gene Rpf1 using sequence specific DNA markers. Acta Hortic. 760, 165–169 (2007).

    Article  CAS  Google Scholar 

  170. 170.

    Mathey, M. M. et al. Largescale standardized phenotyping of strawberry in RosBREED. J. Am. Pomol. Soc. 67, 205–216 (2013).

    Google Scholar 

  171. 171.

    Salinas, N., Fan, Z., Peres, N., Lee, S., Whitaker, V. M. FaRCa1 confers resistance to the root necrosis form of strawberry anthracnose caused by Colletotrichum acutatum. Hortic. Sci. (2020) (accepted).

  172. 172.

    Lerceteau-Köhler, E., Guérin, G. & Denoyes-Rothan, B. Identification of SCAR markers linked to Rca2 anthracnose resistance gene and their assessment in strawberry germplasm. Theor. Appl. Genet. 111, 862–870 (2005).

    Article  CAS  Google Scholar 

  173. 173.

    Chai, L. & Shen, Y.-Y. FaABI4 is involved in strawberry fruit ripening. Sci. Hortic. 210, 34–40 (2016).

    Article  CAS  Google Scholar 

  174. 174.

    Aharoni, A. et al. The strawberry FaMYB1 transcription factor suppresses anthocyanin and flavonol accumulation in transgenic tobacco. Plant J. 28, 319–332 (2001).

    Article  CAS  Google Scholar 

  175. 175.

    Kadomura-Ishikawa, Y., Miyawaki, K., Takahashi, A. & Noji, S. RNAi-mediated silencing and overexpression of the FaMYB1 gene and its effect on anthocyanin accumulation in strawberry fruit. Biol. Plant 59, 677–685 (2015).

    Article  CAS  Google Scholar 

  176. 176.

    Salvatierra, A., Pimentel, P., Moya-León, M. A. & Herrera, R. Increased accumulation of anthocyanins in Fragaria chiloensis fruits by transient suppression of FcMYB1 gene. Phytochemistry 90, 25–36 (2013).

    Article  CAS  Google Scholar 

  177. 177.

    Medina-Puche, L. et al. MYB10 plays a major role in the regulation of flavonoid/phenylpropanoid metabolism during ripening of Fragaria ×ananassa fruits. J. Exp. Bot. 65, 401–417 (2014).

    Article  CAS  Google Scholar 

  178. 178.

    Lin-Wang, K. et al. An R2R3 MYB transcription factor associated with regulation of the anthocyanin biosynthetic pathway in Rosaceae. BMC Plant Biol. 10, 50 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. 179.

    Kadomura-Ishikawa, Y., Miyawaki, K., Takahashi, A., Masuda, T. & Noji, S. Light and abscisic acid independently regulated FaMYB10 in Fragaria ×ananassa fruit. Planta 241, 953–965 (2015).

    Article  CAS  Google Scholar 

  180. 180.

    Schaart, J. G. et al. Identification and characterization of MYB-bHLH-WD40 regulatory complexes controlling proanthocyanidin biosynthesis in strawberry (Fragaria ×ananassa) fruits. N. Phytol. 197, 454–467 (2013).

    Article  CAS  Google Scholar 

  181. 181.

    Vallarino, J. G. et al. Central role of FaGAMYB in the transition of the strawberry receptacle from development to ripening. N. Phytol. 208, 482–496 (2015).

    Article  CAS  Google Scholar 

  182. 182.

    Pillet, J., Yu, H.-W., Chambers, A. H., Whitaker, V. M. & Folta, K. M. Identification of candidate flavonoid pathway genes using transcriptome correlation network analysis in ripe strawberry (Fragaria ×ananassa) fruits. J. Exp. Bot. 66, 4455–4467 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. 183.

    Daminato, M., Guzzo, F. & Casadoro, G. A SHATTERPROOF-like gene controls ripening in non-climacteric strawberries, and auxin and abscisic acid antagonistically affect its expression. J. Exp. Bot. 64, 3775–3786 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. 184.

    Lu, W. et al. One novel strawberry MADS-box transcription factor FaMADS1a acts as a negative regulator in fruit ripening. Sci. Hortic. 227, 124–131 (2018).

    Article  CAS  Google Scholar 

  185. 185.

    Seymour, G. B. et al. A SEPALLATA gene is involved in the development and ripening of strawberry (Fragaria ×ananassa Duch.) fruit, a non-climacteric tissue. J. Exp. Bot. 62, 1179–1188 (2011).

    Article  CAS  Google Scholar 

  186. 186.

    Molina-Hidalgo, F. J. et al. The fruit-specific transcription factor FaDOF2 regulates the production of eugenol in ripe fruit receptacles. J. Exp. Bot. 68, 4529–4543 (2017).

    Article  CAS  Google Scholar 

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The authors would like to express our sincere thanks to Michael Coe for providing valuable RosBREED survey data on the use of DNA information in strawberry breeding. We are also grateful for funding from USDA National Institute of Food and Agriculture Specialty Crop Research Initiative projects [2014-51181-22376, 2014-51181-22378]; USDA National Institute of Food and Agriculture National Research Support Project 10; NSF Plant Genome Research Program award #444573, NSF CIF21 Data infrastructure Building Blocks award #1443040; USDA Hatch project 1014919, California Strawberry Commission, Florida Strawberry Growers Association, Washington Tree Fruit Research Commission, and Washington State University.

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Whitaker, V.M., Knapp, S.J., Hardigan, M.A. et al. A roadmap for research in octoploid strawberry. Hortic Res 7, 33 (2020). https://doi.org/10.1038/s41438-020-0252-1

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