1 Introduction

As one of the major tropical fruits, pineapple (Ananas comosus) has attracted much attention due to its economic value and rich genetic composition, which play a key role in variety improvement and adaptation (Hossain, 2016; Cheng et al., 2018; Chaudhary et al., 2019; Ali et al., 2020). Genetic diversity in pineapple germplasm resources is important for breeding programs to improve yield, fruit quality and stress resistance. Genetic diversity is mainly attributed to the heterozygous characteristics of pineapple and the presence of several varieties such as Perola, Queen, and Abacaxi, each of which exhibits its own unique and potential traits (Duval et al., 2001; Zhao and Qin, 2018). Studies by Paz et al. (2012), Makaranga et al. (2018) and Zhao and Qin (2018) show that the application of molecular markers such as SSR, SNP and AFLP is conducive to the assessment and understanding of genetic diversity. It provides important information for genetic structure analysis and character improvement.

The utilization of pineapple germplasm is still facing challenges. The low genetic diversity of pineapples observed by researchers in areas such as Tanzania and Cuba limits the improvement and breeding of local varieties (Paz et al., 2005; Paz et al., 2012; Makaranga et al., 2018). The complex reproductive biology of pineapple, such as open pollination uncertainty and somatic cell variation, increases the complexity of the breeding process and hinders the development of new varieties (Reinhardt et al., 2018; Wang and Paull, 2018; Chen et al., 2019; Nureszuan et al., 2021; Jia et al., 2024). Studies by Makaranga et al. (2018) and Jia et al. (2024) suggest that more advanced breeding strategies must be introduced to effectively exploit and utilize the genetic potential of pineapple germplasm.

This study will evaluate the genetic diversity and character discovery of pineapple germplasm through meta-analysis, and propose strategies to overcome the bottleneck of germplasm utilization by integrating data from multiple studies. This study hopes to provide theoretical support for cultivating more resistant and higher quality pineapple varieties, and then promote the development and productivity of pineapple cultivation.

2 Genetic Diversity Studies in Pineapple: Literature Landscape

2.1 Common molecular techniques

Various molecular techniques have been widely used in the study of pineapple genetic diversity to assess and characterize genetic variation between and within different genotypes. Commonly used molecular markers include RAPD, RFLP, AFLP, SSR, and SNP (Duval et al., 2001; Kato et al., 2005; Paz et al., 2012; Zhou et al., 2015; Zhao and Qin, 2018). SSR markers are widely used in genetic diversity assessment due to their high polymorphism and good repeatability (Wang et al., 2017; Ismail et al., 2020; Nashima et al., 2020). AFLP markers have been used to reveal genetic relationships and diversity in specific germplasm resources (Carlier et al., 2010; Paz et al., 2012; Sheeja et al., 2021).

2.2 Traits commonly studied

In pineapple genetic diversity studies, researchers often focus on traits related to breeding potential, such as yield, fruit size, fruit quality, and productivity (Zhao and Qin, 2018; Adje et al., 2019; Junior et al., 2021; Chen et al., 2024a; Chen et al., 2024b). Researchers also focused on specific traits, such as leaf margin phenotype and flesh color, and mined their contributing genes and QTLS by genome sequencing (Figure 1) (Nashima et al., 2022). The study of Sinaga and Marpaung (2024) showed that the study of stress resistance traits is also the focus, and the study of stress resistance traits is conducive to cultivating disease-resistant and stress-resistant pineapple varieties.

Figure 1 Phenotype of leaf margin and flesh color in pineapples (Adopted from Nashima et al., 2022) Image caption: (a) Pipe-type leaf margin phenotype. (b) Spiny-type leaf margin phenotype. (c) White flesh color phenotype. (d) Yellow flesh color phenotype (Adopted from Nashima et al., 2022)

2.3 Need for meta-analytic consolidation

It is necessary to conduct a meta-analysis in the field of pineapple genetic diversity. Meta-analyses integrate different studies to provide an understanding of genetic diversity patterns and trait associations for different germplasm resources. Junior et al. (2021) demonstrated that the integration of meta-analyses helps to identify consistent genetic markers and traits that can be used in breeding programs to improve pineapple varieties. Meta-analysis is great for standardizing methods and results, which makes it easier to compare results across studies and regions.

3 Meta-Analytic Insights on Genetic Diversity

3.1 Global diversity patterns

Pineapple germplasm showed significant genetic diversity among different regions and varieties, and SSR, AFLP and ISSR molecular markers were used to reveal rich genetic variation. According to the study of Ismail et al. (2020), the SSR study in Malaysia showed moderate polymorphism, with an average of 3.9 alleles detected at each locus and an average PIC value of 0.433, indicating that its genetic diversity was at a medium level. Paz et al. (2012) showed that the genetic diversity of Cuban germplasm was low through AFLP analysis, and most of the materials were clustered at genetic distance less than 0.20, indicating a limited range of variation. Hayati and Kasiamdari (2024) showed that Indonesian varieties showed high genetic diversity with 89.38% polymorphisms detected by ISSR markers.

3.2 Population structure across studies

Studies in different regions revealed the differences in population structure of pineapple germplasm resources. Ismail et al. 's study in 2020 showed that the population structure analysis in Malaysia used the delta K method to identify two major genetic clusters, and the findings were supported by UPGMA systematic cluster maps. Rattanathawornkiti et al. ’s study in 2016 showed that AFLP studies in Thailand identified 9 independent genetic populations in 37 materials, which were closely related to morphological characteristics of breeds (such as Cayenne and Queen taxa). The population structure of pineapple is complex, which can be influenced by both genetic and environmental factors.

3.3 Variation among germplasm types

Variability between different types of pineapple germplasm has been demonstrated in multiple studies. According to Zhao and Qin (2018), the genetic diversity of pineapple is driven by cross-pollination and somatic variation, resulting in wide differences in plant morphology and fruit traits among different varieties. Zhou et al. (2015) reported that the application of SNP markers revealed high genetic redundancy in the germbank, and somatic mutations were considered to be the main source of intraspecific variation. Continuous intraspecific variability in wild species such as Ananas ananassoides and Ananas parguazensis is an important contributor to overall genetic diversity (Duval et al., 2001).

4 Trait Discovery through Meta-Analysis

4.1 Fruit quality traits

The fruit quality traits of pineapple are critical for fresh food and processing purposes. Several studies have highlighted that genetic diversity in germplasm resources is beneficial for improving fruit quality traits (such as size, sweetness, and flesh color). Ismail et al. (2020) used SSR markers to study Malaysian pineapple materials and found that they had moderate polymorphism, which could be used to improve fruit sweetness and taste traits. Genes associated with flesh color, such as carotenoid cleaved dioxygenase 4 (AcCCD4), identified in Nashima et al. (2022) provide the genetic basis for the development of high-quality fruit color varieties through marker-assisted selection (Figure 2). Zhou et al. 's study in 2015 showed that the application of SNP markers revealed significant variability within varieties, providing rich selection resources for the improvement of fruit quality traits.

Figure 2 Carotenoid accumulation and AcCCD4 expression during fruit ripening in ‘Yugafu’, (Yu) and ‘Yonekura’ (Yo) (Adopted from Nashima et al., 2022) Image caption: (a) Flesh appearance. (b) Carotenoid content. (c) AcCCD4 relative gene expression. Three biological replicates for each sample were examined to determine carotenoid quantities and conduct gene expression analysis. Error bars indicate SE. VIO, violaxanthin; cis-VIO, 9-cis-violaxanthin; LUT, lutein; ZEA, zeaxanthin; BCR, β-cryptoxanthin; ACA, α-carotene; BCA, β-carotene (Adopted from Nashima et al., 2022)

4.2 Stress tolerance and resistance

Stress and pest resistance traits help ensure sustainable cultivation of pineapples in a varied environment. Studies on genetic diversity of AFLP and ISSR markers suggest that genetic variation in pineapple germplasm can be used to breed cultivars with high resistance (Paz et al., 2012; Wang et al., 2017). Studies of pineapple germplasm from Cuba and Indonesia have revealed material resources with great potential to resist biological and abiotic stresses (Paz et al., 2012; Hayati and Kasiamdari, 2024). The identification of genetic populations with specific stress resistance traits provides a scientific basis for parental selection (Rattanathawornkiti et al., 2016).

4.3 Flowering and growth traits

Flowering and growth characteristics are the key points to optimize the production cycle and increase the yield of pineapple. Some studies have found that there are genetic differences in flowering and growth traits among different materials. The genetic analysis of half-sib lines by Junior et al. (2021) showed that traits such as fruit quality and soluble solid content provided scientific basis for breeding superior parents. The use of molecular markers such as RFLP and SNP can help to identify key genetic variants that affect flowering time and plant structure, and promote growth traits (Duval et al., 2001; Zhou et al., 2015). The study of Wang et al. (2017) showed that genetic cluster analysis of materials will be conducive to analyzing the genetic basis of flowering and growth traits and formulating targeted breeding strategies.

5 Case Study: Meta-Analysis of Brix-Related QTLs

5.1 Data aggregation process

The meta-analysis summarized QTL data related to Brix (soluble solid content) by integrating multiple studies using different molecular markers and genetic analysis methods. Studies included in the analysis included studies using SSR, AFLP, and SNP markers (Paz et al., 2012; Zhou et al., 2015; Wang et al., 2017; Zhao and Qin, 2018; Ismail et al., 2020). The subjects of the study were germplasm resources from Malaysia, Cuba, Indonesia and other regions, which ensured the diversity and representativeness of the data (Hayati and Kasiamdari, 2024).

5.2 Findings and statistical interpretation

The results of the meta-analysis revealed genetic differences in Brix-related traits in different materials. SSR and SNP markers performed well in identifying polymorphic sites related to Brix content (Zhou et al., 2015; Wang et al., 2017; Zhao and Qin, 2018). Multiple QTLS were consistently associated with high Brix levels and could be considered as breeding target sites. Statistical analysis showed that the genetic variation of Brix traits was influenced by both intraspecific and interspecific diversity, and the increase of Brix level in some materials was closely related to the existence of specific alleles (Duval et al., 2001; Junior et al., 2021; Nashima et al., 2022). Rattanathawornkiti et al. (2016) and Wang et al. (2017) successfully identified several genetic populations associated with superior Brix traits through principal component analysis (PCA) and cluster analysis.

5.3 Implications for breeding programs

The results of the meta-analysis provided scientific basis for pineapple breeding. The identification of specific QTLS associated with high Brix values can provide theoretical basis for marker-assisted selection and help breeders develop new varieties with sweeter and better quality (Zhou et al., 2015; Junior et al., 2021; Nashima et al., 2022). The genetic diversity revealed in the study indicates that researchers can use heterosis to breed new varieties with excellent Brix characteristics (Wang et al., 2017; Ismail et al., 2020; Hayati and Kasiamdari, 2024). The incorporation of molecular markers into the breeding process will significantly improve the efficiency of parental screening and accelerate the breeding of high-quality and high-yield pineapple varieties (Duval et al., 2001; Rattanathawornkiti et al., 2016).

6 Applications in Breeding and Genomic Innovation

6.1 Marker-assisted and genomic selection

Marker-assisted selection and genomic selection play a key role in modern pineapple breeding programs. The use of SSR, AFLP, SNP and other molecular markers is very helpful for assessing genetic diversity and identifying desirable traits in germplasm. Studies have shown that SSR markers are more efficient than ISSR markers in assessing genetic diversity, which is conducive to researchers' selection of parents with excellent traits (Wang et al., 2017; Ismail et al., 2020). SNP markers can provide stable and accurate DNA fingerprints, which are helpful for genotype identification and germplasm resource management, and can accelerate the screening and breeding of good genotypes (Zhou et al., 2015).

6.2 Genomic prediction models informed by meta-data

The wealth of genetic information is used by genomic prediction models to predict the performance of undetermined genotypes. By integrating metadata from different studies into predictive models, researchers can significantly improve their accuracy. Nashima et al. ’s study in 2022, showed that haplotype-based genome sequencing has enabled the localization of genes associated with important traits such as leaf margin morphology and pulp color. Studies by Zhou et al. (2015) and Nashima et al. (2022) demonstrate that combining genomic information with phenotypic data can help build models that predict the performance of new hybrid combinations, speeding up the breeding cycle and improving selection efficiency.

6.3 Bridging research and practical use

Translating genetic research results into practical breeding strategies will help promote the development of the pineapple industry. A number of studies have revealed that genetic identification of pineapple germplasm materials using AFLP and ISSR markers will help reveal the genetic relationship and population structure, and provide scientific basis for hybrid selection and breeding program design (Paz et al., 2012; Rattanathawornkiti et al., 2016; Wang et al., 2017). The application of genomic tools in breeding programs can help select parents with high heterosis potential and accelerate the development of quality varieties (Junior et al., 2021).

7 Limitations and Future Directions

7.1 Methodological constraints

SSR and ISSR markers show moderate to high effectiveness in polymorphism detection, but the efficiency of SSR and ISSR markers is different, and some studies have shown that SSR markers are more advantageous in genetic diversity assessment (Wang et al., 2017; Ismail et al., 2020; Hayati and Kasiamdari, 2024). AFLP markers show low diversity in some germplasm populations (Gerber et al., 2000). Existing studies mainly focus on some specific geographical regions or germplasm banks, which cannot fully represent the global genetic diversity of pineapple (Paz et al., 2012), it is difficult for researchers to fully reveal the genetic relationships and diversity patterns among germplasm (Wang et al., 2017).

7.2 Uncovered traits and geographic gaps

Scientists have now identified genes associated with leaf margin morphology and pulp color (Nashima et al., 2022), but further research is needed on other traits such as yield, fruit quality and productivity (Zhao and Qin, 2018; Junior et al., 2021). In terms of geographical representation, current genetic diversity studies mainly focus on Malaysia, Cuba, Indonesia and Thailand (Paz et al., 2012; Rattanathawornkiti et al., 2016; Ismail et al., 2020; Hayati and Kasiamdari, 2024), while other important pineapple production areas have not been adequately sampled, geographic gaps may cause breeders to bias their perceptions of global genetic diversity, limiting the effectiveness of breeding programs.

7.3 Multi-omics integration in future meta-analyses

Future meta-analysis studies should focus more on the integration of multi-omics data to analyze the germplasm resources of pineapple more comprehensively. Integrating genomic, transcriptome, proteome, and metabolome data is helpful for identifying key genes and their pathways that regulate important traits (Zhao and Qin, 2018; Nashima et al., 2022). The use of SNP markers can provide robust and comparable DNA fingerprints for the identification and management of global germplasm resources. Through the multi-omics integration method, researchers can break through the limitation of single marker study, fully grasp the genetic diversity and character framework of pineapple, and promote the management of germplasm resources, the formulation of breeding strategies and the protection of varieties (Zhou et al., 2015; Wang et al., 2017).

Acknowledgments

The authors appreciate the comments from Mr. Rudi Mai and Mr. Qixue Liang on the manuscript of this study.

Funding

This study was supported by the Research and Training Fund of the Hainan Institute of Tropical Agricultural Resources (Project No. H2025-02).

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Adje C.A.O., Achigan-Dako E.G., d'Eeckenbrugge G.C., Yedomonhan H., and Agbangla C., 2019, Morphological characterization of pineapple (Ananas comosus) genetic resources from Benin, Fruits, 74(4): 167-179.

https://doi.org/10.17660/th2019/74.4.2

Ali M., Hashim N., Aziz A., and Lasekan O., 2020, Pineapple (Ananas comosus): A comprehensive review of nutritional values, volatile compounds, health benefits, and potential food products, Food Research International, 137: 109675.

https://doi.org/10.1016/J.FOODRES.2020.109675

Carlier J., Sousa N., Santo T., d'Eeckenbrugge G., and Leitão J., 2010, A genetic map of pineapple (Ananas comosus (L.) Merr.) including SCAR, CAPS, SSR and EST-SSR markers, Molecular Breeding, 29: 245-260.

https://doi.org/10.1007/s11032-010-9543-9

Chaudhary V., Kumar V., Singh K., Kumar R., Kumar V., and Chaudhary C.V., 2019, Pineapple (Ananas comosus) product processing: a review, Journal of Pharmacognosy and Phytochemistry, 8: 4642-4652.

Chen B., Hou J.F., Cai Y.F., Wang G.Y., Cai R.X., and Zhao F.C., 2024a, Utilizing genetic diversity for maize improvement: strategies and success stories, Maize Genomics and Genetics, 15(3): 136-146.

https://doi.org/10.5376/mgg.2024.15.0014

Chen B., Hou J.F., Cai Y.F., Wang G.Y., Cai R.X., and Zhao F.C., 2024b, Genetic diversity in the genus zea: insights from chloroplast genome variability, Maize Genomics and Genetics, 15(5): 228-238.

Chen H., Hu B., Zhao L., Shi D., She Z., Huang X., Priyadarshani S., Niu X., and Qin Y., 2019, Differential expression analysis of reference genes in pineapple (Ananas comosus L.) during reproductive development and response to abiotic stress, Hormonal Stimuli, Tropical Plant Biology, 12: 67-77.

https://doi.org/10.1007/s12042-019-09218-2

Cheng Y., Bartholomew D., and Qin Y., 2018, Biology of the pineapple plant, In: Ming R. (eds.), Genetics and genomics of pineapple, plant genetics and genomics: crops and models, Springer, Cham, Switzerland, pp.27-40.

https://doi.org/10.1007/978-3-030-00614-3_2

Duval M., Noyer J., Perrier X., d’Eeckenbrugge G., and Hamon P., 2001, Molecular diversity in pineapple assessed by RFLP markers, Theoretical and Applied Genetics, 102: 83-90.

https://doi.org/10.1007/s001220051621

Gerber S., Mariette S., Streiff R., Bodénès C., and Kremer A., 2000, Comparison of microsatellites and amplified fragment length polymorphism markers for parentage analysis, Molecular Ecology, 9(8): 1037-1048.

https://doi.org/10.1046/j.1365-294x.2000.00961.x

Hayati R., and Kasiamdari R., 2024, Genetic diversity of Indonesian pineapple (Ananas comosus (L.) Merr.) cultivars based on ISSR markers, Pertanika Journal of Tropical Agricultural Science, 47(4): 1087-1100.

https://doi.org/10.47836/pjtas.47.4.02

Hossain M.F., 2016, World pineapple production: an overview, African Journal of Food, Agriculture, Nutrition and Development, 16(4): 11443-11456.

https://doi.org/10.18697/ajfand.76.15620

Ismail S., Ghani N., Razak S., Abidin R., Yusof M., Zubir M., and Zainol R., 2020, Genetic diversity of pineapple (Ananas comosus) germplasm in Malaysia using simple sequence repeat (SSR) markers, Tropical Life Sciences Research, 31: 15-27.

https://doi.org/10.21315/tlsr2020.31.3.2

Jia P., Liu S., Lin W., Yu H., Zhang X., Xiao X., Sun W., Lu X., and Wu Q., 2024, Authenticity identification of F₁ hybrid offspring and analysis of genetic diversity in pineapple, Agronomy, 14(7): 1490.

https://doi.org/10.3390/agronomy14071490

Júnior J., Bezerra J., Canuto V., and Santos D., 2021, Variation among pineapple half-sibs and selecting genitors based on potential genetic divergence, World Journal of Advanced Research and Reviews, 10(3): 289-301.

https://doi.org/10.30574/wjarr.2021.10.3.0278

Kato C., Nagai C., Moore P., Zee F., Kim M., Steiger D., and Ming R., 2005, Intra-specific DNA polymorphism in pineapple (Ananas comosus (L.) Merr.) assessed by AFLP markers, Genetic Resources and Crop Evolution, 51: 815-825.

https://doi.org/10.1007/s10722-005-0005-x

Makaranga A., Seth M., Ndee A., Mneney E., Mbwambo G., Lema K., Godfrey A., Mrema L., Kachiwile A., Mrema E., and Msogoya T., 2018, Diversity and genetic identity of pineapple [Ananas comosus (L.) Merr.] in Tanzania based on microsatellite markers, African Journal of Biotechnology, 17: 811-817.

https://doi.org/10.5897/AJB2018.16498

Nashima K., Hosaka F., Terakami S., Kunihisa M., Nishitani C., Moromizato C., Takeuchi M., Shoda M., Tarora K., Urasaki N., and Yamamoto T., 2020, SSR markers developed using next-generation sequencing technology in pineapple, Ananas comosus (L.) Merr., Breeding Science, 70: 415-421.

https://doi.org/10.1270/jsbbs.19158

Nashima K., Shirasawa K., Isobe S., Urasaki N., Tarora K., Irei A., Shoda M., Takeuchi M., Omine Y., Nishiba Y., Sugawara T., Kunihisa M., Nishitani C., and Yamamoto T., 2022, Gene prediction for leaf margin phenotype and fruit flesh color in pineapple (Ananas comosus) using haplotype-resolved genome sequencing, The Plant Journal, 110(3): 720-734.

https://doi.org/10.1111/tpj.15699

Nureszuan S., Noorasmah S., and Shiamala D., 2021, Flowering synchronization in pineapples (Ananas comosus L. Merr): a review, Journal of Applied Horticulture, 23(2): 206-211.

https://doi.org/10.37855/jah.2020.v22i03.50

Paz E., Gil K., Rebolledo L., Rebolledo A., Uriza D., Martínez O., Diaz L., Lorenzo J., and Simpson J., 2012, Genetic diversity of Cuban pineapple germplasm assessed by AFLP markers, Crop Breeding and Applied Biotechnology, 12: 104-110.

https://doi.org/10.1590/S1984-70332012000200002

Paz E., Gil K., Rebolledo L., Rebolledo A., Uriza D., Martínez O., Isidrón M., and Simpson J., 2005, AFLP characterization of the Mexican pineapple germplasm collection, Journal of the American Society for Horticultural Science, 130: 575-579.

https://doi.org/10.21273/JASHS.130.4.575

Rattanathawornkiti K., Kanchanaketu T., Suwanagul A., and Hongtrakul V., 2016, Genetic relationship assessment of pineapple germplasm in Thailand revealed by AFLP markers, Genomics and Genetics, 9: 56-65.

Reinhardt D., Bartholomew D., Souza F., Carvalho A., Padua T., Junghans D., and Matos A., 2018, Advances in pineapple plant propagation, Revista Brasileira de Fruticultura, 40: e-302.

https://doi.org/10.1590/0100-29452018302

Sheeja T.E., Kumar I.P.V., Giridhari A., Minoo D., Rajesh M.K., and Babu K.N., 2021, Amplified fragment length polymorphism: applications and recent developments, In: Besse P. (eds.), Molecular plant taxonomy, methods in molecular biology, Humana, New York, USA, pp.187-218.

https://doi.org/10.1007/978-1-0716-0997-2_12

Sinaga A., and Marpaung D., 2024, Abiotic stress-induced gene expression in pineapple as a potential genetic marker, Advanced Agrochem, 3(2): 133-142.

https://doi.org/10.1016/j.aac.2024.01.005

Wang M., and Paull R., 2018, Genetic transformation of pineapple, In: Ming R. (eds.), Genetics and genomics of pineapple, plant genetics and genomics: crops and models, Springer, Cham, Switzerland, pp.69-86.

https://doi.org/10.1007/978-3-030-00614-3_6

Wang J.S., He J.H., Chen H.R., Chen Y. Y.,  and Qiao F., 2017, Genetic diversity in various accessions of pineapple [Ananas comosus (L.) Merr.] using ISSR and SSR markers, Biochemical genetics, 55: 347-366.

https://doi.org/10.1007/s10528-017-9803-z

Zhao H., and Qin Y., 2018, Genetic diversity of pineapple, In: Ming R. (eds.), Genetics and genomics of pineapple, plant genetics and genomics: crops and models, Springer, Cham, Switzerland, pp.51-59.

https://doi.org/10.1007/978-3-030-00614-3_4

Zhou L., Matsumoto T., Tan H., Meinhardt L., Mischke S., Wang B., and Zhang D., 2015, Developing single nucleotide polymorphism markers for the identification of pineapple (Ananas comosus) germplasm, Horticulture Research, 2: 15056.

https://doi.org/10.1038/hortres.2015.56