Recurrent genome recovery in backcross breeding programs of Passiflora edulis Sims based on SNP DArTseq sequencing
Palavras-chave:
diversity matrix technology, molecular markers, next-generation sequencing, passion fruit, plant breedingResumo
The backcross method in Passiflora edulis Sims is used for genetic improvement with the aim of transferring disease-resistance from wild to commercial species. Recurrent genome recovery and the characterization of hybrids and potential parents with desirable traits are essential for the design of intra and interspecific crosses. In the present study, recurrent genome recovery and genetic variability of three genealogies comprising hybrids and wild species of Passiflora were evaluated. Eighty-four genotypes were evaluated using 3.717 biallelic codominant SNP markers generated through DArTseq (NGS) sequencing. To characterize the structure of the genotype panel, the three genealogies were independently analyzed by calculating the genetic distance and by hierarchical clustering analysis (UPGMA) principal coordinate analysis (PCoA), and intersection analysis (UpSetR). The genetic availability of SNPs was evaluated using Polymorphism Information Content (PIC), one ratio proportion, Minor Allele Frequency (MAF), and reproducibility with the adegenet package (R software). The results identified well-defined similarity groups between hybrids and parents, with a clear trend of clustering of accessions of the same species and of backcrossed genotypes closer to the recurrent parent. The present study emphasizes the efficiency of backcrossing to recover recurrent genome and the preservation of genetic variability to improve the sour passion fruit.
Referências
1. Viana AP, Silva FHL, Gonçalves GM, Silva MGM, Ferreira RT, Pereira TNS, et al. UENF Rio Dourado: a new passion fruit cultivar with high yield potential. Crop Breed Appl Biotechnol. 2016;16(3):250-3. https://doi.org/10.1590/1984-70332016v16n3c38
2. Ferreira RT, Viana AP, Silva FHL, Santos EA, Santos JO. Seleção recorrente intrapopulacional em maracujazeiro-azedo via modelos mistos. Rev Bras Frutic. 2016;38(1):158-66. https://doi.org/10.1590/0100-2945-260/14
3. Faleiro FG, Junqueira NTV, Jesus ON, Costa AM, Machado CF, Junqueira KP, et al. Espécies de maracujazeiro no mercado internacional. In: Junghans TG, Jesus ON, editors. Maracujá: do cultivo à comercialização. Brasília, DF: Embrapa; 2017. p. 15-37. Available from: https://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1085000
4. Pereira M, Maciel GM, Haminiuk CWI, Bach F, Hamerski F, Scheer AP, et al. Effect of extraction process on composition, antioxidant and antibacterial activity of oil from Yellow Passion Fruit (Passiflora edulis var. flavicarpa) seeds. Waste Biomass Valoriz. 2019;10(9):2611-25. https://doi.org/10.1007/s12649-018-0269-y
5. Faleiro FG. Maracujás: cultivares, sistemas de produção e mercado. In: Faleiro FG, editor. Fruticultura tropical: capacitação e experiências de sucesso. Brasília, DF: Embrapa; 2025. p. 13-20. Available from: https://www.infoteca.cnptia.embrapa.br/infoteca/bitstream/doc/1174039/1/CPAC-2025-Fruticultura-tropical.pdf
6. Instituto Brasileiro de Geografia e Estatística (IBGE). Produção agropecuária: maracujá [Internet]. Rio de Janeiro: IBGE; c2025 [cited 2025 Sep 23]. Available from: https://www.ibge.gov.br/explica/producao-agropecuaria/maracuja/br
7. Meletti LMM. Avanços na cultura do maracujá no Brasil. Rev Bras Frutic. 2011;33(spe1):83-91. https://doi.org/10.1590/s0100-29452011000500012
8. Faleiro FG. Maracujá: fruta nativa do Brasil para o mundo. Anu HF[Internet]. 2022;79-81. Available from: https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1152428/1/Maracuja-fruta-nativa-2022.pdf
9. Faleiro FG, Junqueira NTV, Braga MF, Oliveira EJ, Peixoto JR, Costa AM. Germoplasma e melhoramento genético do maracujazeiro – histórico e perspectivas [Internet]. Planaltina, DF: Embrapa Cerrados; 2011. (Documentos, 307). Available from: https://www.infoteca.cnptia.embrapa.br/infoteca/bitstream/doc/942309/1/doc307.pdf
10. Faleiro FG, Junqueira NTV, Jesus ON, Junghans TG, Machado CF, Grattapaglia D, et al. Caracterização e uso de germoplasma e melhoramento genético do maracujazeiro (Passiflora spp.) assistidos por marcadores moleculares – Fase IV [Internet]. Planaltina, DF: Embrapa Cerrados; 2021. (Documentos, 376). Available from: https://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1139511
11. Empresa Brasileira de Pesquisa Agropecuária (Embrapa). Cultivares de maracujá da Embrapa [Internet]. Brasília, DF: Embrapa; c2024 [cited 2024 Jan 3]. Available from: https://www.embrapa.br/cultivar/maracuja
12. Faleiro FG, Junqueira NTV. Programa de melhoramento dos maracujás (Passiflora L.). In: Faleiro FG, Amabile RF, Rodrigues LN, editors. Pesquisa e inovação em germoplasma e melhoramento genético na Embrapa Cerrados. Brasília, DF: Embrapa; 2024. p. 39-44. Available from:https://www.infoteca.cnptia.embrapa.br/infoteca/bitstream/doc/1172316/1/CPAC-Livro-Germoplasma.pdf
13. Junqueira NTV, Braga MF, Faleiro FG, Peixoto JR, Bernacci LC. Potencial de espécies silvestres de maracujazeiro como fonte de resistência a doenças. In: Faleiro FG, Junqueira NTV, Braga MF, editors. Maracujá: germoplasma e melhoramento genético. Planaltina, DF: Embrapa Cerrados; 2005. p. 81-108.
14. Junqueira NTV, Santos EC, Junqueira KP, Faleiro FG, Bellon G, Braga MF. Physical and chemical characteristics and yield of Passiflora nitida Kunth accessions from North and Central regions of Brazil. Rev Bras Frutic. 2010;32(3):874-80. https://doi.org/10.1590/S0100-29452010005000102
15. Cavalcante NR, Viana AP, Almeida Filho JE, Pereira MG, Ambrósio M, Santos EA, et al. Novel selection strategy for half-sib families of sour passion fruit Passiflora edulis (Passifloraceae) under recurrent selection. Genet Mol Res. 2019;18(3):gmr18305. https://doi.org/10.4238/gmr18305
16. Faleiro FG, Junqueira NTV, Braga MF, Costa AM. Conservação e caracterização de espécies silvestres de maracujazeiro (Passiflora spp.) e utilização potencial no melhoramento genético, como porta-enxertos, alimentos funcionais, plantas ornamentais e medicinais – resultados de pesquisa [Internet]. Planaltina, DF: Embrapa Cerrados; 2012. (Documentos, 312). Available from: https://ainfo.cnptia.embrapa.br/digital/bitstream/item/92990/1/doc-312.pdf
17. Faleiro FG, Pires JL, Lopes UV. Uso de marcadores moleculares RAPD e microssatélites visando a confirmação da fecundação cruzada entre Theobroma cacao e Theobroma grandiflorum. Agrotrópica. 2003;15(1):41-6.
18. Junqueira KP, Faleiro FG, Junqueira NTV, Bellon G, Ramos JD, Braga MF, et al. Confirmação de híbridos interespecíficos artificiais no gênero Passiflora por meio de marcadores RAPD. Rev Bras Frutic. 2008;30(1):191-6. https://doi.org/10.1590/S0100-29452008000100035
19. Inglis PW, Pappas MdCR, Resende LV, Grattapaglia D. Fast and inexpensive protocols for consistent extraction of high quality DNA and RNA from challenging plant and fungal samples for high-throughput SNP genotyping and sequencing applications.
PLoS One. 2018;13(10):e0206085. https://doi.org/10.1371/journal.pone.0206085
20. DArT Pty Ltd. Diversity Arrays Technology [Internet]. Yarralumla: DArT Pty Ltd; c2024 [cited 2024 Jan 6]. Available from: https://www.diversityarrays.com/
21. Sansaloni CP, Petroli CD, Carling J, Hudson CJ, Steane DA, Myburg AA, et al. A high-density Diversity Arrays Technology (DArT) microarray for genome-wide genotyping in Eucalyptus. Plant Methods. 2010;6:16. https://doi.org/10.1186/1746-4811-6-16
22. Ma D, Dong SS, Zhang S, Wei X, Xie Q, Ding Q, et al. Chromosome-level reference genome assembly provides insights into aroma biosynthesis in passion fruit (Passiflora edulis). Mol Ecol Resour. 2021;21(3):955-68. https://doi.org/10.1111/1755-0998.13310
23. Kilian A, Wenzl P, Huttner E, Carling J, Xia L, Blois H, et al. Diversity Arrays Technology: a generic genome profiling technology on open platforms. In: Pompanon F, Bonin A, editors. Data production and analysis in population genomics: methods and protocols. New York: Springer; 2012. p. 67-89. (Methods in Molecular Biology, vol. 888). https://doi.org/10.1007/978-1-61779-870-2_5
24. R Core Team. R: a language and environment for statistical computing [Internet]. Vienna: R Foundation for Statistical Computing; 2021. Available from: https://www.R-project.org/
25.Sokal RR. A statistical method for evaluating systematic relationships. Univ Kans Sci Bull. 1958;38(2):1409-38. Available from: https://ia800509.us.archive.org/21/items/cbarchive_33927_astatisticalmethodforevaluatin1902/astatisticalmethodforevaluatin1902.pdf
26. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans R Soc A Math Phys Eng Sci. 2016;374(2065):20150202. https://doi.org/10.1098/rsta.2015.0202
27. Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: visualization of intersecting sets. IEEE Trans Vis Comput Graph. 2014;20(12):1983-92. https://doi.org/10.1109/TVCG.2014.2346248
28. Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33(18):2938-40. https://doi.org/10.1093/bioinformatics/btx364
29. Reis RV, Oliveira EJ, Viana AP, Pereira TNS, Pereira MG, Silva MGM. Diversidade genética em seleção recorrente de maracujazeiro-amarelo detectada por marcadores microssatélites. Pesqui Agropecu Bras. 2011;46(1):51-7.
30. Cavalcante NR, Viana AP, Almeida AM, Silva FHL. Effect of agronomic and molecular information on the genetic diversity of passion fruit. Rev Especialista. 2023;5:a10. https://doi.org/10.35418/2526-4117/v5a10
31. Silva ML, Nunes ES, Gomes RLF, Lopes ÂCA, Araújo ASF. Structure and molecular genetic diversity in natural populations and active germplasm banks of Passiflora cincinnata Mast. Chil J Agric Res. 2022;82(4):628-36. https://doi.org/10.4067/s0718-58392022000400628
32. Wu Y, Xu J, Han X, Qin X, Li L, Liu W, et al. Genetic diversity analysis and fingerprint construction for 87 passionfruit (Passiflora spp.) germplasm accessions on the basis of SSR fluorescence markers. Int J Mol Sci. 2024;25(19):10815. https://doi.org/10.3390/ijms251910815
33. Coronado RA, Jiménez VM, Mora-Newcomer E. Diversity and genetic structure of yellow passion fruit in Boyacá-Colombia using microsatellite DNA markers. Braz J Biol. 2024;84:e282426. https://doi.org/10.1590/1519-6984.282426
34. Araponga JS, Viana AP, Souza AM, Amaral Júnior AT, Pereira MG, Silva FHL. Genetic diversity and population structure of sour passion fruit in Brazil. J Genet Eng Biotechnol. 2025;23(1):100607. https://doi.org/10.1016/j.jgeb.2025.100607
35. Bezerra ARG, Silva ML, Araújo ASF. Genetic diversity of Passiflora cincinnata in the Chapada do Araripe, Northeast Brazil. Obs Econ Latinoam. 2024;22(6):e201. https://doi.org/10.55905/oelv22n6-201
36. Silveira FA, Souza MM, Oliveira EJ, Viana AP. Variabilidade e estrutura genética molecular em acessos de Passiflora edulis Sims. com base em marcadores análogos a genes de resistência. Biotemas. 2023;36(2):e91095. https://doi.org/10.5007/2175-7925.2023.e91095
37. Snekha V, Gowda DCS, Lakshmana D, Narayanaswamy P, Shankarappa TH, Nandeesha P. Evaluation of different passion fruit genotypes based on morphological, quantitative traits and molecular marker (ISSR). Plant Sci Today. 2024;11(sp4):5420. https://doi.org/10.14719/pst.5420
38. Bunjkar S, Sharma S, Kumar A. Unlocking genetic diversity and germplasm characterization with molecular markers: strategies for crop improvement. J Appl Biol Biotechnol. 2024;27(6):873-81. https://doi.org/10.9734/jabb/2024/v27i6873
39. Bidyananda M, Singh NS, Wani SH. Plant genetic diversity studies: insights from DNA marker analyses. Int J Plant Biol. 2024;15(3):46-60. https://doi.org/10.3390/ijpb15030046
40. Singh N, Choudhury DR, Singh AK, Kumar S, Srinivasan K, Tyagi RK, et al. Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties. PLoS One. 2013;8(12):e84136. https://doi.org/10.1371/journal.pone.0084136
41. Olagunju YO, Olawuyi OJ. Diversity assessment with SNP, SSR, AFLP, and RAPD markers in plants: a systematic review and meta-analysis. bioRxiv [Preprint]. 2026 [cited 2026 Aug 12]:[15 p.]. https://doi.org/10.64898/2026.07.03.736291
42. Anokye M, Tetteh JP, Oteng-Frimpong R. The role of single nucleotide polymorphisms (SNPs) in modern plant breeding: from discovery to application. Preprints [Preprint]. 2025 [cited 2026 Aug 12]:[18 p.]. https://doi.org/10.20944/preprints202504.1646.v1
43. Pootakham W. Genotyping by sequencing (GBS) for genome-wide SNP identification in plants. In: Shavrukov Y, editor. Methods in molecular biology [Internet]. New York: Springer; 2023. p. 1-15. https://doi.org/10.1007/978-1-0716-3024-2_1
44. Pereira GS, Nunes ES, Laperuta LDC. The passion fruit genome. In: Chapman MA, editor. Compendium of plant genomes [Internet]. New York: Springer; 2022. p. 8–22. https://doi.org/10.1007/978-3-031-00848-1_8
45. Castillo NRF, Bassil N, Waddell C, Peever T, Salazar D. Genetic diversity of purple passion fruit, Passiflora edulis f. edulis, based on single-nucleotide polymorphism markers discovered through genotyping by sequencing. Diversity. 2021;13(4):144. https://doi.org/10.3390/d13040144
46. Grossi MC, Guimarães LMS, Viana AP, Oliveira EJ, Lopes R. DArTseq-derived SNPs for the genus Psidium reveal the high diversity of native species. Tree Genet Genomes. 2021;17(3):23. https://doi.org/10.1007/s11295-021-01505-y
47. Fachi LR, Pinto DLP, Rosado LDS, Neves LG, Bruckner CH, Barelli MAA. Strategies for the next cycles of intrapopulation improvement of sour passion fruit. Rev Bras Eng Agric Ambient. 2023;27(3):167-72. https://doi.org/10.1590/1807-1929/agriambi.v27n3p167-172
48. Rosado RDS, Rosado TB, Cruz CD, Ferreira MFS, Ferrão RG. Parental selection based on molecular information under a population genetics approach. Agron Sci Biotechnol. 2021;7:e131. https://doi.org/10.33158/asb.r131.v7.2021
49. Khoury CK, Brush S, Costich DE, Curry HA, de Haan S, Engels JMM, et al. Crop genetic erosion: understanding and responding to loss of crop diversity. New Phytol. 2022;233(1):84-118. https://doi.org/10.1111/nph.17733
50. Adams RH, Schield DR, Castoe TA. Recent advances in the inference of gene flow from population genomic data. Curr Mol Biol Rep. 2019;5(2):107-15. https://doi.org/10.1007/s40610-019-00120-0
51. Enggarini W, Sudjahjo SH, Trikoesoemaningtyas T, Sujiprihati S, Widyastuti U, Trijatmiko KR, et al. Characterization of donor genome segments of BC2 and BC4 Way Rarem × Oryzica Llanos-5 progenies detected by SNP markers. J AgroBiogen. 2012;8(1):1-7. https://doi.org/10.21082/jbio.v8n1.2012.p1-7
52. Li C, Ohadi S, Mesgaran MB. Asymmetry in fitness-related traits of later-generation hybrids between two invasive species. Am J Bot. 2021;108(1):51-62. https://doi.org/10.1002/ajb2.1583
53.Vašut RJ, Pospíšková M, Lukavský J, Weger J. Detection of hybrids in willows (Salix, Salicaceae) using genome-wide DArTseq markers. Plants. 2024;13(5):639. https://doi.org/10.3390/plants13050639
54. Bocianowski J, Tomkowiak A, Bocianowska M, Sobiech A. The use of DArTseq technology to identify markers related to the heterosis effects in selected traits in maize. Curr Issues Mol Biol. 2023;45(4):2644-60. https://doi.org/10.3390/cimb45040173
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