, Volume 208, Issue 3, pp 609–619 | Cite as

Generating relevant information for breeding Passiflora edulis: genetic parameters and population structure

  • Fernando H. L. Silva
  • Patricio R. MuñozEmail author
  • Christopher I. Vincent
  • Alexandre Pio Viana


Passion fruit is an economically important tropical fruit crop with unrealized genetic potential. This study aimed to provide breeders with essential estimates of genetic parameters and of the structure of a typical breeding population. To achieve this, eighty-one progenies derived from the third cycle of recurrent selection were assessed for eight fruit yield and quality traits. First, we evaluated the efficiency of the post hoc implementation of a Row–Col design for data analysis instead of the original randomized complete block design (RCBD). Next, we applied a restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) mixed model to estimate genetic parameters: variance components, heritability, and genetic correlations. The population genetic structure was evaluated using 10 simple sequence repeat (SSR) markers. Results indicate that the post hoc blocking fit the data significantly better or equally to the RCBD. Soluble solids content showed the highest heritability value (0.53 ± 0.087) while yield presented the lowest estimate (0.24 ± 0.090). The 10 SSR loci amplified a total of 29 alleles revealing that the progenies evaluated could be divided into three groups. This grouping information can be used to direct future crosses of this population to maximize heterosis for the traits of interest. Correlations among variables indicate that the number of fruit can be used as a proxy for yield. Future population evaluation studies should consider incomplete block designs to maximize the land and labor resources.


Passion fruit Passiflora Mixed models REML/BLUP Genetic correlation Post hoc blocking design 



The authors are thankful to the Foundation for Research Support of the State of Rio de Janeiro (FAPERJ) and National Council for Scientific and Technological Development (CNPq), for the financial support to the experiment, and the Coordination of Improvement of Higher Education Personnel (CAPES), for the granting of a doctoral scholarship for the first author.


  1. Bernardo R (2002) Genotype × environment interaction. In: Bernardo R (ed) Breeding for quantitative traits in plants. Stemma Press, Woodbury, pp 147–171Google Scholar
  2. Bhateria S, Sood SP, Pathania A (2006) Genetic analysis of quantitative traits across environments in Linseed (Linum ustitatissimum L). Euphytica 150(1–2):185–194CrossRefGoogle Scholar
  3. Chen Y, Lübberstedt T (2010) Molecular basis of trait correlations. Trends Plant Sci 15:454–461CrossRefPubMedGoogle Scholar
  4. Choudhary P, Khanna SM, Jain PK, Bharadwaj C, Kumar J, Lakhera PC, Srinivasan S (2012) Genetic structure and diversity analysis of the primary gene pool of chickpea using SSR markers. Genet Mol Res 11(2):891–905CrossRefPubMedGoogle Scholar
  5. Costa e Silva J, Dutkowski GW, Gilmour AR (2001) Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual. Can J For Res 31:1887–1893CrossRefGoogle Scholar
  6. de Freitas JPX, Oliveira EJ, Jesus ON, da Cruz Neto AJ, Santos LR (2012) Development of a base population for recurrent selection in yellow passion fruit using selection indexes. Pesq Agrop Bras 47:393–401CrossRefGoogle Scholar
  7. de Moraes MC, Geraldi IO, Matta FP, Vieira MLC (2005) Genetic and phenotypic parameter estimates for yield and fruit quality traits from a single wide cross in yellow passion fruit. HortScience 40(7):1978–1981Google Scholar
  8. de Oliveira EJ, Santos VS, Lima DS, Machado ML, Lucena RS, Motta TBN (2011) Genotypic and phenotypic correlation estimates from passion fruit germplasm. Bragantia 70(2):255–261Google Scholar
  9. de Silva FHL, Viana AP, Ferreira RT, de Freitas JCO, Santos JO, Rodrigues DL (2014) Measurement of genetic diversity in progenies of sour passion fruit by Ward-MLM methodology: a strategy for heterotic group formation. Revista Ciência e Agrotecnologia 38:1234–1239Google Scholar
  10. Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15Google Scholar
  11. EMBRAPA (2006) Empresa Brasileira de Pesquisa Agropecuária. Centro Nacional de Pesquisa do Solo. Sistema brasileiro de classificação de solos. 2 ed Rio de JaneiroGoogle Scholar
  12. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14(8):2611–2620CrossRefPubMedGoogle Scholar
  13. Falconer DS (1989) Introduction to quantitative genetics, 3rd edn. Longmans Green/Wiley, Harlow, Essex, UK/New YorkGoogle Scholar
  14. Faleiro FGF, Junqueira NTV, Braga MF (2005) Germoplasma e melhoramento genético do maracujazeiro: Desafios da pesquisa. In: Faleiro FGF, Junqueira NTV, Braga MF (eds) Maracujá: germoplasma e melhoramento genético. Embrapa Cerrados, Planaltina, pp 55–78Google Scholar
  15. Faleiro FGF, Farias Neto AL, Ribeiro WQ (2008) Pré-melhoramento, melhoramento e pós-melhoramento: estratégias e desafios. Embrapa Cerrados 1 ed PlanaltinaGoogle Scholar
  16. Filippi CV, Aguirre N, Rivas JG, Zubrzycki J, Puebla A, Cordes D, Moreno MV, Fusari CM, Alvarez D, Heinz RA, Hopp HE, Paniego NB, Lia VV (2015) Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers. BMC Plant Biol 15(52):1–12Google Scholar
  17. Freitas ILDJ, Amaral Junior AT, Viana AP, Pena GF, Cabral PS, Vittorazzi C, Silva TRDC (2013) Genetic gain evaluated with selection indices and with REML/Blup in popcorn. Pesq Agrop Bras 48(11):1464–1471CrossRefGoogle Scholar
  18. Gezan SA (2005) Optimal design and analysis of clonal forestry trials using simulated data. PhD Thesis, University of FloridaGoogle Scholar
  19. Gezan SA, Huber DA, White TL (2006) Post hoc blocking to improve heritability and precision of best linear unbiased genetic predictions. Can J For Res 36:2141–2147CrossRefGoogle Scholar
  20. Gilmour AR, Gogel BJ, Cullis BR, Welham SJ, Thompson R (2002) ASReml User Guide Release 1.0 VSN International Ltd., Hemel HempsteadGoogle Scholar
  21. Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml user Guide release 3.0. VSN International Ltd., Hemel HempsteadGoogle Scholar
  22. Gonçalves GM, Viana AP, Pereira MG, Bezerra Neto FV, Amaral AT, Pereira TNS (2008) Phenotypic and genetic-additive correlations in yellow passion fruit obtained by design I. Ciência e Agrotecnologia 32:1413–1418CrossRefGoogle Scholar
  23. Gonçalves GM, Viana AP, Pereira MG, Bezerra Neto FV, do Amaral Júnior AT, Pereira TNS, Gonçalves TJM (2009) Genetic parameter estimates in yellow passion fruit based on design I. Braz Arch Biol Tech 52:523–530CrossRefGoogle Scholar
  24. Griffing B (1956) Concept of general and specific combining ability in relation to diallel crossing system. Aust J Biol Sci 9:463–493Google Scholar
  25. IBGE (2014) Instituto Brasileiro de Geografia e Estatística. Banco de dados agregados: produção agrícola municipal. Rio de Janeiro. Found at:
  26. Kempton RA, Seraphin JC, Sword AM (1994) Statistical analysis of two-dimensional variation in variety yield trials. J Agric Sci 122:335–342CrossRefGoogle Scholar
  27. Laviola BG, Rosado TB, Bhering LL, Kobayashi AK, Resende MDV (2010) Genetic parameters and variability in physic nut accessions during early developmental stages. Pesquisa Agropecuária Brasileira 45(10):117–1123CrossRefGoogle Scholar
  28. MAPA, Ministério da Agricultura, Pecuária e Abastecimento. Registro Nacional de Cultivares. Found at: Last accessed 11 Jan, 2015Google Scholar
  29. Marin ALA, Costa MR, Sartorato A, Peloso MJD, Barros EG, Moreira MA (2003) Genetic variability and pedigree analysis of Brazilian common bean elite genotypes. Scientia Agricola 60(2):283–290CrossRefGoogle Scholar
  30. Martins MR, Oliveira JC, Di Mauro AO, Silva PC (2003) Evaluation of sweet passion fruit (Passiflora alata Curtis) populations obtained by open polinization. Rev Bras Frutic 25(1):111–114CrossRefGoogle Scholar
  31. Mrode RA. (2005) Linear models for the prediction of animal breeding values, 2nd edn. CABI Publishing, Oxfordshire, UK.CrossRefGoogle Scholar
  32. Mulamba NN, Mock JJ (1978) Improvement of yield potential of the Eto Blanco maize (Zea mays L.) population by breeding for plant traits. Egypt J Genet Cytol 7:40–51Google Scholar
  33. Muse SV, Gault S (1994) A likelihood approach for comparing synonymous and non-synonymous substitution rates, with application to the chloroplast genome. Mol Biol Evol 11:715–724PubMedGoogle Scholar
  34. Neves LG, Bruckner CH, Picanço MC, Sobrinho SP, Araújo KL, Luz PB, Barelli MAA, Krause W (2013) Genetic correlation between agronomically important traits in yellow passion fruit. Amer J Plant Sci 4:2112–2117CrossRefGoogle Scholar
  35. Oliveira EJ (2006) Development of microsatellite markers and their use for the generation and integration of genetic maps of yellow passion fruit (Passiflora edulis Sims f. flavicarpa Deg.) PhD Thesis. (Genetics and Plant Breeding), ESALQ, São PauloGoogle Scholar
  36. Ortiz DC, Bohórquez A, Duque MC, Tohme J, Cuéllar D, Vásquez TM (2012) Evaluating purple passion fruit (Passiflora edulis Sims f. edulis) genetic variability in individuals from commercial plantations in Colombia. Genet Resour Crop Evol 59:1089–1099CrossRefGoogle Scholar
  37. Patterson HD, Hunter EA (1983) The efficiency of incomplete block designs in national list and recommended list cereal variety trials. J Agric Sci Camb 103:427–433CrossRefGoogle Scholar
  38. Pritchard JK, Stephens M, Donnell YP (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959PubMedPubMedCentralGoogle Scholar
  39. Reis RV, Oliveira EJ, Viana AP, Pereira TNS, Pereira MG, Silva MGM (2011) Genetic diversity in recurrent selection of yellow passion fruit detected by microsatellites markers. Pesq Agrop Bras 46:51–57CrossRefGoogle Scholar
  40. Reis RV, Viana AP, Oliveira EJ, Silva MGM (2012) Phenotypic and molecular selection of yellow passion fruit progenies in the second cycle of recurrent selection. Crop Breed Appl Biotechnol 12(1):17–24CrossRefGoogle Scholar
  41. Resende MDV(2000) Análise estatística de modelos mistos via REML/BLUP na experimentação em melhoramento de plantas perenes, ColomboGoogle Scholar
  42. Santos EA, Souza MM, Viana AP, Almeida AA, Freitas JCO, Lawinscky PR (2011) Multivariate analysis of morphological characteristics of two species of passion flower with ornamental potential and of hybrids between them. Genet Mol Res 10(4):2457–2471CrossRefPubMedGoogle Scholar
  43. Santos EA, Viana AP, Freitas JCO, Rodrigues DL, Tavares RF, Paiva CL, Souza MM (2015a) Genotype selection by REML/BLUP methodology in a segregating population from an interspecific Passiflora spp Crossing. Euphytica 204(1):1–11CrossRefGoogle Scholar
  44. Santos EA, Viana AP, Freitas JCO, Rodrigues DL, Tavares RF, Paiva LC, Souza MM (2015b) Genotype selection by REML/BLUP methodology in a segregating population from an interspecific Passiflora spp. crossing. Euphytica 204(1):1–11CrossRefGoogle Scholar
  45. Silva MGM (2009) Recurrent selection intra-populational in passion fruit. PhD Thesis. (Plant Production), Universidade Estadual Norte FluminenseGoogle Scholar
  46. Silva MGM, Viana AP (2012) Alternatives of selection in a yellow passion fruit population under intrapopulation recurrent selection. Revista Brasileira de Fruticultura 34(2):525–531CrossRefGoogle Scholar
  47. Silva MGM, Viana AP, Gonçalves GM, Júnior ATA, Pereira MG (2009) Intrapopulation recurrent selection in yellow passion fruit: alternative to accumulate genetic gains. Ciência Agrotécnica 33(1):170–176CrossRefGoogle Scholar
  48. Viana AP, Pereira TNS, Pereira MG, de Souza MM, Maldonado JFM, do Amaral Júnior AT (2003) Simple and canonic correlation between agronomical and fruit quality traits in yellow passion fruit (Passiflora edulis f. flavicarpa) populations. Crop Breed Appl Biotechnol 3(2):133–140CrossRefGoogle Scholar
  49. Viana AP, Pereira TNS, Pereira MG, Amaral Júnior AT, Souza MM, Maldonado JFM (2004) Genetic parameters in populations of yellow passion fruit. Revista Ceres, Viçosa 51(297):541–551Google Scholar
  50. Wong YS, Sia CM, Khoo HE, Ang YK, Chang SK, Yim HS (2014) Influence of extraction conditions on antioxidant properties of passion fruit (Passiflora edulis) peel. Acta Sci Pol Technol Aliment 13(3):257–265CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Fernando H. L. Silva
    • 1
  • Patricio R. Muñoz
    • 2
    Email author
  • Christopher I. Vincent
    • 3
  • Alexandre Pio Viana
    • 1
  1. 1.Centro de Ciências e Tecnologia AgropecuáriaUniversidade Estadual do Norte Fluminense Darcy Ribeiro (UENF)Rio de JaneiroBrazil
  2. 2.Agronomy DepartmentUniversity of FloridaGainesvilleUSA
  3. 3.School of Natural Resources and Environment, Tropical Research and Education CentreUniversity of FloridaHomesteadUSA

Personalised recommendations