, 213:228 | Cite as

Implementing genomic selection in sour passion fruit population

  • Alexandre Pio Viana
  • Fernando Higino de Lima e Silva
  • Leonardo Siqueira Glória
  • Rodrigo Moreira Ribeiro
  • Willian Krause
  • Marcela Santana Bastos Boechat


Sour passion fruit is an economically important tropical fruit crop with little explored genetic potential. This study aimed to provide breeders with essential estimates of genomic breeding values in economically important traits in passion fruit, using Bayesian models which may contribute to the implementation of Genomic Selection and develop new strategies for the continuity of sour passion fruit breeding programs. For this, the following Bayesian models were tested using 183 polymorphic marks: Bayesian Ridge regression, Bayes A, Bayes B, Bayes B2, Bayes Cπ and Bayesian Lasso for estimation of genomic breeding values. To achieve this, ninety-five full-sib progenies derived from the third cycle of recurrent selection of the sour passion fruit (Passiflora edulis Sims.) at Universidade Estadual do Norte Fluminense Darcy Ribeiro—UENF were used and eight fruit yield (number of fruit, total yield, mean fruit weight, fruit length, fruit width) and quality(percent pulp, skin thickness, soluble solids) traits were assessed. The Bayes Cπ (smaller deviance information criterion) yield the best genetic predictions for almost all traits. Genetic correlations in this study indicate that the number of fruit can be used as a proxy for yield. The values of genomic heritability obtained were high and ranged from 0.62 to 0.76 and predict accuracy ranged from 0.55 to 0.75, so we can to speculate that the use of two replicates in the present study was an adequate amount to obtain phenotypic mean, which was used to adjust the genomic prediction model.


Genomic selection Bayesian methods Sour passion fruit Passiflora Genomic heritability 


  1. Bernardo R, Yu J (2007) Prospects for genome wide selection for quantitative traits in maize. Crop Sci 47(3):1082–1090CrossRefGoogle Scholar
  2. Chen Y, Lübberstedt T (2010) Molecular basis of trait correlations. Trends Plant Sci 15:454–461CrossRefPubMedGoogle Scholar
  3. Coelho AA, Cenci SA, de Resende ED (2010) Qualidade do suco de maracujá-amarelo em diferentes pontos de colheita e após o amadurecimento. Ciência e Agrotecnologia 34(3):722–729CrossRefGoogle Scholar
  4. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A (2009) Prediction quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375–385CrossRefPubMedPubMedCentralGoogle Scholar
  5. de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327–345CrossRefPubMedCentralGoogle Scholar
  6. Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15Google Scholar
  7. Geweke J (1992) Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: Bernardo JM, Berger J, Dawid AP, Smith AFM (eds) Bayesian statistics, vol 4. Oxford University Press, Oxford, pp 169–193Google Scholar
  8. Gianola D (2013) Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 194:573–596CrossRefPubMedPubMedCentralGoogle Scholar
  9. Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando RL (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183:347–363CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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
  11. Grattapaglia D, Resende MDV (2011) Genomic selection in forest tree breeding. Tree Genet Genomes 7(2):241–255CrossRefGoogle Scholar
  12. Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinform 12:186 1–12.
  13. Kumar S, Chagné D, Bink MCAM, Volz RK, Whitwork C (2012) Genomic selection for fruit quality traits in apple (Malus × domestica Borkh). PLoS ONE 7:e36674CrossRefPubMedPubMedCentralGoogle Scholar
  14. Legarra A, Robert-Granie C, Manfredi E, Elsen JM (2008) Performance of genomic selection in mice. Genetics 180:611–618CrossRefPubMedPubMedCentralGoogle Scholar
  15. 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
  16. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  17. 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 40:1978–1981Google Scholar
  18. Morgado MAD, Santos CEM, Linhales H, Bruckner CH (2010) Correlações fenotípicas em características fisicoquímicas do maracujazeiro-azedo. Acta Agron 59(4):457–461Google Scholar
  19. 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
  20. 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):255–261CrossRefGoogle Scholar
  21. Park T, Casella G (2008) The bayesian LASSO. J Am Stat Assoc 103:681–686CrossRefGoogle Scholar
  22. Pérez P, de los Campos G (2014) Genome-wide regression & prediction with the BGLR statistical package. Genetics 198(2):483–495CrossRefPubMedPubMedCentralGoogle Scholar
  23. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0,
  24. Resende MDV, Lopes PS, Silva RL, Pires IL (2008) Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesqui Florest Bras 56:63–77Google Scholar
  25. Resende MDV, Silva FF, Lopes PS, Azevedo CF (2012a) Seleção genômica ampla (GWS) via modelos mistos (REML/BLUP), inferência Bayesiana (MCMC), Regressão aleatória multivariada (RRM) e estatística espacial. Universidade Federal de Viçosa, Viçosa, p 291Google Scholar
  26. Resende MFR, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MDV, Kirst M (2012b) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617–624CrossRefPubMedGoogle Scholar
  27. Silva FF, Rosa GJ, Guimarães SE, Lopes PS, de los Campos G (2011) Three-step Bayesian factor analysis applied to QTL detection in crosses between outbred pig populations. Livest Sci 142(1):210–215CrossRefGoogle Scholar
  28. Silva FHL, Muñoz PR, Vincent CI, Viana AP (2016) Generating relevant information for breeding Passiflora edulis: genetic parameters and population structure. Euphytica 208:609–619CrossRefGoogle Scholar
  29. Silva FHL, Viana AP, Santos EA, Freitas JCO, Rodrigues DL, Amaral Júnior AT (2017) Prediction of genetic gains by selection indexes and REML/BLUP methodology in a population of sour passion fruit under recurrent selection. Acta Sci 39(2):183–190Google Scholar
  30. Smith BJ (2007) boa: an R package for MCMC output convergence assessment and posterior inference. J Stat Softw 21:1–37CrossRefGoogle Scholar
  31. Spiegelhalter DJ, Best NG, Carlin BP, Van der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B 64:583–639CrossRefGoogle Scholar
  32. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58(1):267–288 (Methodological)Google Scholar
  33. Usai MG, Goddard ME, Hayes BJ (2009) LASSO whit cross-validation for genomic selection. Genet Res 91:427–436CrossRefGoogle Scholar
  34. Viana AP, Resende MDV (2014) Genética quantitativa no melhoramento de fruteiras, 10th edn. Interciência, Rio de Janeiro, p 280pGoogle Scholar
  35. Viana AP, Resende MDV, Summaira R, Walker MA (2016) Genome selection in fruit breeding: application to table grapes. Sci Agricola 73(2):142–149CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Alexandre Pio Viana
    • 1
  • Fernando Higino de Lima e Silva
    • 2
  • Leonardo Siqueira Glória
    • 1
  • Rodrigo Moreira Ribeiro
    • 1
  • Willian Krause
    • 3
  • Marcela Santana Bastos Boechat
    • 1
  1. 1.Centro de Ciências e Tecnologias AgropecuáriasUniversidade Estadual do Norte Fluminense Darcy Ribeiro (UENF)Campos dos GoytacazesBrazil
  2. 2.Instituto Federal GoianoRio VerdeBrazil
  3. 3.Universidade do Estado do Mato Grosso (UNEMAT)Tangará da SerraBrazil

Personalised recommendations