, 164:551 | Cite as

Heritability of quantitative traits in segregating common bean families using a Bayesian approach

  • Maria Celeste Gonçalves-Vidigal
  • Freddy Mora
  • Thaís Souto Bignotto
  • Roxelle Ethienne Ferreira Munhoz
  • Lara Daniela de Souza


Genetic parameters for six quantitative traits in the early generation of segregating populations of common beans (Phaseolus vulgaris L.) were evaluated. A Bayesian approach was used for estimating the variance components, breeding values and broad sense heritability of the quantitative traits under analysis. The Markov Chain Monte Carlo method was utilized to analyze the contribution of genes affecting complex traits. Twenty-four F3 families were evaluated in the field during 2005 in Santa Catarina, southern Brazil. With regard to the grain yield and yield components, the additive variances were relatively similar to the dominance variances. This result is confirmed by the 95% credible set from the posterior distribution. The mean estimates of broad-sense heritability (H2) varied from 11.5% to 64.2%. The heritability estimates of yield and yield components were higher than the estimates for the number of days until flowering and reproductive period. However, for grain yield, the 95% heritability credible set included the heritability estimates from point of crop duration. The predicted genetic gain reached the highest value for the number of pods per plant (10.95%). Days to flowering and reproductive period had the lowest values of genetic advance. One hundred seed-weight, grain yield and seeds per pod exhibited a similar predictable level of genetic gain: GA = 5.73%, 5.81% and 4.77%, respectively. The Bayesian framework provided information that is useful for a breeding program, since it contributes to the understanding of how quantitative traits are genetically controlled.


Bayesian analysis Breeding value Genetic effect Markov Chain Monte Carlo 



Days to flowering


Deviance Information Criterion


Markov chain Monte Carlo


Pods per plant


Reproductive period


Seeds per pod


100 Seed-weight


Grain yield



This research was financed by CNPq and CAPES. M. C. Gonçalves-Vidigal receives financial support from CNPq. Freddy Mora was supported by a fellowship from CAPES.


  1. Anbessa Y, Warkentin T, Vandenberg A, Bandara M (2006) Heritability and predicted gain from selection in components of crop duration in divergent chickpea cross populations. Euphytica 152:1–8. doi: 10.1007/s10681-006-9163-y CrossRefGoogle Scholar
  2. Ball RD (2001) Bayesian methods for quantitative trait loci mapping based on model selection: approximate analysis using the Bayesian information criterion. Genetics 59:1351–1364Google Scholar
  3. Barelli MAA, Gonçalves-Vidigal MC, Amaral AT Jr, Vidigal-Filho PS, Scapim CA, Sagrilo E (2000) Diallel analysis for grain yield and yield components in Phaseolus vulgaris L. Acta Sci 22:883–887Google Scholar
  4. Bink MCAM, Boer MP, ter Braak CJF, Cansen J, Voorrips RE, van de Weg WE (2007) Bayesian analysis of complex traits in pedigreed plant populations. Euphytica . doi: 10.1007/s10681-007-9516-1 Google Scholar
  5. Blasco A (2001) The Bayesian controversy in animal breeding. J Anim Sci 79:2023–2046PubMedGoogle Scholar
  6. Broman KW, Speed TP (2002) A model selection approach for the identification of quantitative trait loci in experimental computing time can be intensive within each sweep of crosses. J R Stat Soc [Ser A] 64:641–656. doi: 10.1111/1467-9868.00354 CrossRefGoogle Scholar
  7. Campos AD, Ferreira AG, Hampe MMV, Antunes IF, Brancão N, Silveira EP et al (2004) Peroxidase and polyphenol oxidase activity in bean anthracnose resistance. Pesqui Agropecu Bras 39(7):637–643. doi: 10.1590/S0100-204X2004000700004 Google Scholar
  8. Carbonell SM, Ito MF, Pompeu AS, Francisco F, Ravagnani S, Almeida ALL (1999) Raças fisiológicas de Colletotrichum lindemuthianum e reação de cultivares e linhagens de feijoeiro no Estado de São Paulo. Fitopatol Bras 24:60–65Google Scholar
  9. Casquero PA, Lema M, Santalla M, De Ron AM (2006) Performance of common bean (Phaseolus vulgaris L.) landraces from Spain in the Atlantic and Mediterranean environments. Genet Resour Crop Evol 53:1021–1032. doi: 10.1007/s10722-004-7794-1 CrossRefGoogle Scholar
  10. Ceolin ACG, Gonçalves-Vidigal MC, Vidigal Filho PS, Kvitschal MV, Gonela A, Scapim CA (2007) Genetic divergence of the common bean (Phaseolus vulgaris L.) group Carioca using morpho-agronomic traits by multivariate analysis. Hereditas 144:1–9. doi: 10.1111/j.2006.0018-0661.01943.x CrossRefGoogle Scholar
  11. Coelho ADF, Cardoso AA, Cruz CD, Araújo GAA, Furtado MR, Amaral CLF (2002) Herdabilidades e correlações da produção do feijão e dos seus componentes primários, nas épocas de cultivo da primavera-verão e do verão-outono. Cienc Rural 32:211–216. doi: 10.1590/S0103-84782002000200005 CrossRefGoogle Scholar
  12. Cruz CD, Carneiro PCS (2003) Modelos biométricos aplicados ao melhoramento genético. UFV, ViçosaGoogle Scholar
  13. Dawo MI, Sanders FE, Pilbeam DJ (2007) Grain yield, yield components and plant architecture in the F3 generation of common bean (Phaseolus vulgaris L.) derived from a cross between the determinate cultivar ‘Prelude’ and an indeterminate landrace. Euphytica 156:77–87. doi: 10.1007/s10681-007-9354-1 CrossRefGoogle Scholar
  14. Delgado-Salinas A, Bibler R, Lavin M (2006) Phylogeny of the genus Phaseolus (Leguminosae): a recent diversification in an ancient landscape. Syst Bot 31:779–791. doi: 10.1600/036364406779695960 CrossRefGoogle Scholar
  15. De Ron AM, Menéndez-Sevillano MC, Santalla M (2004) Variation in primitive landraces of common bean (Phaseolus vulgaris L.) from Argentina. Genet Resour Crop Evol 51:883–894. doi: 10.1007/s10722-005-1934-0 CrossRefGoogle Scholar
  16. FAO (2006) Base de dados FAOSTAT. Cited 01 Jul 2006
  17. Fehr WR (1987) Principles of cultivar development. Macmillan, IowaGoogle Scholar
  18. Gaitán-Solís E, Duque MC, Edwards KJ, Tohme J (2002) Microsatellite repeats in common bean (Phaseolus vulgaris): isolation, characterization and cross-species amplification in Phaseolus ssp. Crop Sci 42:2128–2136Google Scholar
  19. Graham PH, Ranalli P (1997) Common bean (Phaseolus vulgaris L.). Field Crops Res 53:131–146. doi: 10.1016/S0378-4290(97)00112-3 CrossRefGoogle Scholar
  20. Gonçalves-Vidigal MC, Silvério L, Vidigal Filho PS (2005) Diallel analysis of the combining ability of common bean (Phaseolus vulgaris L.) cultivars. BIC Annu Rep 48:184–185Google Scholar
  21. Heath SC (1997) Markov chain Monte Carlo segregation and linkage analysis of oligogenic models. Am J Hum Genet 61:748–760. doi: 10.1086/515506 PubMedCrossRefGoogle Scholar
  22. Heidelberger P, Welch PD (1983) Simulation run length control in the presence of an initial transient. Oper Res 31:1109–1114CrossRefGoogle Scholar
  23. Holsinger KE (1999) Analysis of genetic diversity in geographically structured populations: a Bayesian perspective. Hereditas 130:245–255. doi: 10.1111/j.1601-5223.1999.00245.x CrossRefGoogle Scholar
  24. Kelly J, Kolkman JM, Schneider K (1998) Breeding for yield in dry bean (Phaseolus vulgaris L.). Euphytica 102:343–356. doi: 10.1023/A:1018392901978 CrossRefGoogle Scholar
  25. Kumar S, Van-Rheenen HA, Singh O (1999) Genetic analysis of different components of crop duration in chickpea. J Genet Breed 53:189–200Google Scholar
  26. Machado CF, Santos JB, Nunes GHS (2000) Escolha de genitores de feijoeiro por meio da divergência avaliada a partir de caracteres morfo-agronômicos. Bragantia 59:11–20. doi: 10.1590/S0006-87052000000100004 CrossRefGoogle Scholar
  27. Machado CF, Santos JB, Nunes GHS, Ramalho PMA (2002) Choice of common bean parents based on combining ability estimates. Genet Mol Biol 25:179–183. doi: 10.1590/S1415-47572002000200011 CrossRefGoogle Scholar
  28. Mumba LE, Galwey NW (1999) Compatibility between wild and cultivated common bean (Phaseolus vulgaris L.) genotypes of the Mesoamerican and Andean gene pools: Evidence from the inheritance of quantitative characters. Euphytica 108:105–119. doi: 10.1023/A:1003652125405 CrossRefGoogle Scholar
  29. Papini A, Banci F, Nardi E (2007) Molecular evidence of polyphyletism in the plant genus Carum L. (Apiaceae). Genet Mol Biol 30:475–482. doi: 10.1590/S1415-47572007000300029 CrossRefGoogle Scholar
  30. Piepho HP, Mohring J, Melchinger AE, Buchse A (2007) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica . doi: 10.1007/s10681-007-9449-8 Google Scholar
  31. Ramalho MAP, Santos JB, Zimmermann MJO (1993) Genética quantitativa em plantas autógamas: aplicações no melhoramento do feijoeiro. Goiânia, UFGGoogle Scholar
  32. Rava CA, Purchio AF, Sartorato A (1994) Caracterização de patótipos de Colletotrichum lindemuthianum que ocorrem em algumas regiões produtoras de feijoeiro comum. Fitopatol Bras 19:167–172Google Scholar
  33. Roeder K, Escobar M, Kadane JB, Balazs I (1998) Measuring heterogeneity in forensic databases using hierarchical Bayes models. Biometrika 85:269–287. doi: 10.1093/biomet/85.2.269 CrossRefGoogle Scholar
  34. Rosal CJS, Ramalho MAP, Gonçalves FMA, Abreu AFB (2000) Early selection for common bean grain yield. Bragantia 59:189–195. doi: 10.1590/S0006-87052000000200010 CrossRefGoogle Scholar
  35. SAS-Institute (1996) Statistical analysis system user’s guide. SAS Institute, CaryGoogle Scholar
  36. Sillanpaa MJ, Arjas E (1998) Bayesian mapping of quantitative trait loci from incomplete inbreed line cross data. Genetics 148:1373–1388PubMedGoogle Scholar
  37. Singh SP, Cajiao C, Gutiérrez JA, Garcia J, Pastor-Corrales MA, Morales FJ (1989) Selection for seed yield in inter-gene pool crosses of common bean. Crop Sci 29:1126–1131Google Scholar
  38. St Martin SK, Futi X (2000) Genetic gain in early stages of a soybean breeding program. Crop Sci 40:1559–1564Google Scholar
  39. Van-Tassell CP, Van-Vleck DL (1995) A manual for use of MTGSAM. A set of FORTRAN programs to apply Gibbs sampling to animal models for variance component estimation [DRAFT]. U.S. Department of Agriculture (Agricultural Research Service), Lincoln, p 86Google Scholar
  40. Van-Tassell CP, Van-Vleck LD (1996) Multiple-trait Gibbs sampler for animal models: flexible programs for Bayesian and likelihood-based (co)variance component inference. J Anim Sci 74:2586–2597PubMedGoogle Scholar
  41. Walsh B (2001) Quantitative Genetics in the age of genomics. Theor Popul Biol 59:175–184. doi: 10.1006/tpbi.2001.1512 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Maria Celeste Gonçalves-Vidigal
    • 1
  • Freddy Mora
    • 1
  • Thaís Souto Bignotto
    • 1
  • Roxelle Ethienne Ferreira Munhoz
    • 1
  • Lara Daniela de Souza
    • 1
  1. 1.Departamento de AgronomiaUniversidade Estadual de MaringáMaringaBrazil

Personalised recommendations