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Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits

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Abstract

Pleiotropy has played an important role in understanding quantitative traits. However, the extensiveness of this effect in the genome and its consequences for plant improvement have not been fully elucidated. The aim of this study was to identify pleiotropic quantitative trait loci (QTLs) in maize using Bayesian multiple interval mapping. Additionally, we sought to obtain a better understanding of the inheritance, extent and distribution of pleiotropic effects of several components in maize production. The design III procedure was used from a population derived from the cross of the inbred lines L-14-04B and L-08-05F. Two hundred and fifty plants were genotyped with 177 microsatellite markers and backcrossed to both parents giving rise to 500 backcrossed progenies, which were evaluated in six environments for grain yield and its components. The results of this study suggest that mapping isolated traits limits our understanding of the genetic architecture of quantitative traits. This architecture can be better understood by using pleiotropic networks that facilitate the visualization of the complexity of quantitative inheritance, and this characterization will help to develop new selection strategies. It was also possible to confront the idea that it is feasible to identify QTLs for complex traits such as grain yield, as pleiotropy acts prominently on its subtraits and as this “trait” can be broken down and predicted almost completely by the QTLs of its components. Additionally, pleiotropic QTLs do not necessarily signify pleiotropy of allelic interactions, and this indicates that the pervasive pleiotropy does not limit the genetic adaptability of plants.

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Acknowledgments

Authors would like to thank Professor Anete Pereira de Souza of the Department of Biology and Evolution of the State University of Campinas for genotyping the population. Criticisms of both referees were very insightful and made this a better paper. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) has supported this research.

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Authors and Affiliations

  1. Departamento de Ciências Exatas, Universidade Federal de Lavras, CP 3037, Lavras, MG, 37200-000, Brazil

    Marcio Balestre & Júlio Sílvio de Sousa Bueno Filho

  2. Departamento de Agricultura, Universidade Federal de Lavras, CP 3037, Lavras, MG, 37200-000, Brazil

    Renzo Garcia Von Pinho

  3. Departamento de Genética, Escola de Agricultura Luiz de Queiroz, Universidade de São Paulo (ESALQ/USP), CP 83, Piracicaba, SP, 13400-970, Brazil

    Claudio Lopes de Souza Junior

Authors
  1. Marcio Balestre
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  2. Renzo Garcia Von Pinho
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  3. Claudio Lopes de Souza Junior
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  4. Júlio Sílvio de Sousa Bueno Filho
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Corresponding author

Correspondence to Marcio Balestre.

Additional information

Communicated by E. Carbonell.

Electronic supplementary material

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Supporting Fig 1 Behavior of Bayes Factor in selection of Pleiotropic or Linkage model (BMP 1739 kb)

122_2012_1847_MOESM2_ESM.bmp

Supporting Fig 2 QTL location (blue arrows) for pleiotropic model (A) and linkage model (B). One pleiotropic QTL (28 cM) and two linked QTLs (70 and 78 cM) acting on two simulated traits (trait 1 and trait 2) (BMP 3165 kb)

122_2012_1847_MOESM3_ESM.bmp

Supporting Fig 3 Estimates QTL intensity location for the linkage model (A) and the pleiotropic model using Jiang and Zeng (1995) method (B). One pleiotropic QTL (28 cM) and two linked QTLs (70 and 78 cM) acting on two simulated trait (trait 1 and trait 2) (BMP 806 kb)

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Balestre, M., Von Pinho, R.G., de Souza Junior, C.L. et al. Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits. Theor Appl Genet 125, 479–493 (2012). https://doi.org/10.1007/s00122-012-1847-1

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  • DOI: https://doi.org/10.1007/s00122-012-1847-1

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