361-5 Bayesian Mapping of Multiple Traits in Maize: the Importance of Pleiotropic Effects in Studying the Inheritance of Quantitative Traits.

Poster Number 1321

See more from this Division: ASA Section: Global Agronomy
See more from this Session: General Gaining Access To Agronomic Inputs

Wednesday, November 6, 2013
Tampa Convention Center, East Exhibit Hall

Renzo GARCIA Von Pinho, FUNDECC, Federal University of Lavras, Lavras, MG, BRAZIL, Marcio Balestre, CIENCIAS EXATAS, FEDERAL UNIVERSITY OF LAVRAS, Lavras, BRAZIL, CLAUDIO LOPES SOUZA JUNIOR, Departamento de Genetica, Escola de Agricultura Luiz de Queiroz, Universidade de Sao Paulo (ESALQ/USP), PIRACICABA, Brazil and JULIO SILVIO DE SOUSA BUENO FILHO, CIÊNCIAS EXATAS, UNIVERSIDADE FEDERAL DE LAVRAS, LAVRAS, Brazil
Poster Presentation
  • trabalho MárcioUSA 2013.pdf (1.1 MB)
  • 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.

    See more from this Division: ASA Section: Global Agronomy
    See more from this Session: General Gaining Access To Agronomic Inputs