G3: Genes, Genomes, Genetics (Dec 2017)

A Predictive Model for Time-to-Flowering in the Common Bean Based on QTL and Environmental Variables

  • Mehul S. Bhakta,
  • Salvador A. Gezan,
  • Jose A. Clavijo Michelangeli,
  • Melissa Carvalho,
  • Li Zhang,
  • James W. Jones,
  • Kenneth J. Boote,
  • Melanie J. Correll,
  • James Beaver,
  • Juan M. Osorno,
  • Raphael Colbert,
  • Idupulapati Rao,
  • Stephen Beebe,
  • Abiezer Gonzalez,
  • Jaumer Ricaurte,
  • C. Eduardo Vallejos

DOI
https://doi.org/10.1534/g3.117.300229
Journal volume & issue
Vol. 7, no. 12
pp. 3901 – 3912

Abstract

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The common bean is a tropical facultative short-day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G × E interactions. Phenotypic data were collected from a biparental [Andean × Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G × E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTL. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 d. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in silico QTL allele combinations that could yield desired phenotypes under different climatic conditions.

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