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

Abstract

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.

Genomic Prediction of Agronomic Traits in Common Bean (Phaseolus vulgaris L.) Under Environmental Stress

“…Predictions differed between traits and reached best accuracies in DF ( Figure 5). The genetic architecture of DF is comparatively simple and has been modelled before (Bhakta et al, 2017;Wallach et al, 2018). However, also complex traits such as yield reached good PAs, e.g.…”

The genetic architecture of biomechanical traits in sorghum

“…A multitrait QTL analysis was performed for each trait with the BLUEs calculated for each individual experiment from each RIL population. A mixed‐model approach using GenStat 18.1 (VSN International) was used for the multitrait QTL mapping as described by several authors (Assanga et al., 2017; Bhakta et al., 2017; Boer et al., 2007; Carvalho et al., 2015; Malosetti, Ribaut, & van Eeuwijk, 2013; Malosetti, Ribaut, Vargas, Crossa, & Van Eeuwijk, 2008; Sukhwinder‐Singh et al., 2012). Unlike single‐trait QTL analysis, the multitrait QTL analysis permits the evaluation of pleiotropy and linkage of genes as causes of genetic relationship between traits (Sukhwinder‐Singh et al., 2012).…”

Contributions in Puerto Rico to Bean, Phaseolus spp., Research

“…University of Puerto Rico researchers collected field data in Isabela for collaborative research designed to better understand the response of beans to different photoperiod and temperature regimes (Bhakta et al, 2017;Wallace et al, 1995). Other collaborative bean field trials were conducted to study rates of biomass accumulation and partitioning of biomass into seed yield (Wallace et al, 1993).…”