An ecophysiologically based mapping model identifies a major pleiotropic QTL for leaf growth trajectories of Phaseolus vulgaris

Abstract

Crop modeling, a widely used tool to predict plant growth and development in heterogeneous environments, has been increasingly integrated with genetic information to improve its predictability. This integration can also shed light on the mechanistic path that connects the genotype to a particular phenotype under specific environments. We implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework. The procedure enables the characterization of how QTLs act differently in response to developmental and environmental cues. We used this procedure to map growth parameters of leaf area and mass in a mapping population of the common bean (Phaseolus vulgaris L.). Different sets of QTLs are responsible for various aspects of growth, including the initiation time of growth, growth rate, inflection point and asymptotic growth. A major QTL of a large effect was identified to pleiotropically affect trait expression in distinct environments and different traits expressed on the same organism. The integration of crop models and QTL mapping through our statistical procedure provides a powerful means of building a more precise predictive model of genotype-phenotype relationships for crops.

Model-based QTL detection is sensitive to slight modifications in model formulation

“…The ten growth models used during the study consider the impact of initial fruit dry mass DM 0 (observed value) on DM max , and they encompassed differing numbers of parameters (from two to four). Some of our growth equations are the most commonly used models [10]. Eq (2), is shown as eqGibert in this study and is the logistic function Verhulst modified by Gibert et al [30] introducing a parameter, A , to express DM max as being proportional to DM 0 .…”

“…Two main approaches were found in the literature to ensure the applicability of QTL-based ecophysiological modeling. The first is called functional mapping, defined as the top-down approach by Wei et al [10]; it integrates mathematical relationships of different traits into QTL mapping theory. Various authors have applied functional mapping [11–14], using different mathematical relationships.…”

“…Chang-Xing et al [16] applied functional mapping with nonlinear mixed-effect models, while Xing et al [17] used Bayesian B-spline mapping. A second approach, defined as the bottom-up approach by Wei et al [10], is preferred by several authors [18–20]. The method consists of fitting the models first, followed by mapping QTLs based on the estimated parameters.…”

“…Even a good fit cannot guarantee unique parameter estimation, therefore finding the true parameter value remains a challenge since it is hidden in the correlated relations [23–24]. Finally, the method can also outperform the bottom-up approach in terms of curve estimation precision [10].…”

Interaction analyses based on growth parameters of GWAS between Escherichia coli and Staphylococcus aureus

“…It is able to address the limitations of traditional GWAS methods, and uses growth trajectories as a phenotype to conduct GWAS, which remarkably increases the power for gene detection. In this way, f GWAS can capture genotypic differences at the level of phenotypic curves according to growth parameters (Li et al 2015 ; Wei et al 2018 ). On this basis, a previous study fit growth equations to leaf area and leaf area mass growth data of an individual recombinant inbred line (RIL), and developed a bivariate model for mapping growth parameters of the two traits (Wei et al 2018 ).…”