An ecophysiologically based mapping model identifies a major pleiotropic QTL for leaf growth trajectories of Phaseolus vulgaris. (11th July 2018)

• Record Type: Journal Article
• Authors: Wei, Kun; Wang, Jing; Sang, Mengmeng; Zhang, Shilong; Zhou, Houchao; Jiang, Libo; Clavijo Michelangeli, Jose A.; Vallejos, C. Eduardo; Wu, Rongling
• Is Part Of: Plant journal. Volume 95:Number 5(2018)
• Page Start: 775
• Page End: 784
• Publication Date: 2018-07-11
• Subjects: crop model -- growth equation -- functional mapping -- QTL -- the common bean; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.13986
• Languages: English
• ISSNs: 0960-7412

Abstract

Summary: 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. Significance Statement: A gene‐based crop model identifies a major QTL that determines how leaf traits grow in response to environmental change.