Quantitative gene–gene and gene–environment mapping for leaf shape variation using tree‐based models

Abstract

Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures.

Genetic and Developmental Basis for Increased Leaf Thickness in the Arabidopsis Cvi Ecotype

“…Not only is leaf shape inherently multidimensional, thus necessitating multivariate methods for its quantification, but it also changes throughout the organ’s ontogeny in response to a complex interplay of genetic and environmental factors. Finally, adaptive evolutionary change in leaf shape operates on this high dimensional space encompassing multiple genetic, tissue mechanics, environmental, and ontogeny-specific factors ( Langlade et al, 2005 ; Klingenberg, 2010 ; Merks et al, 2011 ; Baker et al, 2015 ; Fu et al, 2017 ). While leaf shape traits have been shown to have a heritable albeit polygenic basis ( Langlade et al, 2005 ; Tian et al, 2011 ; Chitwood et al, 2013 , 2014 ), they are also highly plastic to environmental cues such as temperature and moisture ( Royer and Wilf, 2006 ; Peppe et al, 2011 ).…”

Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data

“…Ensemble learning based on decision trees has been effective in achieving a balance between overfitting and under-fitting and also reducing variance of predictions through aggregating prediction results of multiple classifiers [71]. Another advantage of decision tree-based algorithms is that the hierarchical structures of decision trees naturally considers epistasis among variants without requiring an explicitly model structure [72]. For example, Bagging [73], Random forests [74], and AdaBoost [75,76] are some of the most well-known decision tree-based ensemble learning algorithms.…”

Botanical Digitization: Application of MorphoLeaf in 2D Shape Visualization, Digital Morphometrics, and Species Delimitation, using Homologous Landmarks of Cucurbitaceae Leaves as a Model

“…Geometric morphometrics (GMM) however combines both landmark and outline analysis in studying diversity and shape variation within species (Cope et al, 2012;Punyasena and Smith, 2014). GMM allows for reconstruction of average leaf shape and a visualization of the morphospace in which each species leaf shape belongs (Alasadi et al, 2017;Borges et al, 2020;Fu et al, 2017;Klein and Svoboda, 2017;Lexer et al, 2009;Li et al, 2018) Multiple programs have been used to examine the geometric morphometrics of leaves such as geomorph (Adams and Otárola-Castillo, 2013), tpsUtil and tpsDig2 (Rohlf, 2015), MorphoJ (Klingenberg, 2011), ImageJ (Abramoff et al, 2004), LEAFPROCESSOR (Backhaus et al, 2010) MorphoJ (Klingenberg, 2011), MorphoLeaf (Biot et al, 2016), and MASS (Chuanromanee et al, 2019), to analyze the landmark data. The present study uses MorphoLeaf (Biot et al, 2016) to integrate all basic steps of GMM analysis with an all-in-one method of preserving the leaf outline and integrity, and extracting all the vital details of the leaf details for multiscale analysis.…”

“…Geometric morphometrics (GMM) however combines both landmark and outline analysis in studying diversity and shape variation within species (Cope et al, 2012; Punyasena and Smith, 2014). GMM allows for reconstruction of average leaf shape and a visualization of the morphospace in which each species leaf shape belongs (Alasadi et al, 2017; Borges et al, 2020; Fu et al, 2017; Klein and Svoboda, 2017; Lexer et al, 2009; Li et al, 2018)…”