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

Authors: FU, GUIFANG - Utah State University; DAI, XIAOTIAN - Utah State University; SYMANZIK, JURGEN - Utah State University; Bushman, Shaun

Submitted to: New Phytologist
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2016
Publication Date: 5/31/2017
Citation: Fu, G., Dai, X., Symanzik, J., Bushman, B.S. 2017. Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models. New Phytologist. 213:455-469.

Interpretive Summary: Leaf shape traits have long been a focus of many disciplines, but searching for complex genetic and environmental interactive mechanisms regulating leaf shape variation has not yet been well developed. The question of the respective roles of gene and environment and how they interplay to modulate leaf shape has been an inextricable evolutionary topic that calls for sophisticated methodology. In this article, we modeled leaf image photo data with genetic and environmental data, then determinewd the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed new method was verified through simulations and with a previously published Poplar dataset. This methodology confirmed the previous findings but also resulted in the detection of novel shape characteristics. The focus of this article will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology datasets. 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.

Technical Abstract: Leaf shape traits have long been a focus of many disciplines, but searching for complex genetic and environmental interactive mechanisms regulating leaf shape variation has not yet been well developed. The question of the respective roles of gene and environment and how they interplay to modulate leaf shape has been an inextricable evolutionary topic that calls for sophisticated methodology. In this article, we modeled leaf image photo data with genetic and environmental data, then determined the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed new method was verified through simulations and with a previously published Poplar dataset. This methodology confirmed the previous findings but also resulted in the detection of novel shape characteristics. The focus of this article will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology datasets. 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.