bioRxivEvolutionary BiologyPreprint
Non-linear phenotypic variation uncovers the emergence of heterosis in Arabidopsis thaliana
François VasseuriD, Louise FouqueauiD, Dominique de VienneiD, Thibault NideletiD, Cyrille ViolleiD, Detlef WeigeliD
Abstract
Heterosis describes the phenotypic superiority of hybrids over their parents in traits related to fitness. Understanding and predicting non-additive inheritance such as heterosis is crucial for evolutionary biology, as well as for plant and animal breeding. However, the physiological bases of heterosis remain debated. Moreover, empirical data in various species have shown that diverse genetic and molecular mechanisms are likely to explain heterosis, making it difficult to predict its emergence and amplitude from parental genotypes alone. In this study, we evaluated a model of physiological dominance proposed by Sewall Wright to explain the non-additive inheritance of metabolic fluxes at the cellular level. We used 450 hybrids derived from crosses among natural inbred accessions of Arabidopsis thaliana to test Wrights model for two fitness-related traits at the whole-plant level: growth rate and fruit number. We found that allometric relationships between traits constrain phenotypic variation in hybrids and inbreds to a similar extent. These allometric relationships behave predictably, in a non-linear manner, explaining up to 75% of heterosis amplitude, while genetic distance among parents at best explains 7%. Thus, our findings are consistent with Wrights model of physiological dominance on plant performance, and suggest that the emergence of heterosis is an intrinsic property of non-linear relationships between traits. Furthermore, our study highlights the potential of a geometric approach of phenotypic relationships for predicting heterosis of two major components of crop productivity and yield.
- organism: Arabidopsis
- assay: proteomics
- assay: metabolomics
- research focus: host-pathogen & immune response
- research focus: development & morphogenesis
- research focus: metabolism & bioenergetics
- research focus: cell type annotation
- approach: statistical model
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