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bioRxiv · 10.1101/2024.05.05.592603

Complex genetic architecture underlying the plasticity of tobacco leaf width provides insight into across-environment genomic prediction

Abstract

Phenotypic plasticity refers to the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and developing predictive models for agronomic traits are crucial for future agricultural adaptation to climate change. In this study, we investigated the genetic basis of leaf width mean (LWm) and plasticity (LWp) in a tobacco multiparent advanced generation inter-cross (MAGIC) population which consisting of 594 individuals. We identified 14 quantitative trait loci (QTLs) significantly associated with LWm, 43 with LWp. Our findings suggest that dynamic changes in QTL effects across environments, along with polygenic effects, may underlie the genetic basis of leaf width plasticity. Among them, qLW14 was narrowed down to a 3 Mb structural variation region. When this fragment was deleted in tobacco, plants exhibited increased leaf width, but only under specific environmental conditions. This finding suggests that the key gene within qLW14 may act as a negative regulator of leaf width through interactions with specific environmental factors. By integrating genetic diversity, environments variation, and their interactions into a GEAI model, we were able to build a framework for cross-environment prediction, improving prediction accuracy by 8.7% when compared to traditional model. Overall, this study highlights the complex genetic basis underlying LWp, involving multiple alleles and genotype-environment interactions. These findings provide valuable insights into the role of phenotypic plasticity in plant adaptation to environmental changes.

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BibTeXRIS

liu, l., liu, w., sun, y., han, y., hao, r., zhang, w., guo, l., zhao, j., Zan, Y., cheng, l., si, h.. 2024-05-07. Complex genetic architecture underlying the plasticity of tobacco leaf width provides insight into across-environment genomic prediction. https://doi.org/10.1101/2024.05.05.592603

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