Exploiting light energy utilization strategies in Populus simonii through multitrait-GWAS: insights from stochastic differential models.

Abstract

The photosynthetic phenotype of trees undergoes changes and interactions that reflect their abilities to exploit light energy. Environmental disturbances and genetic factors have been recognized as influencing these changes and interactions, yet our understanding of the underlying biological mechanisms remains limited, particularly in stochastic environments. Here, we developed a high-dimensional stochastic differential framework (HDSD) for the genome-wide mapping of quantitative trait loci (QTLs) that regulate competition or cooperation in environment-dependent phenotypes. The framework incorporates random disturbances into system mapping, a dynamic model that views multiple traits as a system. Not only does this framework describe how QTLs regulate a single phenotype, but also how they regulate multiple phenotypes and how they interact with each other to influence phenotypic variations. To validate the proposed model, we conducted mapping experiments using chlorophyll fluorescence phenotype data from Populus simonii. Through this analysis, we identified several significant QTLs that may play a crucial role in photosynthesis in stochastic environments, in which 76 significant QTLs have already been reported to encode proteins or enzymes involved in photosynthesis through functional annotation. The constructed genetic regulatory network allows for a more comprehensive analysis of the internal genetic interactions of the photosynthesis process by visualizing the relationships between SNPs. This study shows a new way to understand the genetic mechanisms that govern the photosynthetic phenotype of trees, focusing on how environmental stochasticity and genetic variation interact to shape their light energy utilization strategies.