Unraveling and Predicting Complex Traits in Plants: Tackling Polygenic Architecture and Genotype-by-Environment Interactions

Complex agronomic traits in plants, such as yield, grain quality, and stress tolerance, are governed by polygenic architectures and exhibit substantial phenotypic plasticity in response to environmental fluctuations. The interplay between genetic factors and environmental conditions—referred to as genotype-by-environment interaction (G×E)—remains a major challenge in plant breeding and quantitative genetics. While G×E is often treated as a confounding factor, understanding its underlying mechanisms is essential for developing climate-resilient crops. Recent advances in high-throughput genotyping, phenomics, envirotyping, and machine learning now provide unprecedented opportunities to dissect G×E from a statistical noise into a predictable biological component. This Research Topic aims to bring together cutting-edge approaches that integrate multi-omics data, environmental covariates, and advanced modeling frameworks to unravel how complex traits respond to environmental cues across diverse genetic backgrounds.The primary goal of this Research Topic is to advance the theoretical and applied understanding of how complex traits are shaped by genotype-by-environment interactions within a polygenic framework. We aim to bridge the gap between statistical genetics and translational breeding by promoting methodologies that move beyond static trait assessment toward dynamic, environment-aware prediction models. Specifically, this collection seeks to:• Elucidate the genetic architecture underlying G×E by identifying stable and environment-specific quantitative trait loci (QTLs) using multi-environment trial (MET) data and novel statistical approaches.• Develop and validate next-generation genomic prediction models that integrate environmental covariates (e.g., temperature, precipitation, soil properties) to improve predictive accuracy across unseen environments and climate scenarios.• Leverage high-dimensional phenotypic time-series data and multi-omics layers (transcriptomics, metabolomics) to capture the temporal dynamics of complex trait expression.• Translate these insights into breeding strategies that enhance crop adaptability and yield stability under changing environmental conditions.By fostering interdisciplinary contributions, this Research Topic aspires to establish a holistic framework for dissecting and predicting G×E, ultimately accelerating the development of climate-resilient crop varieties.This Research Topic welcomes original research, methods, review, and perspective that address the dissection and prediction of complex traits in plants under the influence of genotype-by-environment interactions. We encourage submissions that integrate multi-environment trial (MET) data, envirotyping, high-throughput phenotyping, and/or multi-omics approaches with quantitative genetics and machine learning frameworks. Key areas of interest include novel statistical models for G×E mapping, environment-aware genomic selection, dynamic trait analysis using time-series phenomics, and the application of polygenic models to breeding for climate adaptation. All manuscripts must adhere to the author guidelines of Frontiers in Genetics. Submissions that present validated methodologies, large-scale datasets, or translational applications in breeding programs are particularly encouraged. For further details on article types and submission requirements, please refer to the journal’s official website. Topic editors: Mingxing Cheng, Muhammad Ahsan Asghar, FEI XU, Bo Peng Source: https://www.frontiersin.org/research-topics/78826/unraveling-and-predicting-complex-traits-in-plants-tackling-polygenic-architecture-and-genotype-by-environment-interactions