An Integrated Deep Learning and Statistical Framework for Whole-Network Gene–Environment Association with Leaf Vascular Architecture
A novel framework integrating deep learning and statistics enables whole-network gene–environment association analysis using leaf vascular architecture as a phenotype. It fine-tunes EDTER for vein extraction, builds an annotated image database, and applies SSCCA to identify three significant gene–geography interactions in poplar.
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[Submitted on 23 Jul 2026]
Title:An Integrated Deep Learning and Statistical Framework for Whole-Network Gene--Environment Association with Leaf Vascular Architecture
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Abstract:Leaf veins exhibit remarkable diversity in architecture and patterning, yet existing gene--environment association studies have primarily quantified leaf venation using a small collection of low-dimensional summary traits, thereby discarding most of the structural information contained in the original images. We propose an integrated deep learning and statistical framework. The proposed framework achieves four methodological advances. First, it represents the complete leaf vascular architecture as a whole-network image phenotype. Second, it fine-tunes the deep learning-based Edge Detection with Transformers (EDTER) model to accurately extract whole-network leaf vascular architecture from RGB images by jointly learning local and global contextual features. Third, it constructs a new annotated leaf image database by integrating edge maps generated by DiffusionEdge with the Berkeley Segmentation Database (BSDS500). Fourth, it applies Semiparametric Sparse Canonical Correlation Analysis (SSCCA) to perform variable selection and model associations between repeatedly measured high-dimensional Bivariate image responses and high-dimensional predictors while simultaneously accommodating sparse, zero-inflated data represented by edge maps through a truncated latent Gaussian copula model. Two simulation studies demonstrate the performance of the proposed framework under increasing levels of complexity. Application to a real \emph{Populus} dataset identifies three significant gene--geography interactions associated with leaf vascular architecture, providing new biological insights and establishing a broadly applicable methodological framework for high-dimensional complex image phenotypes.
Subjects:
Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2607.22763 [cs.LG]
(or arXiv:2607.22763v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.22763
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Geran Zhao [view email] [v1] Thu, 23 Jul 2026 23:23:00 UTC (1,077 KB)
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