Significance and Stability Analysis of Gene-Environment Interaction using RGxEStat

• URL: http://arxiv.org/abs/2604.03337v1
• Date: Fri, 03 Apr 2026 07:19:57 GMT
• Title: Significance and Stability Analysis of Gene-Environment Interaction using RGxEStat
• Abstract summary: Genotype-by-Environment (GxE) interactions influence the performance of genotypes across diverse environments.<n>This paper introduces two key models for GxE interaction research.<n>It also presents RGxEStat, a lightweight interactive tool developed by the authors.
• Score: 8.285860290078018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Genotype-by-Environment (GxE) interactions influence the performance of genotypes across diverse environments, reducing the predictability of phenotypes in target environments. In-depth analysis of GxE interactions facilitates the identification of how genetic advantages or defects are expressed or suppressed under specific environmental conditions, thereby enabling genetic selection and enhancing breeding practices. This paper introduces two key models for GxE interaction research. Specifically, it includes significance analysis based on the mixed effect model to determine whether genes or GxE interactions significantly affect phenotypic traits; stability analysis, which further investigates the interactive relationships between genes and environments, as well as the relative superiority or inferiority of genotypes across environments. Additionally, this paper presents RGxEStat, a lightweight interactive tool, which is developed by the authors and integrates the construction, solution, and visualization of the aforementioned models. Designed to eliminate the need for breeders and agronomists to learn complex SAS or R programming, RGxEStat provides a user-friendly interface for streamlined breeding data analysis, significantly accelerating research cycles. Codes and datasets are available at https://github.com/mason-ching/RGxEStat.

Related papers

• GRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana [42.524660998568315]
  We present a novel dataset linking gene expression profiles with phenotypic trait measurements in Arabidopsis thaliana.<n>GRAFT supports tasks such as phenotype prediction and interpretable graph learning.<n>This is the first dataset to provide multimodal gene information and heterogeneous trait or phenotype data for the same Arabidopsis thaliana specimens.
  arXiv  Detail & Related papers  (2026-06-25T16:02:38Z)
• AGP: A Novel Arabidopsis thaliana Genomics-Phenomics Dataset and its HyperGraph Baseline Benchmarking [43.90415454135298]
  We present a curated dataset linking gene expression profiles with phenotypic trait measurements in Arabidopsis thaliana.<n>AGP supports tasks such as phenotype prediction and interpretable graph learning.<n>To the best of our knowledge, this is the first dataset that provides multi-modal gene information and heterogeneous trait or phenotype data.
  arXiv  Detail & Related papers  (2025-08-19T21:21:23Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• Inferring genotype-phenotype maps using attention models [0.21990652930491852]
  Predicting phenotype from genotype is a central challenge in genetics.<n>Recent advances in machine learning, particularly attention-based models, offer a promising alternative.<n>Here, we apply attention-based models to quantitative genetics.
  arXiv  Detail & Related papers  (2025-04-14T16:32:17Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion [108.94237816552024]
  We propose the first genotype-to-phenotype diffusion model (G2PDiffusion) that generates morphological images from DNA.<n>The model contains three novel components: 1) a MSA retrieval engine that identifies conserved and co-evolutionary patterns; 2) an environment-aware MSA conditional encoder that effectively models complex genotype-environment interactions; and 3) an adaptive phenomic alignment module to improve genotype-phenotype consistency.
  arXiv  Detail & Related papers  (2025-02-07T06:16:31Z)
• FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics [46.189419603576084]
  FGBERT is a novel metagenomic pre-trained model that employs a protein-based gene representation as a context-aware tokenizer.<n>It demonstrates superior performance on metagenomic datasets at four levels, spanning gene, functional, bacterial, and environmental levels.
  arXiv  Detail & Related papers  (2024-02-24T13:13:17Z)
• evolSOM: an R Package for evolutionary conservation analysis with SOMs [0.4972323953932129]
  We introduce evolSOM, a novel R package that utilizes Self-Organizing Maps (SOMs) to explore and visualize the conservation of biological variables. The package automatically calculates and graphically presents displacements, enabling efficient comparison and revealing conserved and displaced variables. Illustratively, we employed evolSOM to study the displacement of genes and phenotypic traits, successfully identifying potential drivers of phenotypic differentiation in grass leaves.
  arXiv  Detail & Related papers  (2024-02-09T20:33:48Z)
• Genetic heterogeneity analysis using genetic algorithm and network science [2.6166087473624318]
  Genome-wide association studies (GWAS) can identify disease susceptible genetic variables. Genetic variables intertwined with genetic effects often exhibit lower effect-size. This paper introduces a novel feature selection mechanism for GWAS, named Feature Co-selection Network (FCSNet)
  arXiv  Detail & Related papers  (2023-08-12T01:28:26Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Granger causal inference on DAGs identifies genomic loci regulating transcription [77.58911272503771]
  GrID-Net is a framework based on graph neural networks with lagged message passing for Granger causal inference on DAG-structured systems. Our application is the analysis of single-cell multimodal data to identify genomic loci that mediate the regulation of specific genes.
  arXiv  Detail & Related papers  (2022-10-18T21:15:10Z)
• Mycorrhiza: Genotype Assignment usingPhylogenetic Networks [2.286041284499166]
  We introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium.
  arXiv  Detail & Related papers  (2020-10-14T02:36:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.