{
  "id": 18562367,
  "title": "A hierarchical Bayesian model for dynamic phenotypes in multi-environment trials with genotype by environment interaction",
  "url": "https://urgent.news/2026/11/06/a-hierarchical-bayesian-model-for-dynamic-phenotypes-in-multi",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-06T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764527v1?rss=1"
  },
  "original_language": "en",
  "account": "In multi-environment trials, new phenotyping tools generate longitudinal phenotypes that outline plant growth and development. The parameters that characterize these dynamic traits can be integrated as predictors in models for static target traits, such as yield and biomass. A hierarchical Bayesian approach was employed to develop a differential equation (DE) model for log-logistic growth in longitudinal traits, considering sets of genotypes across various environments. This model comprises three key DE parameters: midpoint, slope, and asymptote, which represent final biomass. The DE parameters are influenced by genotype, environment, and their interaction (G x E). By utilizing this formulation, researchers can evaluate and quantify the genotype-specificity of the DE parameters. Should none of the DE parameters necessitate a G x E term, the dynamic parameters associated with the specific trait will serve as valuable features for incorporating into prediction models for yield or biomass. The study employed APSIM-simulated longitudinal wheat biomass data to fit a DE model and assess the contributions of genotype, environment, and G x E to phenotypic variation in the DE parameters. The analysis revealed that the asymptote exhibited a clear G x E effect, while genotypic and G x E parameters within the DE parameters were used to characterize genotypes, and estimates for environmental and G x E impacts formed inputs for environmental characterization.",
  "summary": "New phenotyping tools produce longitudinal phenotypes that describe plant growth and development. Estimates for the parameters that describe the dynamics of such longitudinal phenotypes can be incorporated as predictors in models for static target traits like yield and biomass. Within a hierarchical Bayesian context, we present a differential equation (DE) model for log-logistic growth in…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}