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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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A Bayesian approach for exploring person × environment interaction within the environmental sensitivity

Francesca Lionetti1, Antonio Calcagnì2, Giulio D'Urso3

  • 1Department of Neurosciences, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.

Journal of Child Psychology and Psychiatry, and Allied Disciplines
|May 3, 2024
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Summary
This summary is machine-generated.

A new Bayesian method using Markov Chain Monte Carlo (MCMC) effectively estimates environmental sensitivity interactions. This approach outperforms traditional methods, especially in small samples, offering more accurate results for individual-environment interplay research.

Keywords:
Bayesian approachDifferential susceptibilityMCMC estimatesdiathesis‐stressvantage sensitivity

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Area of Science:

  • Psychology
  • Statistics
  • Environmental Health

Background:

  • Investigating individual-environment interplay is crucial for understanding environmental sensitivity.
  • Existing statistical models like Diathesis-stress face computational challenges, particularly with small sample sizes common in clinical studies.

Purpose of the Study:

  • To propose a Bayesian solution for estimating interaction parameters in environmental sensitivity models.
  • To adapt existing Nonlinear Least Squares (NLS) approaches using Monte Carlo Markov Chains (MCMC).

Main Methods:

  • A Bayesian approach utilizing Monte Carlo Markov Chains (MCMC) was developed.
  • The MCMC method was adapted from Widaman et al.'s Nonlinear Least Squares (NLS) approach.

Main Results:

  • Both MCMC and NLS yielded similar results with large sample sizes.
  • MCMC demonstrated superior performance over NLS, particularly with small samples and higher residual variance, resolving estimation issues and improving accuracy.

Conclusions:

  • The Bayesian MCMC approach offers a feasible solution to computational challenges in frequentist methods for studying individual-environment interactions.
  • This method enhances the trustworthiness of research outcomes, with potential impacts on clinical and applied understanding of environmental sensitivity.