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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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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Multivariate partial linear varying coefficients model for gene-environment interactions with multiple longitudinal

Honglang Wang1, Jingyi Zhang2,3, Kelly L Klump4

  • 1Department of Mathematical Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA.

Statistics in Medicine
|May 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing multiple health traits over time, enhancing the identification of genetic factors influencing complex behaviors like emotional eating. The method reveals dynamic gene effects and potential environmental interactions.

Keywords:
gene-environment interactionlongitudinal traitsmulti-trait analysispartial linear modelquadratic inference function

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Area of Science:

  • Genetics
  • Biostatistics
  • Behavioral Science

Background:

  • Correlated phenotypes frequently share common genetic underpinnings, suggesting that multi-trait analyses can improve statistical power and elucidate pleiotropic effects.
  • Longitudinal data allows for the examination of dynamic gene effects over time, offering deeper insights into genetic influences on traits measured repeatedly.

Purpose of the Study:

  • To propose a multivariate partially linear varying coefficients model for identifying genetic variants whose effects may be modulated by environmental factors.
  • To develop a testing framework for jointly assessing genetic associations while accounting for time-varying genetic effects in longitudinal data.

Main Methods:

  • Utilized a multivariate partially linear varying coefficients model with penalized splines for nonparametric function approximation.
  • Extended quadratic inference functions to manage longitudinal correlations in bivariate phenotypic traits.
  • Established theoretical properties including consistency and asymptotic normality for the proposed estimators.

Main Results:

  • The proposed statistical framework effectively identifies genetic variants associated with complex traits.
  • Demonstrated the method's utility in identifying single nucleotide polymorphisms linked to emotional eating behavior in a real-world dataset.
  • Simulation studies confirmed the performance and robustness of the developed testing procedure.

Conclusions:

  • The multivariate partially linear varying coefficients model offers a powerful approach for genetic association studies with longitudinal, correlated phenotypes.
  • This method enhances the understanding of pleiotropy and dynamic gene-environment interactions over time.
  • The findings have implications for identifying genetic determinants of behaviors such as emotional eating.