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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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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 gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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Prokaryotic genomes exhibit a streamlined organization of coding and non-coding regions essential for gene expression and protein synthesis. While coding regions contain the genetic instructions for proteins or functional RNAs, non-coding regions regulate the precise transcription and translation of these genes.Coding Regions: Proteins and RNAsThe primary coding regions, known as structural genes, include sequences transcribed into messenger RNA (mRNA) and ultimately translated into...
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The eukaryotic nucleus is a double membrane-bound organelle that contains nearly all of the cell’s genetic material in the form of chromosomes. It is rightly called the “brain” of the cell as it shoulders the responsibility of responding to various physiological processes, stress, altered metabolic conditions, and other cellular signals. 
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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Structured gene-environment interaction analysis.

Mengyun Wu1,2, Qingzhao Zhang3, Shuangge Ma2

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.

Biometrics
|August 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new structured gene-environment interaction analysis method that accounts for genetic data structures. It improves the analysis of complex diseases by effectively handling high-dimensional genetic information.

Keywords:
gene-environment interactionhigh-dimensional modelingstructured analysis

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Gene-environment (G-E) interactions are crucial for understanding complex diseases, but their analysis is challenging due to high dimensionality and the need to account for main effects and interactions.
  • Existing methods often fail to incorporate the inherent structures within genetic data, such as SNP adjacency and gene expression networks.

Purpose of the Study:

  • To develop a novel structured gene-environment interaction analysis method that accommodates the structural properties of genetic measurements.
  • To improve the accuracy and robustness of G-E interaction analysis in high-dimensional settings.

Main Methods:

  • Developed a structured G-E interaction analysis framework utilizing penalization for both main genetic effects and their interactions.
  • Applied penalization for regularized estimation and selection to handle high-dimensional data.
  • Incorporated structural information of genetic data, including SNP adjacency and gene expression networks.

Main Results:

  • The proposed structured interaction analysis method is effectively realized and demonstrates consistency properties under high-dimensional settings.
  • Simulations and real-world data analyses (GENEVA diabetes, TCGA melanoma) show competitive practical performance compared to existing methods.
  • The method successfully accommodates complex genetic data structures, leading to improved G-E interaction analysis.

Conclusions:

  • The developed structured G-E interaction analysis provides a powerful tool for dissecting the genetic and environmental components of complex diseases.
  • This approach enhances the ability to identify significant G-E interactions by leveraging the structural information within genetic data.
  • The method offers a robust and effective solution for high-dimensional G-E interaction studies in genomics and epidemiology.