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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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Background and Environment Affect Phenotype02:27

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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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Epistasis Analysis01:09

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Pleiotropy01:33

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Overview
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Epistasis01:39

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Updated: May 30, 2025

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

Jingmao Li1, Qingzhao Zhang1,2, Shuangge Ma3

  • 1Department of Statistics and Data Science, School of Economics, Xiamen University, Fujian, China.

Statistics in Medicine
|January 27, 2025
PubMed
Summary

This study introduces a new method for analyzing gene-environment (G-E) interactions in complex diseases, particularly for hierarchical multi-label classification. The approach effectively handles unlabeled data, improving disease outcome prediction and feature selection.

Keywords:
G‐E interactionshierarchical multi‐label classificationhigh‐dimensional datasemi‐supervised

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Area of Science:

  • Biomedical Informatics
  • Genetics
  • Computational Biology

Background:

  • Gene-environment (G-E) interactions are crucial for understanding disease etiology beyond individual genetic or environmental factors.
  • Existing G-E interaction analyses often overlook hierarchical multi-label classification and semi-supervised learning scenarios.
  • Unlabeled data, common in real-world biomedical studies, are frequently excluded by current hierarchical multi-label classification methods.

Purpose of the Study:

  • To develop a novel computational framework for analyzing G-E interactions within a hierarchical multi-label classification context.
  • To address the challenge of incorporating unlabeled data in G-E interaction analysis using a semi-supervised approach.
  • To provide a robust method for identifying complex disease outcomes influenced by G-E interactions.

Main Methods:

  • A novel approach for two-layer hierarchical response incorporating G-E interactions was developed.
  • A two-step penalized estimation strategy was employed.
  • An efficient expectation-maximization (EM) algorithm was utilized for parameter estimation.

Main Results:

  • The proposed method demonstrated superior performance in both classification accuracy and feature selection compared to existing approaches.
  • Simulations confirmed the effectiveness and robustness of the developed algorithm.
  • Application to The Cancer Genome Atlas (TCGA) lung cancer data validated its practical utility.

Conclusions:

  • This study bridges a significant knowledge gap in G-E interaction analysis by introducing a versatile framework for hierarchical multi-label classification.
  • The developed method offers a powerful tool for dissecting complex disease mechanisms influenced by G-E interactions.
  • The approach effectively handles semi-supervised learning scenarios, enhancing applicability to real-world biomedical data.