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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Epistasis Analysis01:09

Epistasis Analysis

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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

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...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

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Related Experiment Video

Updated: May 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

A novel method for identifying nonlinear gene-environment interactions in case-control association studies.

Cen Wu1, Yuehua Cui

  • 1Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.

Human Genetics
|August 27, 2013
PubMed
Summary

This study introduces a new statistical model to detect complex gene-environment interactions in diseases. The flexible varying-coefficient model captures nonlinear effects, improving our understanding of disease risk factors.

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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Complex diseases arise from interactions between multiple genetic variants and environmental factors.
  • Gene × environment (G × E) interactions are crucial for individual disease risk, but their mechanisms are not fully understood.
  • Current regression-based methods often assume linear relationships, which may not capture the complexity of G × E interactions.

Purpose of the Study:

  • To propose a flexible varying-coefficient model for detecting nonlinear G × E interactions in binary disease traits.
  • To extend previous work on continuous traits to address binary outcomes.
  • To develop statistical tests for elucidating various G × E interaction mechanisms.

Main Methods:

  • A flexible varying-coefficient model was proposed, approximating varying coefficients using non-parametric regression.
  • The model assesses the nonlinear response of genetic factors to environmental changes.
  • Statistical tests were developed to analyze different G × E interaction mechanisms.

Main Results:

  • The proposed varying-coefficient model effectively detects nonlinear G × E interactions for binary disease traits.
  • Simulation studies and real-world data analysis demonstrated the method's utility.
  • The approach provides a more nuanced understanding of genetic and environmental contributions to disease.

Conclusions:

  • The flexible varying-coefficient model offers a powerful tool for investigating complex G × E interactions.
  • This method enhances the analysis of genetic and environmental influences on disease risk, particularly for nonlinear relationships.
  • The findings have implications for understanding and potentially mitigating risks for diseases like type 2 diabetes.