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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...
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...
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...
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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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...

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Related Experiment Video

Updated: May 24, 2026

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

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Published on: January 7, 2014

Testing gene-environment interactions in gene-based association studies.

Xuefeng Wang1, Huaizhen Qin, Nathan J Morris

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH 44106-7281, USA. robert.elston@cwru.edu.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using kernel machines to detect gene-environment interactions, particularly for rare variants. The approach shows reasonable power in identifying these complex genetic effects in population data.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Gene-based and single-nucleotide polymorphism (SNP) set association studies complement SNP analysis.
  • Kernel-based nonparametric regression is a powerful tool for genetic association studies.
  • Testing for gene-environment interactions, especially with rare variants, remains a challenge.

Purpose of the Study:

  • To extend kernel-based nonparametric regression to incorporate and test for gene-environment interaction effects.
  • To specifically address the challenge of detecting interactions involving rare variant SNPs within genes.
  • To evaluate the performance of a novel least-squares kernel machine approach for interaction analysis.

Main Methods:

  • Construction of nonparametric regression models within the least-squares kernel machine framework.
  • Inclusion of gene-environment interaction effects in the statistical models.
  • Application and performance evaluation using the Genetic Analysis Workshop 17 dataset with simulated replicates.

Main Results:

  • The proposed least-squares kernel machine method successfully detected simulated gene-environment interaction effects.
  • Genome scan analysis of the quantitative phenotype Q1 demonstrated the method's capability.
  • The approach showed reasonable power for detecting interaction effects, even with rare variants.

Conclusions:

  • Kernel-based nonparametric regression, specifically the least-squares kernel machine, is a viable and powerful tool for detecting gene-environment interactions.
  • This method offers a flexible approach for analyzing complex genetic effects, including those involving rare variants.
  • The findings support the utility of this method for genetic association studies aiming to uncover gene-environment interplay.