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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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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.
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Genetic Screens02:46

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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
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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.
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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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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.
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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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A penalized robust method for identifying gene-environment interactions.

Xingjie Shi1, Jin Liu, Jian Huang

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China; Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America.

Genetic Epidemiology
|March 12, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel rank-based penalized estimation method for identifying gene-environment interactions in high-throughput studies. The approach offers improved accuracy, especially with complex data, and avoids reliance on significance levels for interaction selection.

Keywords:
gene-environment interactionmarker identificationpenalizationrobust rank estimation

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Area of Science:

  • Genomics
  • Biostatistics
  • Epidemiology

Background:

  • Identifying gene-environment interactions is crucial for understanding disease etiology and phenotypes in high-throughput studies.
  • Existing methods often rely on parametric models susceptible to misspecification and use significance levels for interaction selection.

Purpose of the Study:

  • To develop a robust method for identifying gene-environment interactions that is less sensitive to model misspecification.
  • To introduce a penalized rank-based estimation approach for simultaneous estimation and identification of interactions.
  • To enhance computational feasibility through smoothed rank estimation.

Main Methods:

  • Utilized rank-based estimation, known for its robustness to model specification.
  • Employed penalization for the simultaneous estimation and identification of gene-environment interactions.
  • Developed a smoothed rank estimation for computational efficiency.

Main Results:

  • The proposed method demonstrated superior performance compared to existing alternatives, particularly with contaminated or heavy-tailed data.
  • Accurate identification of gene-environment interactions was achieved without relying on significance levels.
  • Analysis of a lung cancer study revealed novel gene interactions with potential biological significance.

Conclusions:

  • The rank-based penalized estimation offers a more reliable approach for identifying gene-environment interactions in complex datasets.
  • The method provides a valuable tool for genomic research, improving the accuracy of interaction discovery.
  • The identified gene interactions in the lung cancer study warrant further investigation for their clinical implications.