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Related Concept Videos

Gene-Environment Interactions01:20

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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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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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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Related Experiment Video

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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Gene-environment dependence creates spurious gene-environment interaction.

Frank Dudbridge1, Olivia Fletcher2

  • 1Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.

American Journal of Human Genetics
|August 26, 2014
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Summary
This summary is machine-generated.

Statistical interactions between genetic markers and environment may be misleading. True gene-environment interactions can be masked or falsely detected when genetic markers are used instead of causal variants, especially under gene-environment dependence.

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Area of Science:

  • Genetics
  • Epidemiology
  • Bioinformatics

Background:

  • Gene-environment interactions are crucial for understanding disease etiology and improving risk prediction.
  • Few gene-environment interactions are confirmed, partly due to reliance on genetic markers (e.g., tag SNPs) instead of causal variants.
  • Previous research indicates power loss and increased sample size requirements when using markers under gene-environment independence.

Purpose of the Study:

  • To investigate the impact of gene-environment dependence on the detection of statistical interactions between genetic markers and environmental factors.
  • To determine conditions under which marker-environment interactions may arise without true causal variant-environment interactions.

Main Methods:

  • Theoretical analysis of statistical interactions under conditions of gene-environment dependence.
  • Examination of scenarios involving mediation, pleiotropy, and confounding.
  • Consideration of linkage disequilibrium for gene-gene interactions.

Main Results:

  • A statistical marker-environment interaction can exist even when no true interaction is present between the causal variant and the environment, particularly under gene-environment dependence.
  • Simple conditions for the absence of marker-environment interaction were identified but do not generally hold when gene-environment dependence is present.
  • Gene-environment dependence is a property of the causal variant and cannot be directly assessed from marker data.

Conclusions:

  • Caution is advised when interpreting statistical interactions involving genetic markers, especially in the presence of gene-environment dependence.
  • The findings highlight potential pitfalls in epidemiological risk models relying on marker data.
  • Further consideration of mechanistic interpretations and the limitations of genetic markers in interaction studies is warranted.