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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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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Gene Therapy00:59

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Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...
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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Gene-environment interactions in case-control studies with silent disease.

Iryna Lobach1, Joshua Sampson2, Siarhei Lobach3

  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco, California.

Genetic Epidemiology
|June 14, 2018
PubMed
Summary

Accurately estimating gene-environment interactions (G × E) in genome-wide association studies (GWAS) is challenging with undiagnosed disease in controls. A new pseudolikelihood method corrects bias caused by misdiagnosed controls in G × E estimation.

Keywords:
case-control studygene-environment interactionsprostate cancerpseudolikelihoodsilent disease

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Genome-wide association studies (GWAS) frequently assess gene-environment interactions (G × E).
  • Misclassification of disease status in control groups can bias G × E estimates.
  • Silent or undiagnosed disease in controls, varying with environmental factors, presents a significant challenge.

Purpose of the Study:

  • To develop a method for accurately estimating G × E in case-control GWAS.
  • To address bias introduced by undiagnosed disease in the control population.
  • To provide a robust approach for analyzing genetic associations influenced by environmental factors.

Main Methods:

  • Proposed a pseudolikelihood approach to correct for misdiagnosis in controls.
  • Evaluated the method's performance through extensive simulations.
  • Applied the developed method to a real-world GWAS dataset for prostate cancer.

Main Results:

  • Uncorrected case-control status leads to biased G × E estimates.
  • The pseudolikelihood method effectively removes bias from misdiagnosed controls.
  • Accurate estimation of G × E is achievable even with varying frequencies of silent disease.

Conclusions:

  • Accounting for undiagnosed disease in controls is crucial for accurate G × E estimation in GWAS.
  • The proposed pseudolikelihood method offers a reliable solution for correcting bias in G × E analyses.
  • This approach enhances the understanding of gene-environment interplay in diseases like prostate cancer.