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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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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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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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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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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Identifying gene-environment interactions incorporating prior information.

Xiaoyan Wang1,2, Yonghong Xu3, Shuangge Ma2

  • 1College of Finance and Statistics, Hunan University, Changsha, China.

Statistics in Medicine
|January 15, 2019
PubMed
Summary

Identifying gene-environment interactions for complex diseases is challenging. This study introduces a novel quasi-likelihood method that leverages existing literature to improve the detection of gene-environment interactions and main genetic effects.

Keywords:
G-E interactionpenalized joint analysisprior informationquasi-likelihood

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Gene-environment (G-E) interactions play a crucial role in complex diseases, often with effects independent of main genetic or environmental factors.
  • Identifying these G-E interactions remains a significant challenge in biomedical research despite extensive efforts.
  • Existing studies provide valuable, yet often underutilized, information for understanding G-E interactions and main genetic effects.

Purpose of the Study:

  • To develop a joint statistical modeling framework for identifying G-E interactions and their associated main genetic effects.
  • To incorporate information from existing literature into the statistical model to enhance the identification of G-E interactions.
  • To assess the performance and robustness of the proposed method using simulations and real-world biological data.

Main Methods:

  • A quasi-likelihood-based approach was developed to integrate information mined from existing biomedical literature.
  • A penalization strategy was employed for the identification and selection of G-E interactions, respecting the hierarchical structure of main effects and interactions.
  • The proposed method was evaluated through simulations under varying qualities of existing information and applied to The Cancer Genome Atlas (TCGA) data.

Main Results:

  • Simulations demonstrated significant improvements in identifying G-E interactions when incorporating high-quality existing information.
  • The method showed reasonable performance and robustness even with less informative existing literature.
  • Analysis of TCGA data for cutaneous melanoma and glioblastoma multiforme yielded practical and biologically sensible findings.

Conclusions:

  • The proposed quasi-likelihood and penalization approach effectively identifies gene-environment interactions and main genetic effects by leveraging existing literature.
  • The method offers a robust and practical tool for complex disease research, enhancing the discovery of G-E interactions.
  • This approach advances the field by integrating prior knowledge, leading to more informed and accurate identification of genetic and environmental influences on disease.