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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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Predator-Prey Interactions02:39

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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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Export of Mitochondrial and Chloroplast Genes02:19

Export of Mitochondrial and Chloroplast Genes

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A eukaryotic cell can have up to three different types of genetic systems: nuclear, mitochondrial, and chloroplast. During evolution, organelles have exported many genes to the nucleus; this transfer is still ongoing in some plant species. Approximately 18% of the Arabidopsis thaliana nuclear genome is thought to be derived from the chloroplast’s cyanobacterial ancestor, and around 75% of the yeast genome derived from the mitochondria’s bacterial ancestor. This export has occurred...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

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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.
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...
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Gene Flow02:39

Gene Flow

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Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Robust semiparametric gene-environment interaction analysis using sparse boosting.

Mengyun Wu1,2, Shuangge Ma2

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.

Statistics in Medicine
|July 31, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for analyzing gene-environment interactions in complex diseases, improving accuracy for long-tailed data and handling missing variables. The approach offers better prediction and stability in disease research.

Keywords:
gene-environment interactionmissingnessrobustnesssemiparametric modelingsparse boosting

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Gene-environment (G-E) interactions are crucial for understanding complex disease pathogenesis.
  • Existing G-E interaction analyses face limitations including handling long-tailed distributions, data contamination, assuming linear effects, and accommodating missing environmental data.
  • Addressing these limitations is essential for robust disease association studies.

Purpose of the Study:

  • To develop a flexible statistical framework for gene-environment interaction analysis.
  • To accommodate nonlinear effects, data contamination, and missing environmental variables in G-E interaction studies.
  • To enhance the accuracy and stability of identifying genetic and environmental risk factors for complex diseases.

Main Methods:

  • A semiparametric model was employed to capture nonlinear G-E effects.
  • Huber loss function and Qn estimator were utilized to robustly handle long-tailed distributions and data contamination.
  • A regression-based multiple imputation method addressed missing environmental data.
  • Sparse boosting was used for efficient model estimation and variable selection.

Main Results:

  • The proposed method significantly outperformed existing approaches in extensive simulations.
  • The approach demonstrated satisfactory prediction accuracy and stability in real-world cancer datasets (stomach adenocarcinoma, cutaneous melanoma).
  • Sensible biological discoveries were made through the analysis of The Cancer Genome Atlas data.

Conclusions:

  • The developed semiparametric approach provides a powerful and flexible tool for G-E interaction analysis.
  • This method effectively addresses key limitations of previous G-E interaction studies, including nonlinearity, data contamination, and missingness.
  • The approach has practical implications for identifying disease-associated G-E interactions and improving disease risk prediction.