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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

237
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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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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Epistasis Analysis01:09

Epistasis Analysis

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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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Epistasis01:39

Epistasis

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

24
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
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An R-Based Landscape Validation of a Competing Risk Model
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Gene-environment interaction analysis under the Cox model.

Kuangnan Fang1, Jingmao Li1, Yaqing Xu2

  • 1Department of Statistics and Data Science, School of Economics, Xiamen University, No.422, Siming South Road, Xiamen 361005, Fujian, China.

Annals of the Institute of Statistical Mathematics
|February 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing gene-environment interactions in complex diseases. The approach rigorously identifies key genetic and environmental factors influencing survival outcomes, improving disease understanding.

Keywords:
Asymptotic consistencyCox modelGene–environment interaction analysisPenalized estimation

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Area of Science:

  • Genetics
  • Environmental Health
  • Biostatistics

Background:

  • Gene-environment (G-E) interactions are crucial for understanding complex diseases like cancer.
  • Current joint G-E interaction analyses for survival outcomes often lack rigorous statistical foundations.

Purpose of the Study:

  • To develop a statistically rigorous method for joint gene-environment interaction analysis in survival outcomes.
  • To address the limitations of existing methods by incorporating a strong theoretical framework.

Main Methods:

  • Utilizing the Cox model for survival analysis.
  • Applying sparse group penalization for regularized estimation and variable selection.
  • Ensuring the "main effects, interactions" variable selection hierarchy.

Main Results:

  • Rigorous establishment of consistency properties under high-dimensional settings.
  • Development of an effective computational algorithm.
  • Demonstration of competitive performance through simulations and TCGA STAD data analysis.

Conclusions:

  • The proposed method provides a statistically sound approach for joint G-E interaction analysis.
  • The method effectively identifies important main effects and interactions, advancing the field.
  • The approach has practical utility, as shown by its application to real-world cancer data.