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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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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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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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Related Experiment Video

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A screening-testing approach for detecting gene-environment interactions using sequential penalized and unpenalized

H Robert Frost1, Angeline S Andrew, Margaret R Karagas

  • 1Institute for Quantitative Biomedical Sciences, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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This study introduces a novel method to detect gene-environment interactions, improving statistical power and reducing bias in genetic association studies. The new approach enhances the identification of complex biological relationships for better clinical outcome predictions.

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Area of Science:

  • Genetics
  • Environmental Health
  • Biostatistics

Background:

  • Gene-environment (G × E) interactions are crucial for understanding environmental exposures and clinical outcomes.
  • Traditional methods for detecting G × E interactions in genomewide association studies (GWAS) suffer from low statistical power and omitted variable bias due to multiple hypothesis correction (MHC) and single-model testing.
  • Penalized regression offers joint analysis but cannot assess statistical significance or effect size, while two-stage methods improve power but still face MHC and bias issues.

Purpose of the Study:

  • To develop a novel approach for detecting gene-environment interactions that overcomes the limitations of existing methods, specifically addressing low power and coefficient estimation bias.
  • To combine the strengths of screening-testing (two-stage) methods and hierarchical penalized regression for a more robust G × E interaction analysis.
  • To improve the accuracy and reliability of identifying G × E interactions in large-scale genetic data.

Main Methods:

  • A two-stage approach is proposed: Stage 1 uses elastic net-penalized multiple logistic regression for joint estimation of filter statistics (marginal association or gene-environment correlation) for all candidate genetic markers.
  • Stage 2 employs a single multiple logistic regression model to jointly assess marginal terms and G × E interactions for markers passing the Stage 1 filter.
  • A single likelihood-ratio test is utilized to determine the overall statistical significance of the assessed interactions.

Main Results:

  • The proposed method demonstrates improved efficacy compared to traditional G × E detection methods.
  • The approach effectively addresses both low power and omitted variable bias inherent in earlier techniques.
  • Validation on a bladder cancer dataset confirms the method's practical utility and performance.

Conclusions:

  • The novel combined screening-penalized regression approach offers a statistically valid and powerful method for detecting gene-environment interactions.
  • This method provides a significant advancement in analyzing complex G × E relationships, leading to more reliable genetic association studies.
  • The findings have implications for identifying genetic predispositions influenced by environmental factors, potentially improving disease risk prediction and prevention strategies.