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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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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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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Identifying gene-environment interactions for prognosis using a robust approach.

Hao Chai1, Qingzhao Zhang2, Yu Jiang3

  • 1Department of Biostatistics, Yale University, United States.

Econometrics and Statistics
|June 4, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a robust accelerated failure time (AFT) model to improve prognosis prediction for complex diseases by accounting for data contamination and subtypes. The method effectively identifies gene-environment interactions in cancer data.

Keywords:
Exponential squared lossGene-environment interactionMarker identificationPrognosisRobustness

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Associated Chromosome Trap for Identifying Long-range DNA Interactions
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Area of Science:

  • Biostatistics
  • Genomics
  • Cancer Research

Background:

  • Prognosis is crucial for complex diseases, influenced by genetic (G) and environmental (E) factors, including gene-environment (G x E) interactions.
  • Prognosis data can exhibit contamination or mixture distributions, leading to biased estimations if not addressed.
  • Existing methods may struggle with data heterogeneity, impacting the accuracy of prognostic models.

Purpose of the Study:

  • To develop a robust statistical model for disease prognosis that accounts for data contamination and mixture distributions.
  • To enhance the accuracy of prognostic predictions by incorporating gene-environment interactions.
  • To introduce a penalized accelerated failure time (AFT) model with a novel loss function for improved estimation and marker selection.

Main Methods:

  • Utilized an accelerated failure time (AFT) model framework.
  • Proposed an exponential squared loss function to handle data contamination or mixture distributions.
  • Employed a penalization approach for regularized estimation and marker selection, implemented via coordinate descent (CD) and minorization maximization (MM) algorithms.

Main Results:

  • The proposed method demonstrates robust performance in the presence of data contamination or mixture distributions, outperforming nonrobust alternatives.
  • Achieved comparable or superior performance to existing robust methods like quantile regression in specific scenarios.
  • Successfully applied to The Cancer Genome Atlas (TCGA) lung cancer data, identifying significant gene-environment interactions and stable prognostic markers.

Conclusions:

  • The developed robust AFT model effectively addresses data contamination and mixture issues in prognostic analysis.
  • The method provides reliable identification of gene-environment interactions and prognostic markers, with implications for cancer research.
  • This approach offers a valuable tool for improving prognostic accuracy in complex diseases with heterogeneous data.