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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 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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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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Conditional Inference Tree for Multiple Gene-Environment Interactions on Myocardial Infarction.

Zhijun Wu1, Xiuxiu Su1, Haihui Sheng2

  • 1Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Archives of Medical Research
|December 21, 2017
PubMed
Summary
This summary is machine-generated.

This study used conditional inference trees to analyze gene-environment interactions for myocardial infarction (MI) risk. High-density lipoprotein cholesterol, genetic risk score, and other factors significantly influenced MI risk in Chinese men.

Keywords:
Conditional inference treeData miningGenome wide association studyMyocardial infarction

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Area of Science:

  • Cardiovascular Disease Epidemiology
  • Genetics and Genomics
  • Computational Biology

Background:

  • Identifying gene-environment interactions for complex diseases like myocardial infarction (MI) is challenging.
  • Previous studies have focused on single factors, overlooking combined genetic and environmental influences.

Purpose of the Study:

  • To develop and apply a novel method for analyzing gene-environment interactions in MI risk prediction.
  • To synthesize information from genetic and environmental factors using conditional inference trees (CTREE).

Main Methods:

  • A case-control study of 1440 Chinese men (730 MI patients, 710 controls).
  • Calculation of a weighted genetic risk score (GRS) from 25 single nucleotide polymorphisms (SNPs).
  • Development of a CTREE model to map gene-environment interactions influencing MI.

Main Results:

  • High-order interactions were detected between dyslipidemia, GRS, smoking, age, and diabetes.
  • High-density lipoprotein cholesterol (HDL-C) ≤1.25 mmol/L was a key discriminator for MI risk.
  • Specific combinations of HDL-C, GRS, and lipoprotein (a) levels indicated significantly elevated MI risk (OR: 5.89).

Conclusions:

  • The CTREE approach effectively visualizes complex gene-environment interactions.
  • CTREE simplifies risk assessment by providing a decision-making pathway based on multiple factors.
  • This method offers a powerful tool for understanding multifactorial disease etiology.