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Related Concept Videos

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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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Epigenetic Regulation01:37

Epigenetic Regulation

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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
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Epigenetic Regulation01:46

Epigenetic Regulation

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Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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Related Experiment Video

Updated: Apr 17, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Understanding Epistatic Interactions between Genes Targeted by Non-coding Regulatory Elements in Complex Diseases.

Min Kyung Sung1, Hyoeun Bang1, Jung Kyoon Choi1

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Korea.

Genomics & Informatics
|February 24, 2015
PubMed
Summary

Epigenetic interactions influence common diseases like type 2 diabetes, hypertension, and coronary artery disease. This study identifies disease-associated genes in regulatory regions, revealing shared genetic links between these conditions.

Keywords:
coronary artery diseasediabetes mellitusepistasishypertensionregulatory region

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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Area of Science:

  • Genetics
  • Genomics
  • Molecular Biology

Background:

  • Complex diseases exhibit polygenic architecture, often missed by single-locus methods.
  • Non-coding variants are frequently associated with diseases, posing challenges in understanding their functional impact.
  • Epistatic interactions, or gene-gene interactions, play a crucial role in complex trait genetics.

Purpose of the Study:

  • To investigate epistatic interactions in type 2 diabetes mellitus (DM), hypertension (HT), and coronary artery disease (CAD) using Korea Association Resource (KARE) data.
  • To identify regulatory elements and genes implicated in the genetic basis of these common diseases.
  • To explore the shared genetic architecture and interrelationships between DM, HT, and CAD.

Main Methods:

  • Genome-wide association studies (GWAS) focusing on epistatic single-nucleotide polymorphisms (SNPs).
  • Analysis of SNP enrichment in regulatory regions, including enhancers and DNase I footprints (ENCODE Project Consortium 2012).
  • Integration of whole-genome multiple-cell-type enhancer data (DNase I profiles and Cap Analysis Gene Expression [CAGE]) to assign affected genes.
  • Construction of a knowledge-based epistatic network to visualize gene-disease relationships.

Main Results:

  • Epistatic SNPs were significantly enriched in enhancer regions and DNase I footprints, suggesting disruption of transcription factor binding sites.
  • Genes assigned through enhancer data analysis were significantly enriched in known disease-associated gene sets.
  • The developed epistatic network revealed shared associated genes and numerous epistatic interactions among DM, HT, and CAD.
  • These findings highlight the genetic underpinnings of the observed clinical relationships between the three diseases.

Conclusions:

  • Epistatic interactions in non-coding regulatory regions are critical for understanding complex diseases.
  • The integration of multi-omics data, including enhancer profiles, is a powerful approach for identifying disease-relevant genes.
  • DM, HT, and CAD share a common genetic basis mediated by epistatic interactions, providing insights into their co-occurrence.