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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

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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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Genetic Screens02:46

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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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Incomplete Dominance01:43

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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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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Updated: Dec 27, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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GenEpi: gene-based epistasis discovery using machine learning.

Yu-Chuan Chang1,2, June-Tai Wu3, Ming-Yi Hong4

  • 1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan.

BMC Bioinformatics
|February 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces GenEpi, a new computational tool for detecting genetic interactions (epistasis) linked to diseases. GenEpi efficiently identifies complex genetic patterns, aiding in understanding diseases like Alzheimer's.

Keywords:
EpistasisGWASMachine learning

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Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with phenotypes.
  • Current GWAS methods for detecting epistasis (interactions between genetic variants) are limited.
  • Efficient epistasis detection is vital for understanding complex diseases like Alzheimer's disease (AD).

Purpose of the Study:

  • To present GenEpi, a computational package designed to uncover epistasis associated with phenotypes using a machine learning approach.
  • To develop an efficient and effective GWAS method for detecting both within-gene and cross-gene epistasis.

Main Methods:

  • GenEpi employs a two-stage modeling workflow.
  • Features are generated using two-element combinatorial encoding.
  • Prediction models are constructed using L1-regularized regression with stability selection.

Main Results:

  • GenEpi outperforms existing methods in detecting ground-truth epistasis on simulated data.
  • Applied to Alzheimer's disease data, GenEpi identified biologically meaningful and predictive disease-related variant interactions.
  • The package demonstrated effectiveness and efficiency in detecting phenotype-associated epistasis.

Conclusions:

  • GenEpi effectively and efficiently detects epistasis associated with phenotypes, as shown by simulation and AD data.
  • The GenEpi package has the potential to significantly advance the study of complex diseases.