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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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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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Overview
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Trihybrid Crosses02:27

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Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal...
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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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Related Experiment Video

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A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe
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A survey about methods dedicated to epistasis detection.

Clément Niel1, Christine Sinoquet2, Christian Dina3

  • 1Computer Science Institute of Nantes-Atlantic (Lina), Centre National de la Recherche Scientifique UMR 6241, Ecole Polytechnique de l'Université de Nantes Nantes, France.

Frontiers in Genetics
|October 7, 2015
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) identify single nucleotide polymorphisms (SNPs) linked to diseases. This review explores advanced methods for detecting complex SNP interactions (epistasis) beyond simple single-locus tests.

Keywords:
biological data miningcomplex diseaseepistasis detectionfeature selectiongenome-wide association study

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) have identified thousands of SNPs associated with diseases and complex traits.
  • Current analyses often use simplified single-locus tests, overlooking complex genetic interactions.
  • Understanding these interactions, known as epistasis, is crucial for a comprehensive view of disease genetics.

Purpose of the Study:

  • To review and present recent strategies for detecting epistatic interactions.
  • To explain the operating principles of various epistasis detection methods.
  • To highlight the challenges and advancements in genome-wide epistasis analysis.

Main Methods:

  • Exhaustive methods: Multifactor Dimensionality Reduction (MDR), Likelihood Ratio-Based Tests (LRT), Receiver Operating Characteristic (ROC) curve analysis.
  • Non-exhaustive methods: Machine learning techniques (Random Forests, Bayesian Networks), Combinatorial Optimization (Ant Colony Optimization, Evolutionary Systems).

Main Results:

  • The review categorizes and describes diverse analytical approaches for epistasis detection.
  • It contrasts exhaustive methods with non-exhaustive strategies, detailing their respective strengths and weaknesses.
  • The presented methods aim to overcome the computational challenges of genome-wide epistasis analysis.

Conclusions:

  • Detecting epistatic interactions is essential for understanding complex disease genetics beyond single SNP associations.
  • A variety of computational and statistical methods are available, each with specific applications and limitations.
  • Advancements in these methods are critical for tackling the scale of genome-wide epistasis analysis.