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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

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...
Epistasis01:39

Epistasis

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...
Genetic Screens02:46

Genetic Screens

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
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
In-vitro Mutagenesis01:16

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

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Related Experiment Video

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Generation of Genetically Modified Mice through the Microinjection of Oocytes
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GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.

Ryan J Urbanowicz1, Jeff Kiralis, Nicholas A Sinnott-Armstrong

  • 1Department of Genetics, Institute for Quantitative Biomedical Sciences, Dartmouth Medical School, Lebanon, NH, USA. jason.h.moore@dartmouth.edu.

Biodata Mining
|October 3, 2012
PubMed
Summary
This summary is machine-generated.

GAMETES software generates complex genetic models for disease simulation studies. It precisely creates pure, strict multi-locus models, aiding in the evaluation of new epistasis detection algorithms.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Detecting complex multi-locus genetic effects, particularly epistasis, is challenging for geneticists.
  • Existing methods for simulating epistatic models, like genetic algorithms, are computationally expensive and lack precision.
  • Pure and strict epistatic models represent a challenging worst-case scenario for association studies, making them ideal for simulation.

Purpose of the Study:

  • To introduce GAMETES, a novel software package and algorithm for generating complex multi-locus genetic disease models.
  • To provide a user-friendly tool for creating simulated datasets to evaluate bioinformatics algorithms for epistasis detection.

Main Methods:

  • Developed GAMETES, an algorithm for generating random, pure, strict n-locus biallelic single nucleotide polymorphism (SNP) disease models.
  • Incorporated genetic constraints including heritability, minor allele frequencies, and population prevalence.
  • Included a dataset simulation strategy for rapid generation of simulated datasets.

Main Results:

  • GAMETES efficiently and accurately generates complex n-locus epistatic models with specified genetic constraints.
  • Demonstrated the utility of GAMETES in an example simulation study using the MDR algorithm.
  • The software facilitates the creation of simulated datasets for evaluating epistasis detection methods.

Conclusions:

  • GAMETES is a fast, flexible, and precise tool for generating complex genetic models for simulation studies.
  • It is particularly proficient at creating lower heritability models commonly used in algorithm evaluation.
  • GAMETES can be combined with various dataset simulation strategies and aids in theoretical characterization of genetic models.