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Epistasis Analysis01:09

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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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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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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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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.
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Epistasis analysis using artificial intelligence.

Jason H Moore1, Doug P Hill

  • 1Department of Genetics, Geisel School of Medicine, DHMC, One Medical Center Dr., HB 7937, Lebanon, NH, 03756, USA, Jason.H.Moore@Dartmouth.edu.

Methods in Molecular Biology (Clifton, N.J.)
|November 19, 2014
PubMed
Summary
This summary is machine-generated.

We developed artificial intelligence (AI) methods to detect epistasis in genetic data. This computational evolution approach analyzes genome-wide data, mimicking human expert analysis for improved genetic association studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Epistasis, gene-gene interactions, is complex to detect in large genetic datasets.
  • Current methods for analyzing genetic association studies may not fully capture complex interactions.

Purpose of the Study:

  • To introduce a novel artificial intelligence (AI) methodology for detecting and characterizing epistasis.
  • To develop an AI strategy that analyzes genome-wide genetic data guided by expert knowledge.

Main Methods:

  • The study utilizes computational evolution, a form of genetic programming.
  • This approach learns to solve problems while generating novel solutions.

Main Results:

  • The AI methodology demonstrates potential for analyzing complex genetic association data.
  • Examples of application to real data are provided to showcase the approach.

Conclusions:

  • Artificial intelligence, specifically computational evolution, offers a powerful new approach for epistasis detection.
  • This methodology can enhance the analysis of genome-wide genetic association studies by mimicking expert reasoning.