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Related Concept Videos

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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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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The Representativeness Heuristic02:13

The Representativeness Heuristic

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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The Anchoring-and-Adjustment Heuristic01:25

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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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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Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis.

Yingxia Sun, Xuan Wang, Junliang Shang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 8, 2018
    PubMed
    Summary
    This summary is machine-generated.

    EACO, a novel method using ant colony optimization, effectively detects complex genetic interactions (epistasis) in genome-wide association studies. It improves accuracy by integrating heuristic information and complementary fitness functions for disease-related SNP analysis.

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    Area of Science:

    • Genetics
    • Bioinformatics
    • Computational Biology

    Background:

    • Epistasis learning aims to identify associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases within genome-wide association studies (GWAS).
    • Detecting epistatic interactions remains challenging due to the absence of effective heuristic information to guide the search process.

    Purpose of the Study:

    • To propose a novel method, EACO (Epistasis learning based on Ant Colony Optimization), for detecting epistatic interactions.
    • To enhance epistasis detection by introducing heuristic information, a combined fitness function, and a candidate solution filtration strategy.

    Main Methods:

    • EACO utilizes the ant colony optimization (ACO) algorithm, incorporating multi-SURF* heuristic information into ant-decision rules for linear-time guided search.
    • A fitness function combining mutual information and the Gini index is employed to evaluate SNP-phenotype associations.
    • A candidate solution filtration strategy is implemented to adaptively retain optimal solutions, improving search accuracy.

    Main Results:

    • EACO demonstrated promising performance in identifying epistasis.
    • Comparative experiments on simulation and a real age-related macular degeneration dataset showed EACO's effectiveness against established methods like AntEpiSeeker, MACOED, epiACO, BOOST, SNPRuler, TEAM, and epiMODE.

    Conclusions:

    • EACO offers a more accurate and efficient approach to epistasis searching in GWAS.
    • The proposed method, with its novel heuristic information and fitness evaluation, shows significant potential for uncovering complex genetic underpinnings of diseases.