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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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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Related Experiment Video

Updated: Jun 8, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Association Rule Discovery Has the Ability to Model Complex Genetic Effects.

William S Bush, Tricia A Thornton-Wells, Marylyn D Ritchie

    IEEE Symposium on Computational Intelligence and Data Mining. IEEE Symposium on Computational Intelligence and Data Mining
    |September 28, 2011
    PubMed
    Summary

    Association Rule Discovery (ARD) effectively analyzes complex genetic data for diseases like Parkinson's. The Apriori algorithm shows power in detecting genetic effects, even with trait and locus heterogeneity.

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    Last Updated: Jun 8, 2026

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    Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

    Published on: August 15, 2019

    Area of Science:

    • Genetics
    • Bioinformatics
    • Computational Biology

    Background:

    • Advances in genotyping necessitate efficient analysis methods for genetic association studies.
    • Complex diseases involve multiple genes and traits, complicating genetic analysis.
    • Understanding genetic architecture requires methods to address trait heterogeneity, locus heterogeneity, and epistasis.

    Purpose of the Study:

    • To evaluate the effectiveness of Association Rule Discovery (ARD) using the Apriori algorithm for analyzing complex genetic data.
    • To assess ARD's power in detecting genetic effects under various simulated genetic architectures.
    • To determine if bootstrapping improves ARD's performance in genetic association studies.

    Main Methods:

    • Application of the Apriori algorithm, an Association Rule Discovery (ARD) method.
    • Analysis of simulated genetic datasets with varying degrees of complexity, including trait heterogeneity, locus heterogeneity, and epistasis.
    • Utilizing information difference to prior as a rule measure within the Apriori algorithm.

    Main Results:

    • Apriori demonstrated good power to detect functional effects in simulated cases of simple trait heterogeneity and trait heterogeneity with epistasis.
    • Moderate power was observed for trait heterogeneity combined with locus heterogeneity.
    • Bootstrapping the rule induction process did not significantly enhance the power to detect these genetic effects.

    Conclusions:

    • Association Rule Discovery (ARD) provides a flexible framework for characterizing complex genetic effects in association studies.
    • The Apriori algorithm is a viable tool for identifying patterns in large-scale genetic data.
    • ARD methods can help unravel the genetic underpinnings of complex diseases by accounting for intricate genetic interactions and variations.