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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

6.2K
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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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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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Related Experiment Video

Updated: Apr 5, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Review: High-performance computing to detect epistasis in genome scale data sets.

Alex Upton, Oswaldo Trelles, José Antonio Cornejo-García

    Briefings in Bioinformatics
    |August 15, 2015
    PubMed
    Summary

    Most human diseases stem from complex genetic variations, not single markers. Analyzing gene interactions (epistasis) using computational methods and high-performance computing offers a more promising approach for disease research and diagnostics.

    Keywords:
    SNP-interactionsbiomarkerdisease markerepistasisgenome sequencinggenotypinghigh-performance computing

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    Area of Science:

    • Genetics
    • Computational Biology
    • Bioinformatics

    Background:

    • Complex diseases arise from multiple genetic variations, challenging single nucleotide polymorphism (SNP) association studies.
    • Current genome-wide association studies (GWAS) often focus on individual genetic markers, limiting their success in explaining complex disease etiology.
    • Epistasis, the interaction between multiple genetic variants, is increasingly recognized as crucial for understanding disease development.

    Purpose of the Study:

    • To review computational methods for analyzing epistatic interactions in disease-related genetic data.
    • To explore the use of epistatic analysis for developing diagnostic biomarkers.
    • To discuss future directions in genetic analysis, considering advances in sequencing and non-coding variants.

    Main Methods:

    • Review of computational approaches for analyzing gene-gene interactions (epistasis).
    • Focus on analyzing microarray data for epistatic interactions.
    • Consideration of high-performance computing and heuristic methods for managing large genomic datasets.

    Main Results:

    • Epistatic analysis presents a more effective strategy than single-marker analysis for complex diseases.
    • Computational methods are essential for analyzing the vast number of variant combinations.
    • Epistatic interactions hold potential for the development of novel diagnostic biomarkers.

    Conclusions:

    • Analyzing epistatic interactions is vital for understanding complex human diseases.
    • Advancements in computational power and sequencing technologies are enabling more sophisticated genetic analyses.
    • Future research should integrate epistatic analysis with non-coding variant data for comprehensive disease insights.