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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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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.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Genome-wide Association Studies-GWAS01:11

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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.
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Updated: Nov 3, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Novel EDGE encoding method enhances ability to identify genetic interactions.

Molly A Hall1,2,3, John Wallace1, Anastasia M Lucas4

  • 1Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America.

Plos Genetics
|June 4, 2021
PubMed
Summary
This summary is machine-generated.

We introduce elastic data-driven genetic encoding (EDGE), a flexible method for analyzing genetic interactions between single nucleotide polymorphisms (SNPs). EDGE outperforms traditional methods in detecting SNP-SNP interactions and identifying novel associations, such as for age-related cataract.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Traditional genetic encoding methods (additive, dominant, recessive) for single nucleotide polymorphisms (SNPs) rely on assumptions about their genetic models.
  • SNPs genome-wide rarely share identical genetic models, complicating SNP-SNP interaction analyses and increasing the multiple testing burden when all encoding combinations are tested.

Purpose of the Study:

  • To present a novel, flexible genetic encoding method, elastic data-driven genetic encoding (EDGE), designed to overcome the limitations of traditional encodings for SNP-SNP interaction analysis.
  • To assess the power and accuracy of EDGE in detecting genetic interactions compared to traditional methods.

Main Methods:

  • Developed EDGE, which assigns heterozygous values to SNPs based on their observed genetic model in the dataset before interaction testing.
  • Evaluated EDGE's power using simulated genetic models across various minor allele frequencies (MAFs) and applied it to real genetic data from the eMERGE Network and UK Biobank for five phenotypes.

Main Results:

  • EDGE demonstrated superior power in detecting genetic interactions compared to traditional additive and dominant encodings across 10%, 30%, and 50% MAFs.
  • EDGE maintained a low false-positive rate, whereas traditional encodings showed inflated false positives.
  • EDGE identified a novel SNP-SNP interaction for age-related cataract (rs7787286-rs4695885) not detected by other methods.

Conclusions:

  • EDGE offers a flexible and powerful approach for detecting SNP-SNP interactions, particularly when SNPs exhibit diverse genetic models.
  • The method successfully identified a novel genetic interaction for age-related cataract, highlighting its potential in genetic association studies.
  • Recommends the adoption of EDGE for robustly identifying interactions between SNPs with varied modes of action.