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Related Concept Videos

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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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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Epistasis01:39

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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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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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Updated: Oct 22, 2025

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Perspectives on Allele-Specific Expression.

Siobhan Cleary1, Cathal Seoighe1

  • 1School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;

Annual Review of Biomedical Data Science
|September 1, 2021
PubMed
Summary
This summary is machine-generated.

Genetic variation significantly impacts gene expression levels, leading to allelic imbalance. Understanding this imbalance is crucial for studying human diseases and complex traits.

Keywords:
RNA-seqallele-specific expressionallelic imbalancegenetic variation

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Area of Science:

  • Genomics
  • Population Genetics
  • Molecular Biology

Background:

  • Diploidy means two gene copies, but expression levels can differ.
  • Genomic imprinting and cis-acting variants cause unequal gene expression.
  • Allelic expression imbalance (AEI) influences genetic disease susceptibility.

Purpose of the Study:

  • To review insights from RNA sequencing data on genetic contributions to AEI.
  • To discuss tools and statistical models for analyzing gene expression imbalance.
  • To explore the role of AEI in human diseases and phenotypes.

Main Methods:

  • Analysis of large-scale RNA sequencing data across individuals and tissues.
  • Development and application of statistical models for AEI detection.
  • Integration of genetic variation data with gene expression profiles.

Main Results:

  • Genetic variation is a major driver of allelic expression imbalance.
  • RNA sequencing data provide unprecedented opportunities to study AEI.
  • AEI contributes to complex human diseases and phenotypes.

Conclusions:

  • Genetic factors substantially influence gene expression levels.
  • AEI analysis is key to understanding genetic disease mechanisms.
  • Further research into AEI can reveal novel therapeutic targets.