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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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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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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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Single Nucleotide Polymorphisms-SNPs01:05

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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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Updated: Jul 8, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Markus Hoffmann1,2,3, Julian M Poschenrieder1,4, Massimiliano Incudini5

  • 1Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany.

Medrxiv : the Preprint Server for Health Sciences
|December 11, 2023
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Summary
This summary is machine-generated.

NeEDL identifies higher-order epistatic interactions (EIs) in polygenic diseases using network medicine. This approach significantly improves statistical power and biological relevance for genetic discovery.

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Area of Science:

  • Genetics
  • Computational Biology
  • Network Medicine

Background:

  • Most heritable diseases are polygenic, meaning multiple genes contribute to their development.
  • Understanding the genetic architecture requires identifying epistatic interactions (EIs) between single nucleotide polymorphisms (SNPs).
  • Current methods for EI detection are limited to pairs of SNPs due to computational complexity.

Approach:

  • NeEDL (network-based epistasis detection via local search) utilizes network medicine to identify higher-order EIs.
  • This approach enhances statistical significance by an order of magnitude compared to existing tools.
  • The method can be accelerated by quantum computing, addressing computational demands.

Key Points:

  • NeEDL discovers EIs involving an average of five SNPs, offering deeper insights into polygenic diseases.
  • The approach was applied to eight diseases, identifying known and novel disease-associated genes.
  • Results are reproducible across independent cohorts and accessible via the Epistasis Disease Atlas.

Conclusions:

  • NeEDL is the first application demonstrating quantum computing's potential to accelerate biomedical research.
  • The network medicine approach provides unprecedented statistical and biological evidence for higher-order EIs.
  • Findings offer a basis for improved polygenic disease risk scores and combination therapies.