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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-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.
GWAS does not require the identification of the target gene involved in...
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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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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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Protein Networks02:26

Protein Networks

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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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Genomics02:02

Genomics

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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: Jun 15, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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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

  • 1Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.

Nucleic Acids Research
|August 22, 2024
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Summary
This summary is machine-generated.

Network medicine and NeEDL identify higher-order epistatic interactions (EIs) for polygenic diseases. This approach significantly enhances statistical power and biological relevance, accelerating biomedical research and improving disease risk prediction.

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Last Updated: Jun 15, 2025

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Network Medicine

Background:

  • Most heritable diseases are polygenic, involving complex genetic architectures.
  • Discovering clinically relevant epistatic interactions (EIs) between single nucleotide polymorphisms (SNPs) is crucial for understanding these diseases.
  • Current methods for EI detection are limited to pairs of SNPs due to computational complexity.

Purpose of the Study:

  • To develop a novel computational method, NeEDL (network-based epistasis detection via local search), for detecting higher-order EIs.
  • To leverage network medicine principles to enhance the identification and statistical significance of EIs.
  • To demonstrate the potential of quantum computing for accelerating computationally intensive tasks in genetic research.

Main Methods:

  • Implemented NeEDL, a network-based local search algorithm, to identify higher-order EIs.
  • Applied NeEDL to eight different diseases, analyzing interactions among multiple SNPs.
  • Integrated network medicine to guide the selection of statistically significant EIs.

Main Results:

  • NeEDL identified EIs that are an order of magnitude more statistically significant than existing methods.
  • Discovered average EIs consisting of five SNPs, providing deeper insights into polygenic disease architecture.
  • Identified known and novel disease-associated genes through SNP-based EIs, with reproducible results across independent cohorts.

Conclusions:

  • NeEDL effectively detects higher-order EIs with significant statistical and biological evidence.
  • The approach offers unique insights into polygenic diseases and supports the development of improved risk scores and combination therapies.
  • NeEDL highlights the potential of integrated quantum computing for accelerating biomedical research.