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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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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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A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking

Shouheng Tuo1,2,3, Jiewei Jiang4

  • 1School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China. tuo_sh@126.com.

Interdisciplinary Sciences, Computational Life Sciences
|July 2, 2024
PubMed
Summary
This summary is machine-generated.

A new algorithm, MTHS-EE-DHEI, efficiently identifies high-order SNP epistatic interactions (HEIs) for complex disease genetics. It outperforms existing methods in detecting genetic susceptibility factors.

Keywords:
Explicit-encodingHigh-order SNP epistatic interactions (HEIs)Multitasking harmony search (MTHS)Single-nucleotide polymorphism (SNP)

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Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Complex diseases have a genetic basis influenced by single-nucleotide polymorphisms (SNPs).
  • Detecting high-order epistatic interactions (HEIs) among SNPs is challenging due to vast search spaces and computational complexity.
  • HEIs, though having small individual effects, can have significant joint effects on disease susceptibility.

Purpose of the Study:

  • To develop and evaluate a novel algorithm, MTHS-EE-DHEI, for efficient detection of high-order SNP epistatic interactions (HEIs).
  • To improve the identification of genetic factors contributing to complex disease susceptibility.
  • To provide a computationally efficient method for analyzing complex genetic interactions.

Main Methods:

  • Proposed a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI).
  • Employed a three-stage approach: harmony search with lightweight evaluation functions, G-test filtering, and machine learning validation (MDR, RF).
  • Evaluated performance on simulated and real-world datasets (AMD, RA, BC).

Main Results:

  • MTHS-EE-DHEI demonstrated superior detection power and computational efficiency compared to four state-of-the-art algorithms.
  • Successfully identified significant SNP combinations contributing to complex disease susceptibility.
  • Validated effectiveness on diverse datasets, including age-related macular degeneration, rheumatoid arthritis, and breast cancer.

Conclusions:

  • MTHS-EE-DHEI is an effective and efficient tool for discovering high-order SNP epistatic interactions (HEIs).
  • The algorithm offers valuable insights into the genetic architecture of complex diseases.
  • The findings contribute to advancing the understanding of genetic susceptibility in complex diseases.