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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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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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Related Experiment Video

Updated: Jul 4, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions.

Fuxiang Ren1, Shiyin Li1, Zihao Wen2,3

  • 1College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.

Genes
|January 26, 2024
PubMed
Summary
This summary is machine-generated.

A new algorithm, SEMO, efficiently detects complex single-nucleotide polymorphism (SNP) interactions for disease susceptibility. It accurately identifies SNP combinations linked to breast cancer, improving upon existing methods.

Keywords:
biogenetic markersdisease modelsmulti-locus SNP interactionsmulti-objective optimizationsingle-nucleotide polymorphismsspherical evolutionary algorithms

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Single-nucleotide polymorphisms (SNPs) are vital biogenetic markers for understanding complex disease susceptibility and pathogenesis.
  • Identifying high-dimensional SNP interactions is computationally challenging due to combinatorial search inefficiencies.

Purpose of the Study:

  • To introduce the spherical evolutionary multi-objective (SEMO) algorithm for efficient detection of multi-locus SNP interactions.
  • To evaluate SEMO's performance against state-of-the-art algorithms in identifying SNP associations.

Main Methods:

  • The SEMO algorithm employs a spherical search factor and historical memory feedback for balanced search and acquisition.
  • A multi-objective fitness function combining K2-Score and LR-Score evaluates SNP associations during evolutionary iterations.

Main Results:

  • SEMO demonstrated superior performance, detecting SNP interactions faster and more accurately than six comparative algorithms on simulated data.
  • Application to the WTCCC breast cancer dataset identified significant two- and three-point SNP interactions associated with breast cancer.

Conclusions:

  • The SEMO algorithm effectively identifies complex SNP interactions with high speed and accuracy.
  • SEMO's ability to detect novel SNP combinations offers a new approach for breast cancer research and genetic marker discovery.