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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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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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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Detecting Disease-Associated SNP-SNP Interactions Using Progressive Screening Memetic Algorithm.

Boxin Guan, Yuhai Zhao, Ying Yin

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    Summary
    This summary is machine-generated.

    This study introduces a novel Progressive Screening Memetic Algorithm (PSMA) to efficiently detect disease-associated single nucleotide polymorphism (SNP) interactions. PSMA overcomes limitations of existing methods by evaluating each SNP-SNP interaction only once, improving accuracy and speed in genetic studies.

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    Area of Science:

    • Genetics
    • Bioinformatics
    • Computational Biology

    Background:

    • Genome-wide association studies (GWAS) utilize millions of single nucleotide polymorphisms (SNPs) to identify genetic disease associations.
    • Detecting SNP-SNP interactions is crucial for understanding complex disease genetics, but the search space is combinatorially vast.
    • Existing evolutionary algorithms for SNP-SNP interaction detection suffer from redundant evaluations and can converge to local optima.

    Purpose of the Study:

    • To develop an efficient and accurate algorithm for detecting disease-associated SNP-SNP interactions.
    • To address the computational challenges and local optima issues in current evolutionary algorithms for this task.

    Main Methods:

    • A Progressive Screening Memetic Algorithm (PSMA) was developed, representing all potential SNP-SNP interactions in a graph.
    • PSMA employs a progressive screening strategy to ensure each SNP-SNP interaction is evaluated only once, reducing the graph.
    • Two local search algorithms were integrated to enhance the detection power of PSMA.

    Main Results:

    • The proposed PSMA method demonstrated superior performance compared to existing state-of-the-art methods.
    • PSMA achieved higher accuracy in detecting disease-associated SNP-SNP interactions.
    • PSMA significantly reduced computation time, indicating improved efficiency.

    Conclusions:

    • PSMA offers an effective solution for identifying complex disease-associated SNP-SNP interactions.
    • The algorithm's unique screening strategy enhances computational efficiency and avoids local optima.
    • PSMA represents a significant advancement in the genetic analysis of complex diseases.