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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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Comparing Copy Number Variations and SNPs02:26

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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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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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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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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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Related Experiment Video

Updated: Mar 9, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data.

Suneetha Uppu, Aneesh Krishna, Raj P Gopalan

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 7, 2017
    PubMed
    Summary

    This review explores data mining and machine learning methods for detecting interactions between single nucleotide polymorphisms (SNPs) that influence complex diseases, addressing computational challenges in genetic epidemiology.

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    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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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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    Area of Science:

    • Genetics
    • Computational Biology
    • Bioinformatics

    Background:

    • Genome-wide association studies (GWAS) are advancing the understanding of complex disease genetics.
    • High-throughput technologies enable large-scale genetic epidemiological analysis, identifying disease-susceptibility single nucleotide polymorphisms (SNPs).
    • Interactions between SNPs are crucial for complex diseases but pose mathematical and computational challenges.

    Purpose of the Study:

    • To review current data mining and machine learning methods for detecting SNP interactions.
    • To discuss software packages available for SNP interaction analysis.
    • To address considerations for developing robust analytical models.

    Main Methods:

    • Review of existing literature on data mining and machine learning approaches for SNP interaction detection.
    • Analysis of computational complexity and mathematical challenges in interaction studies.
    • Evaluation of data simulation techniques for model performance assessment.

    Main Results:

    • Identified various data mining and machine learning methods applicable to SNP interaction analysis.
    • Highlighted challenges in computational complexity and mathematical modeling.
    • Reviewed data simulation strategies for performance evaluation.

    Conclusions:

    • Data mining and machine learning offer solutions to computationally complex SNP interaction studies.
    • Careful consideration of model development issues is essential for accurate results.
    • Future directions in SNP interaction analysis are discussed, emphasizing advancements in computational approaches.