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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

Updated: May 7, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Single nucleotide polymorphism network: a combinatorial paradigm for risk prediction.

Puspita Das Roy1, Dhriti Sengupta, Anjan Kr Dasgupta

  • 1Department of Biochemistry, University of Calcutta, Kolkata, West Bengal, India.

Plos One
|September 17, 2013
PubMed
Summary

This study introduces a novel network-based approach for disease risk prediction using combined genotype data. The method effectively classifies individuals into disease risk groups by analyzing genotype combinations and identifying key genetic markers.

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09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Disease Risk Prediction

Background:

  • Traditional SNP genotyping ranks single nucleotide polymorphisms (SNPs) for disease association.
  • Existing methods often analyze individual alleles or haplotypes, limiting comprehensive risk assessment.
  • A need exists for advanced methods to integrate multi-locus genotypic data for improved disease prediction.

Purpose of the Study:

  • To develop and validate a network representation approach for disease risk prediction using combined genotypic data.
  • To classify individuals into diseased and control groups based on genotype combinations.
  • To identify specific genotype combinations and polymorphisms associated with disease risk.

Main Methods:

  • Constructed genotype-sets from combinations of genotypes at five independent loci on P2RY1 and P2RY12 genes.
  • Utilized a network architecture with super-nodes (case/control) and individual nodes, each represented by a set of M genotypes.
  • Analyzed derived networks with M-1 markers to assess the variability in case-specific and control-specific connections.

Main Results:

  • The network analysis demonstrated perfect segregation between case and control super-nodes.
  • A small fraction of individuals with common genotype sets connected to both super-nodes were identified.
  • The approach was successfully applied to a separate oral cancer case-control study, improving data presentation and interpretation.

Conclusions:

  • This network-based method offers a powerful tool for classifying individuals into disease risk groups based on combined genotypic data.
  • The approach facilitates the identification of critical polymorphisms influencing disease prevalence within a population.
  • The findings suggest a promising direction for personalized risk assessment and genetic marker discovery.