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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%...
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
Pharmacogenomics: Identification of New Drug Targets01:29

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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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Related Experiment Video

Updated: Jun 8, 2026

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor
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Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Published on: August 25, 2023

Functionally informative tag SNP selection using a Pareto-optimal approach.

Phil Hyoun Lee1, Jae-Yoon Jung, Hagit Shatkay

  • 1Center for Human Genetics Research, Department of Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02114, USA. phlee@pngu.mgh.harvard.edu

Advances in Experimental Medicine and Biology
|September 25, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multiobjective optimization framework to select single nucleotide polymorphism (SNP) markers that are both informative for association studies and functionally significant, improving genetic disease research.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Area of Science:

  • Genetics and Bioinformatics
  • Human Disease Genetics
  • Computational Biology

Background:

  • Selecting informative single nucleotide polymorphism (SNP) markers is crucial for genetic association studies to identify disease-related genes.
  • Current SNP selection methods, such as tag SNP and functional SNP selection, are often treated separately, limiting their combined potential.
  • Integrating these distinct approaches is necessary to enhance the discovery of genetic underpinnings of human diseases.

Purpose of the Study:

  • To develop a novel multiobjective optimization framework for selecting single nucleotide polymorphism (SNP) markers.
  • To identify SNPs that are simultaneously informative for tagging and possess functional significance (FS).
  • To improve the efficiency and effectiveness of SNP selection for genetic association studies.

Main Methods:

  • Development of a multiobjective optimization framework based on Pareto optimality.
  • Application of the algorithm to 34 lung cancer disease-susceptibility genes.
  • Comparative analysis against existing tag SNP and functional SNP selection systems.

Main Results:

  • The proposed framework successfully identifies SNPs with both high tagging informativeness and functional significance.
  • The algorithm consistently outperforms existing state-of-the-art systems in selecting superior SNP subsets.
  • Demonstrated improvement in both selection objectives compared to conventional methods.

Conclusions:

  • The novel multiobjective optimization framework offers a superior approach to SNP selection for genetic association studies.
  • Combining tag SNP and functional SNP selection criteria through Pareto optimality enhances the identification of disease-relevant genetic markers.
  • This integrated method provides a more powerful tool for uncovering the genetic basis of human diseases, exemplified by lung cancer susceptibility genes.