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Related Concept Videos

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%...
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,...
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...
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...
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...

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Related Experiment Video

Updated: Jul 16, 2026

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
05:51

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

Informative SNP selection methods based on SNP prediction.

Jingwu He1, Alexander Zelikovsky

  • 1Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. jingwu@cs.gsu.edu

IEEE Transactions on Nanobioscience
|March 31, 2007
PubMed
Summary

Selecting informative single nucleotide polymorphisms (SNPs), or tag SNPs, is crucial for disease association studies. This research introduces new algorithms for tag SNP selection, optimizing prediction accuracy and reducing the number of SNPs needed.

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

Published on: April 4, 2018

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Last Updated: Jul 16, 2026

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
05:51

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

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

Background:

  • Identifying associations between complex diseases and genetic variations like single nucleotide polymorphisms (SNPs) is a key area of research.
  • Efficiently selecting a subset of informative SNPs, known as tag SNPs, is essential for cost-effective genotyping and large-scale haplotype analysis.

Purpose of the Study:

  • To demonstrate that tag SNP selection is dependent on the chosen SNP prediction method.
  • To develop and evaluate novel algorithms for tag SNP selection and SNP prediction.
  • To improve the efficiency and accuracy of tag SNP selection for genetic studies.

Main Methods:

  • Proposed greedy and local-minimization algorithms for tag SNP selection.
  • Introduced two new SNP prediction approaches: multiple linear regression (MLR) and support vector machines (SVMs).
  • Conducted extensive experimental studies on various datasets, including ten regions from the HapMap project.

Main Results:

  • The MLR prediction method combined with stepwise tag selection requires fewer tags than existing state-of-the-art methods (e.g., Halperin et al.).
  • The MLR-based approach uses approximately 30% fewer tags than IdSelect for statistically covering all SNPs.
  • SVM-based tag selection achieves comparable prediction accuracy to existing methods while using fewer tags.

Conclusions:

  • Tag SNP selection strategies must be optimized in conjunction with specific SNP prediction methods.
  • The proposed MLR and SVM-based methods offer more efficient tag SNP selection compared to current approaches.
  • These findings contribute to more cost-effective and accurate genetic association studies for complex diseases.