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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...
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%...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

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Related Experiment Video

Updated: Jul 10, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Genome-wide selection of tag SNPs using multiple-marker correlation.

K Hao1

  • 1Algorithm and Data Analysis, Affymetrix, Inc., 3420 Central Expressway, Santa Clara, California, USA. ke_hao@163.com

Bioinformatics (Oxford, England)
|November 17, 2007
PubMed
Summary

A new algorithm improves tag SNP selection for genome-wide association studies by using multiple-marker linkage disequilibrium (LD). This method enhances genetic coverage and reduces genotyping costs compared to traditional pairwise LD approaches.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Tag SNP selection is crucial for genome-wide association studies (GWAS).
  • Current methods primarily rely on pairwise (two-marker) linkage disequilibrium (LD).
  • Multiple-marker LD offers additional information for improved genetic coverage.

Purpose of the Study:

  • To develop a novel algorithm for tag SNP selection incorporating multiple-marker LD.
  • To improve genetic coverage and statistical power in GWAS.
  • To reduce genotyping costs.

Main Methods:

  • An iterative algorithm selects tag SNPs by identifying those capturing the most neighboring SNPs via pairwise and multiple-marker LD.
  • The algorithm and associated software (multiTag) were optimized for efficiency.
  • Performance was benchmarked using HapMap release 21 data.

Main Results:

  • The novel algorithm demonstrated superior genetic coverage (7.2% improvement in HapMap CEU for 200K tag SNPs) compared to pairwise methods.
  • Significant cost savings in genotyping (34.1% in HapMap CEU at 90% coverage) were achieved.
  • Tag SNPs identified showed good portability across ethnic groups and higher statistical power in association tests.

Conclusions:

  • The proposed algorithm effectively utilizes multiple-marker LD for superior tag SNP selection.
  • The multiTag software provides a practical and efficient tool for genetic research.
  • This approach offers enhanced genetic coverage, cost-efficiency, and statistical power for GWAS.