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Related Concept Videos

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...
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,...

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Related Experiment Video

Updated: May 10, 2026

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

Quality control for genome-wide association studies.

Cedric Gondro1, Seung Hwan Lee, Hak Kyo Lee

  • 1The Centre for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia.

Methods in Molecular Biology (Clifton, N.J.)
|June 13, 2013
PubMed
Summary

This chapter details quality control (QC) for SNP genotyping in genome-wide association studies. It emphasizes automated QC pipelines using R for data integrity and informed downstream analysis.

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Infinium Assay for Large-scale SNP Genotyping Applications
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Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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Last Updated: May 10, 2026

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

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) rely on accurate single nucleotide polymorphism (SNP) genotyping.
  • Robust quality control (QC) is crucial for reliable GWAS results.

Purpose of the Study:

  • To overview QC issues in SNP-based genotyping for GWAS.
  • To present a practical, automated QC pipeline for genotype data.
  • To guide data integrity and downstream analytical decisions.

Main Methods:

  • Discussion of key genotype quality metrics.
  • Development of an automated QC pipeline using R.
  • Application of the pipeline to Illumina genotyping data.

Main Results:

  • A comprehensive QC pipeline from raw data to filtered datasets.
  • Emphasis on automated data storage, filtering, and manipulation.
  • Strategies for extracting global summaries from high-dimensional genotype data.

Conclusions:

  • Automated QC pipelines enhance data integrity in GWAS.
  • Informed analytical decisions are facilitated by effective QC.
  • R and specific platform pipelines (e.g., Illumina) are practical tools for QC.