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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...

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Updated: May 27, 2026

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

Published on: June 21, 2018

A robust clustering algorithm for identifying problematic samples in genome-wide association studies.

Céline Bellenguez1, Amy Strange, Colin Freeman

  • 1Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.

Bioinformatics (Oxford, England)
|November 8, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a robust statistical algorithm for identifying atypical genome-wide variation summaries in individuals. This quality control tool enhances data accuracy for large-scale genetic studies like genome-wide association studies (GWAS).

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Published on: July 27, 2021

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Last Updated: May 27, 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

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:

  • Genomics
  • Statistical Genetics

Background:

  • High-throughput genotyping arrays enable efficient single nucleotide polymorphism (SNP) surveying across genomes.
  • Genome-wide association studies (GWAS) often utilize statistical models for genotype frequencies.
  • Sample collection and experimental errors can introduce biases and artifacts into genotype data.

Purpose of the Study:

  • To develop a robust statistical algorithm for identifying samples with atypical genome-wide variation summaries.
  • To provide a semi-automated quality control tool for genomic data analysis.
  • To address challenges in genotype data accuracy arising from experimental complexities.

Main Methods:

  • Development of a simple, robust statistical algorithm.
  • Utilizing summary statistics to identify potential data problems.
  • Application to two different genotyping platforms and sample collections.

Main Results:

  • Demonstration of the algorithm as a semi-automated quality control tool.
  • Identification of samples with atypical genome-wide variation summaries.
  • Successful application across diverse genotyping platforms and sample sets.

Conclusions:

  • The developed algorithm offers an efficient method for surveying SNPs in large populations.
  • It serves as a valuable quality control tool to improve data reliability in GWAS and similar studies.
  • The algorithm effectively identifies and helps mitigate biases and artifacts in genotype data.