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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.
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Data quality control in genetic case-control association studies.

Carl A Anderson1, Fredrik H Pettersson, Geraldine M Clarke

  • 1Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. carl.anderson@sanger.ac.uk

Nature Protocols
|November 19, 2010
PubMed
Summary
This summary is machine-generated.

This protocol outlines essential data quality control steps for case-control association studies. It details using PLINK and SMARTPCA to remove biased DNA samples and markers, ensuring reliable genetic association findings.

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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

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Case-control association studies are crucial for identifying genetic links to diseases.
  • Robust data quality control is a prerequisite for accurate genetic association analysis.
  • Previous protocols have addressed study design and marker selection.

Purpose of the Study:

  • To provide a detailed protocol for data quality assessment and control in case-control association studies.
  • To describe practical steps for identifying and removing biased DNA samples and genetic markers.
  • To ensure the reliability and validity of genetic association findings.

Main Methods:

  • Utilizing PLINK software for assessing failure rates per individual and SNP, and evaluating genetic relatedness.
  • Employing SMARTPCA software to identify and manage ancestral outliers within the dataset.
  • Implementing systematic procedures for DNA sample and marker quality control.

Main Results:

  • The protocol enables the identification and removal of low-quality DNA samples and problematic genetic markers.
  • It facilitates the detection of potential biases arising from sample relatedness or ancestral heterogeneity.
  • Successful implementation ensures a high-quality dataset for subsequent statistical association testing.

Conclusions:

  • Effective data quality control is paramount for the integrity of case-control association studies.
  • The described protocol provides a user-friendly, efficient, and reliable method for data preprocessing.
  • Adherence to these quality control measures is essential before performing statistical tests for disease association.