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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.
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Related Experiment Video

Updated: Jun 19, 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

Exploratory data analysis in large-scale genetic studies.

Yik Y Teo1

  • 1Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. teo@well.ox.ac.uk

Biostatistics (Oxford, England)
|October 16, 2009
PubMed
Summary
This summary is machine-generated.

Exploratory data analysis (EDA) is vital for genome-wide association studies (GWAS) to ensure data quality. This review covers essential EDA tools for large-scale genetic datasets, preventing compromised research outcomes.

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Last Updated: Jun 19, 2026

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Published on: July 27, 2021

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) are essential for understanding the genetic underpinnings of common diseases and complex traits.
  • These studies involve large-scale datasets with thousands of samples and millions of genetic variables.
  • Ensuring data integrity is paramount for the validity of GWAS findings.

Purpose of the Study:

  • To review current exploratory data analysis (EDA) tools used in genome-wide association studies (GWAS).
  • To highlight the importance of EDA in identifying problematic samples and genetic data quality issues.
  • To provide strategies for effective EDA in large-scale genetic research.

Main Methods:

  • Review of established and emerging numerical and graphical strategies for EDA in GWAS.
  • Discussion of techniques for identifying sample-related issues and poor genotype quality.
  • Focus on tools applicable to high-throughput genetic data.

Main Results:

  • EDA is a critical prerequisite for reliable genotype-phenotype association analysis in GWAS.
  • Specialized numerical and graphical tools are necessary for managing the scale of GWAS data.
  • Effective EDA safeguards against undetected errors that could skew research outcomes.

Conclusions:

  • The rigorous application of EDA is indispensable for the success of genome-wide association studies.
  • Adoption of appropriate exploratory tools enhances the reliability and reproducibility of genetic association findings.
  • This review serves as a guide to essential EDA practices in large-scale genetic research.