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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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Related Experiment Video

Updated: Jul 6, 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

Study designs for genome-wide association studies.

Peter Kraft1, David G Cox

  • 1Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.

Advances in Genetics
|March 25, 2008
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) are feasible due to advances in genotyping. This review covers design principles, genotyping arrays, and power calculations for cost-effective multistage GWAS, while noting design limitations.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genetics

Background:

  • High-throughput genotyping and large-scale human genetic variation data from projects like the Human Genome and HapMap have enabled genome-wide association studies (GWAS).
  • Designing effective GWAS presents challenges in avoiding systematic biases and achieving adequate statistical power to detect modest genetic associations.
  • The high cost of genome-wide genotyping for large sample sizes necessitates exploring efficient study designs.

Purpose of the Study:

  • To review fundamental design principles for genetic association studies.
  • To discuss the characteristics of fixed genome-wide and custom genotyping arrays relevant to study design.
  • To present a framework and tools for statistical power calculations in GWAS.

Main Methods:

  • Review of existing literature on genetic association study design.
  • Analysis of genotyping array properties (fixed genome-wide and custom arrays).
  • Development of a theoretical framework and practical tools for power calculations.

Main Results:

  • Multistage designs offer a cost-saving strategy for large-scale genetic studies.
  • Understanding genotyping array properties is crucial for optimizing study design.
  • Power calculation tools are essential for determining sample size and study feasibility.

Conclusions:

  • Multistage designs are a viable approach to mitigate costs in genome-wide association studies.
  • Careful consideration of design principles and genotyping strategies is necessary for robust GWAS.
  • Limitations of multistage designs require careful evaluation in study planning.