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Genome-wide Association Studies-GWAS

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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 21, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

A genome-wide association study primer for clinicians.

Tzu-Hao Wang¹, Hsin-Shih Wang

¹Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital and Chang Gung University, Tao-Yuan, Taiwan.

Taiwanese Journal of Obstetrics & Gynecology

|July 4, 2009

Summary

Genome-wide association studies (GWAS) link genetic markers to diseases. Clinician input is crucial for accurate genotype-disease association studies, especially in diagnosis and control selection.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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

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Area of Science:

Genetics
Genomics
Clinical Research

Background:

Genome-wide association studies (GWAS) are powerful tools for identifying genetic variants associated with diseases.
High-throughput genotyping technology enables the examination of hundreds of thousands of genetic markers.

Purpose of the Study:

To provide clinicians with an introduction to GWAS.
To enhance understanding of the value and limitations of GWAS in genotype-disease association studies.

Main Methods:

Review of existing literature and methodologies in GWAS.
Emphasis on the role of clinical expertise in interpreting genetic associations.

Main Results:

GWAS can reveal significant genotype-phenotype correlations.
Clinical judgment is essential to mitigate confounding factors like disease heterogeneity.

Conclusions:

Clinicians play a vital role in ensuring the accuracy of GWAS by correctly identifying cases and disease-free controls.
Effective collaboration between geneticists and clinicians is key for successful genotype-disease association studies.