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Genome-wide Association Studies-GWAS

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...

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Related Experiment Video

Updated: Jun 15, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Quality control for genome-wide association studies.

Michael E Weale¹

¹Department of Medical and Molecular Genetics, King's College London, Guy's Hospital, London, UK

Methods in Molecular Biology (Clifton, N.J.)

|March 19, 2010

Summary

This review details quality control (QC) for SNP genotyping in genome-wide association studies (GWAS). It emphasizes intelligent QC methods for individuals and SNPs to ensure reliable genetic data.

Area of Science:

Genetics
Bioinformatics
Statistical genomics

Background:

Genome-wide association studies (GWAS) rely on high-quality genotype data.
Single nucleotide polymorphism (SNP) genotyping panels are crucial for GWAS.
Effective quality control (QC) is essential to mitigate biases and errors in genetic association studies.

Purpose of the Study:

To provide a comprehensive review of quality control (QC) methods for SNP-based genotyping panels.
To explain the rationale behind individual and SNP QC steps in GWAS.
To promote the use of intelligent QC approaches over arbitrary thresholds.

Main Methods:

Review of established QC procedures for individual data: missingness, gender, duplicates, cryptic relatedness, population outliers, heterozygosity, and inbreeding.

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Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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Last Updated: Jun 15, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Review of established QC procedures for SNP data: missingness, minor allele frequency (MAF), and Hardy-Weinberg equilibrium (HWE).

Discussion on the importance of understanding the biological and statistical basis for each QC step.

Main Results:

Detailed outline of QC metrics for assessing individual and SNP data quality.
Emphasis on data-driven, intelligent QC strategies tailored to specific datasets.
Availability of scripts and code to facilitate the implementation of these QC methods.

Conclusions:

Rigorous QC is paramount for the validity of GWAS findings.
Intelligent QC approaches enhance the reliability and interpretability of genetic association results.
Standardized QC protocols, supported by accessible code, improve the reproducibility of GWAS.