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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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.
GWAS does not require the identification of the target gene involved in...
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Infinium Assay for Large-scale SNP Genotyping Applications
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Quality Control Procedures for Genome-Wide Association Studies.

Van Q Truong1,2, Jakob A Woerner1,2, Tess A Cherlin3

  • 1Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Current Protocols
|November 28, 2022
PubMed
Summary

Quality control for genome-wide association studies (GWAS) is crucial for accurate results. This study addresses challenges in GWAS data quality, offering best practices for genotyped and imputed data to minimize bias.

Keywords:
1000 Genomes ProjectGWASbiobankselectronic health records (EHR)genome-wide association studiesgenomicsgenotype imputationquality control (QC)

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Area of Science:

  • Genomics and Genetic Epidemiology
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are vital for understanding complex disease pathophysiology.
  • The accuracy of GWAS findings is directly dependent on the quality of the underlying genetic data.
  • Evolving quality control (QC) procedures are essential for robust GWAS.

Purpose of the Study:

  • To identify and address challenges in the quality control of genotyped and imputed GWAS data.
  • To provide guidelines and best practices for minimizing bias and errors in GWAS results.
  • To offer a sample dataset and discuss future research directions in GWAS QC.

Main Methods:

  • Enumeration of common issues in GWAS data QC, including file formats, software, sex chromosome anomalies, sample identity, relatedness, population substructure, batch effects, and marker quality.
  • Description of genotype imputation and post-imputation quality assurance approaches.
  • Development of detailed guidelines and provision of a sample dataset for best practice demonstration.

Main Results:

  • Identification of critical QC challenges in both genotyped and imputed GWAS datasets.
  • Demonstration of methods to mitigate potential bias and errors through imputation and quality assurance.
  • Establishment of current best practices for GWAS data QC.

Conclusions:

  • Rigorous quality control is indispensable for the reliable interpretation of GWAS findings.
  • Adherence to recommended QC procedures, including imputation and post-imputation QA, enhances data integrity.
  • Ongoing research is needed to further refine GWAS QC methodologies.