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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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ACPA: automated cluster plot analysis of genotype data.

Arne Schillert1, Daniel F Schwarz, Maren Vens

  • 1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, 23538 Lübeck, Germany. Arne.Schillert@imbs.uni-luebeck.de.

BMC Proceedings
|December 19, 2009
PubMed
Summary

Automated Cluster Plot Analysis (ACPA) automates the quality control of single-nucleotide polymorphisms (SNPs) in genome-wide association studies. This algorithm effectively identifies low-quality SNPs, improving the reliability of genetic epidemiology research.

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Area of Science:

  • Genetic Epidemiology
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are standard tools in genetic epidemiology but present statistical challenges, including the problem of multiplicity.
  • Quality control filters, such as excluding single-nucleotide polymorphisms (SNPs) with high missing data, are crucial for reducing false positives.
  • Visual inspection of cluster plots is a vital quality control step but is often neglected due to its labor-intensive nature.

Purpose of the Study:

  • To develop and evaluate an automated algorithm for quality control of SNPs in GWAS.
  • To address the challenge of subjective and time-consuming manual inspection of cluster plots.
  • To improve the efficiency and objectivity of SNP quality assessment in large-scale genetic studies.

Main Methods:

  • Development of Automated Cluster Plot Analysis (ACPA), an algorithm for automatic quality control of autosomal SNPs.
  • ACPA identifies problematic SNPs by counting samples close to incorrect genotype clusters and excluding SNPs exceeding a defined threshold.
  • Evaluation involved 1,000 quality-controlled SNPs from the Framingham Heart Study, comparing ACPA's decisions with those of two independent human readers.

Main Results:

  • ACPA demonstrated a sensitivity of 88% (95% CI: 81%-93%) and a specificity of 86% (95% CI: 83%-89%) in identifying low-quality SNPs compared to human readers.
  • In a screening setting prioritizing the retention of good SNPs, ACPA achieved 99% (95% CI: 98%-100%) specificity.
  • ACPA successfully identified approximately half of the low-quality SNPs even in the screening setting.

Conclusions:

  • Automated Cluster Plot Analysis (ACPA) provides an effective and automated method for SNP quality control in GWAS.
  • The algorithm offers a valuable tool to complement or replace manual inspection of cluster plots, enhancing efficiency and objectivity.
  • ACPA improves the reliability of genetic epidemiology studies by ensuring higher quality SNP data.