Chapter 16. Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies

  1. Andreas Scherer Founder/CEO of Spheromics
  1. Huixiao Hong1,
  2. Leming Shi1,
  3. James C Fuscoe1,
  4. Federico Goodsaid2,
  5. Donna Mendrick1 and
  6. Weida Tong1

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch16

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Hong, H., Shi, L., Fuscoe, J. C., Goodsaid, F., Mendrick, D. and Tong, W. (2009) Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch16

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. 1

    National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

  2. 2

    Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA

Publication History

  1. Published Online: 2 NOV 2009
  2. Published Print: 30 OCT 2009

ISBN Information

Print ISBN: 9780470741382

Online ISBN: 9780470685983

SEARCH

Keywords:

  • genome-wide association studies (GWAS);
  • single nucleotide polymorphisms (SNPs);
  • genotyping;
  • batch effect;
  • reproducibility;
  • genetic marker;
  • personalized medicine

Summary

Genome-wide association studies (GWAS) use dense maps of single nucleotide polymorphisms (SNPs) that cover the entire human genome to search for genetic markers with different allele frequencies between cases and controls. Given the complexity of GWAS, it is not surprising that only a small portion of associated SNPs in the initial GWAS results were successfully replicated in the same populations. Each of the steps in a GWAS has the potential to generate spurious associations. In addition, there are batch effects in the genotyping experiments and in genotype calling that can cause both Type I and Type II errors. Decreasing or eliminating the various sources of spurious associations and batch effects is vital for reliably translating GWAS findings to clinical practice and personalized medicine. Here we review and discuss the variety of sources of spurious associations and batch effects in GWAS and provide possible solutions to the problems.