A two-platform design for next generation genome-wide association studies

Joshua N. Sampson, Kevin Jacobs, Zhaoming Wang, Meredith Yeager, Stephen Chanock, Nilanjan Chatterjee

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Genome-wide association studies (GWAS) have been successful in their search for common genetic variants associated with complex traits and diseases. With new advances in array technologies together with available genetic reference sets, the next generation of GWAS will extend the search for associations with uncommon SNPs (1% ≤ MAF ≤ 10%). Two possible approaches are genotyping all participants, a prohibitively expensive option for large GWAS, or using a combination of genotyping and imputation. Here, we consider a two platform method that genotypes all participants on a standard genotyping array, designed to identify common variants, and then supplements that data by genotyping only a small proportion of the participants on a platform that has higher coverage for uncommon SNPs. This subset of the study population is then included as part of the imputation reference set. To demonstrate the use of this two-platform design, we evaluate its potential efficiency using a newly available dataset containing 756 individuals genotyped on both the Illumina Human OmniExpress and Omni2.5 Quad. Although genotyping all individuals on the denser array would be ideal, we find that genotyping only 100 individuals on this array, in combination with imputation, leads to only a modest loss of power for detecting associations. However, the loss of power due to imputation can be more substantial if the relative risks for rare variants are significantly larger than those previously observed for common variants.

Original languageEnglish (US)
Pages (from-to)401-409
Number of pages9
JournalGenetic Epidemiology
Issue number4
StatePublished - May 2012
Externally publishedYes


  • Case-control study
  • GWAS
  • Imputation
  • Omni2.5
  • Power
  • Study design

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology


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