Association Tests that Accommodate Genotyping Uncertainty

Thomas A. Louis, Benilton S. Carvalho, M. Daniele Fallin, Rafael A. Irizarryi, Qing Li, Ingo Ruczinski, Vanja Dukić, Ken Rice

Research output: Chapter in Book/Report/Conference proceedingChapter


High-throughput single nucleotide polymorphism (SNP) arrays, typically used in genome-wide association studies with a trait of interest, provide estimates of genotypes for up to several million loci. Most genotype estimates are very accurate, but genotyping errors do occur and can influence test statistics, p-values and ranks. Some SNPs are harder to call than others due to probe properties and other technical/biological factors; uncertainties can be associated with features of interest. SNP- and case-specific genotype posterior probabilities are available, but they are typically not used or used only informally, for example by setting aside the most uncertain calls. To improve on these approaches we take full advantage of Bayesian structuring and develop an analytic framework that accommodates genotype uncertainties. We show that the power of a score test (and statistical information more generally) is directly a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that compared to picking a single AA, AB or BB genotype or to setting aside difficult calls, Bayesian structuring can substantially increase statistical information for detecting a true association and for ranking SNPs, whether the ranking be frequentist or optimal Bayes. This improvement is primarily associated with genotypes that are difficult to call.

Original languageEnglish (US)
Title of host publicationBayesian Statistics 9
PublisherOxford University Press
ISBN (Electronic)9780191731921
ISBN (Print)9780199694587
StatePublished - Jan 19 2012


  • Association studies
  • Bayesian structuring and ranking
  • Genotype uncertainty
  • Single nucleotide polymorphism

ASJC Scopus subject areas

  • Mathematics(all)


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