Association Tests that Accommodate Genotyping Uncertainty*

Thomas A. Louis, Benilton S. Carvalho, M. Daniele Fallin, Rafael A. Irizarryi, Qing Li and Ingo Ruczinski

in Bayesian Statistics 9

Published in print October 2011 | ISBN: 9780199694587
Published online January 2012 | e-ISBN: 9780191731921 | DOI:
Association Tests that Accommodate Genotyping Uncertainty*

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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.

Keywords: Association Studies; Single Nucleotide Polymorphism; Genotype Uncertainty; Bayesian Structuring and Ranking

Chapter.  13637 words.  Illustrated.

Subjects: Probability and Statistics

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