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ASSOCIATON TESTS THAT ACCOMMODATE GENOTYPING ERRORS

• Ingo Ruczinski, Johns Hopkins University, Bloombeg School of Public Health, Department of Biostatistics
• Qing Li, Johns Hopkins University, Bloomberg School of Public Health, Department of Biostatistics
• Benilton Carvalho, Johns Hopkins University, Bloomberg School of Public Health, Department of Biostatistics
• M. Daniele Fallin, Johns Hopkins University, Bloomberg School of Public Health, Department of Epidemiology
• Rafael A. Irizarry, Johns Hopkins University, Bloomberg School of Public Health, Department of Biostatistics
• Thomas A. Louis, Johns Hopkins University Bloomberg School of Public Health, Department of Biostatistics

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Abstract:

High-throughput SNP arrays provide estimates of genotypes for up to one million loci, often used in genome-wide association studies. While these estimates are typically very accurate, genotyping errors do occur, which can influence in particular the most extreme test statistics and p-values. Estimates for the genotype uncertainties are also available, although typically ignored. In this manuscript, we develop a framework to incorporate these genotype uncertainties in case-control studies for any genetic model. We verify that using the assumption of a “local alternative” in the score test is very reasonable for effect sizes typically seen in SNP association studies, and show that the power of the score test is simply a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that the power to detect a true association can be substantially increased for difficult to call genotypes, resulting in improved inference in association studies.

Subject Area:

Genetics

Suggested Citation:

Ingo Ruczinski, Qing Li, Benilton Carvalho, M. Daniele Fallin, Rafael A. Irizarry, and Thomas A. Louis, "ASSOCIATON TESTS THAT ACCOMMODATE GENOTYPING ERRORS" (January 2009). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 181.
http://www.bepress.com/jhubiostat/paper181