A Powerful Method for Including Genotype Uncertainty in Tests of Hardy-Weinberg Equilibrium

Andrew Beck¹, Alexander Luedtke², Keli Liu³, Nathan Tintle⁴

¹Department of Biostatistics, University of Michigan
²Department of Biostatistics, University of California- Berkeley
³Department of Statistics, Harvard University
⁴Department of Mathematics, Statistics, and Computer Science, Dordt College
Email: beckandy@umich.edu, aluedtke@berkeley.edu, kliu@college.harvard.edu, Nathan.Tintle@dordt.edu

Pacific Symposium on Biocomputing 22:368-379(2017)

© 2017 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

The use of posterior probabilities to summarize genotype uncertainty is pervasive across genotype, sequencing and imputation platforms. Prior work in many contexts has shown the utility of incorporating genotype uncertainty (posterior probabilities) in downstream statistical tests. Typical approaches to incorporating genotype uncertainty when testing Hardy-Weinberg equilibrium tend to lack calibration in the type I error rate, especially as genotype uncertainty increases. We propose a new approach in the spirit of genomic control that properly calibrates the type I error rate, while yielding improved power to detect deviations from Hardy-Weinberg Equilibrium. We demonstrate the improved performance of our method on both simulated and real genotypes.