A POWERFUL METHOD FOR INCLUDING GENOTYPE UNCERTAINTY IN TESTS OF HARDY-WEINBERG EQUILIBRIUM

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.

Figure 1. Type I error rate for three different HWE testing methods across different uncertainty levels

Type I error rate is shown across different r² settings for three different HWE testing approaches at the 1% significance level. SNPs in the low minor allele frequency range are depicted (MAF between 0.05 and 0.1)

Figure 2. Power for two different approaches to HWE testing across different uncertainty levels

Power is illustrated across different r² settings for two different HWE testing approaches at the 1% significance level, with a horizontal line at the power of a test using the real genotypes. Power for SNPs with MAF between 0.1 and 0.2 are depicted, when the observed SNP is a mix of two subgroups of individuals where the difference in MAF between the two subgroups is between 0.1 and 0.2, and the 10% of the individuals are from one subgroup and 90% from the other.