How to link call rate and $p$-values for Hardy-Weinberg equilibrium as measures of genome-wide SNP data quality
We study the link between two quality measures of SNP (single nucleotide polymorphism) data in genome-wide association (GWA) studies, that is, per SNP call rates (CR) and p-values for testing Hardy–Weinberg equilibrium (HWE). The aim is to improve these measures by applying methods based on realized randomized p-values, the false discovery rate and estimates for the proportion of false hypotheses. While exact non-randomized conditional p-values for testing HWE cannot be recommended for estimating the proportion of false hypotheses, their realized randomized counterparts should be used. P-values corresponding to the asymptotic unconditional chi-square test lead to reasonable estimates only if SNPs with low minor allele frequency are excluded. We provide an algorithm to compute the probability that SNPs violate HWE given the observed CR, which yields an improved measure of data quality. The proposed methods are applied to SNP data from the KORA (Cooperative Health Research in the Region of Augsburg, Southern Germany) 500K project, a GWA study in a population-based sample genotyped by Affymetrix GeneChip 500K arrays using the calling algorithm BRLMM 1.4.0. We show that all SNPs with CR = 100 per cent are nearly in perfect HWE which militates in favor of the population to meet the conditions required for HWE at least for these SNPs. Moreover, we show that the proportion of SNPs not being in HWE increases with decreasing CR. We conclude that using a single threshold for judging HWE p-values without taking the CR into account is problematic. Instead we recommend a stratified analysis with respect to CR.