Aggregating SNPs Improves Filtering for False Positive Associations Post-Imputation.

Abstract

Imputation causes bias in P-values in downstream genome-wide association studies. Imputation quality measures such as IMPUTE info are used to discriminate between false and true associations. However, implementing a high threshold often discards true associations, while a low threshold preserves false associations. This poses a challenge, especially for studies genotyped with SNP arrays. In practice, association signals register as spikes of low P-values for SNPs in close proximity owing to linkage disequilibrium, but post-imputation filtering is conducted on SNPs independently. We simulated 1536 small case-control studies on the human chromosome 19 both to quantify the introduced bias and to evaluate post-imputation filtering. The established IMPUTE info thresholds 0.3 and 0.8 were compared on individual SNPs and aggregated spikes in the formats 'best guess genotype' and 'dosage'. Furthermore, we applied two recently published methods, Iam hiQ and MagicalRsq, to assess their effect on filtering. We found differences in false signals and imputation quality between the genotype formats, especially in the midrange between thresholds. In this midrange, 51% and 60% of associated SNPs for best guess and dosage format, respectively, are true associations. For aggregated SNPs, the majority of spikes in the midrange are true associations. We propose a new method, the Midrange Filter, which uses both thresholds and formats to classify spikes instead of SNPs. This method discards up to the same number of false signals as the upper threshold, while preserving all true associations in most simulation settings. The PsyCourse study is included as a real data application.