Supplementary Material for: Two Adjustment Strategies for Imputation across Genotyping Arrays
datasetposted on 16.07.2014 by Xie Y., Hancock D.B., Johnson E.O., Rice J.P.
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Genotype imputation is a powerful approach in genome-wide association studies (GWAS) because it can provide higher resolution for associated regions and facilitate meta-analysis. However, bias can exist if different genotyping arrays are used and are unbalanced for case versus control subjects. The intersection imputation strategy [imputation based on single nucleotide polymorphisms (SNPs) available on all arrays] is a valid strategy that eliminates the bias caused by unbalanced genotyping, but achieved at the expense of reduced statistical power. In order to improve power in this situation, we introduce two new strategies: the replacement strategy based on the imputation quality score (IQS) ≥0.9 and the correction strategy. The IQS is a score that we have previously introduced based on Cohen's kappa of rater agreement. The replacement strategy with IQS ≥0.9 is a hybrid approach that utilizes measured genotypes for SNPs available on one or more of all arrays whenever the SNP has a high imputation quality (defined by IQS ≥0.9). The correction strategy combines measured genotypes as well as imputed and corrected genotype dosages for SNPs available on one or more of all arrays. The correction strategy yields a valid statistical test, while the replacement strategy with IQS ≥0.9 eliminates most spurious associations. Both strategies maintain statistical power.