Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies

Abstract

With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of effect size dilution, from phenotypic misclassification or environmental confounders, on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, that leverages GWAS summary statistics to quantify genome-wide effect size dilution across different phenotypic definitions and cohorts . We formulate conditions illustrating how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth. We apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We then present multiple downstream applications, where dilution, irrespective of the exact cause, biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to flag potential data quality issues and harmonize differences in effect size distributions across GWAS.