An Advanced Entropy Approach for Minimizing False Discoveries in Imputation-Based Association Analyses

Genotype imputation is a cornerstone of modern genetic studies, enhancing the resolution of genome-wide association studies (GWAS), fine mapping, and polygenic risk score estimation by inferring untyped variants using reference panels. The output of imputation is a set of probabilistic genotypes, each associated with an inherent degree of uncertainty. However, conventional downstream analyses often overlook this uncertainty, relying instead on allelic dosages-expected allele counts computed from probabilistic genotypes-as proxies. This practice can be misleading, as distinct genotype probability distributions may produce identical dosages despite vastly different confidence levels, potentially introducing bias and inflating false discoveries. To address this limitation, we introduce an entropy-weighted association method that explicitly quantifies imputation uncertainty using Shannon entropy. These entropy values are integrated as observation-level weights within the association model, allowing the method to dynamically account for the reliability of each imputed genotype. Through simulation studies, we demonstrate that this approach substantially reduces false positives, especially when genotypic uncertainty is pronounced. Our findings highlight the importance of modeling imputation uncertainty and offer a framework that improves the robustness of GWAS and other genotype imputation-dependent analyses.