U-PASS and Beyond: Statistical Power Analysis and Genetic Modeling of Qualitative Traits in GWAS
DOI:
https://doi.org/10.66238/fsrma64Keywords:
Genome-Wide Association Studies, Statistical Power, Qualitative Traits, Genetic ModelingAbstract
Genome-wide association studies have fundamentally transformed our understanding of the genetic architectures underlying complex human diseases. Despite these advances, the accurate estimation of statistical power for qualitative traits remains a persistent challenge, particularly when dealing with rare variants and unbalanced case-control ratios. The Unified Power analysis of Association Studies software framework was developed to address these issues, offering a cohesive platform for evaluating statistical power across diverse genetic models. This paper presents a comprehensive examination of the theoretical foundations and practical applications of this unified framework, extending its principles to more complex genetic modeling scenarios. By systematically dissecting the statistical assumptions inherent in qualitative trait analysis, we elucidate the limitations of traditional power calculation methodologies and propose refined analytical extensions. Our investigation utilizes extensive simulated datasets to evaluate the performance of these extended models against standard baseline configurations. The findings demonstrate that integrating generalized linear mixed models with advanced liability threshold assumptions significantly enhances power estimation accuracy for low-frequency variants. Furthermore, this study provides detailed methodological guidelines for designing future genetic association studies, emphasizing the necessity of precise parameter specification to minimize false negative rates. Ultimately, this work contributes to the optimization of genomic research designs, ensuring robust variant discovery in the ongoing pursuit of precision medicine.
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