Genome-wide association studies (GWAS) have led to the discovery of numerous single nucleotide polymorphisms (SNPs) associated with various phenotypes and complex diseases. However, the identified genetic variants do not fully explain the heritability of complex traits, known as the missing heritability problem. To address this challenge and accurately control false positives while maximizing true associations, we propose two approaches involving linkage disequilibrium (LD) scores as covariates. We apply principal component analysis (PCA), one of the dimensionality reduction techniques, to control the False Discovery Rate (FDR) in the presence of high-dimensional covariates. This method not only provides a convenient interpretation of how multiple covariates in high dimensions affect the control of FDR but also offers higher statistical power compared to cases where covariates are not used. Furthermore, we aim to investigate how covariates contribute to increasing the statistical power through various simulation experiments, comparing the results with real data examples to derive better interpretations. Using real-world datasets, including GWAS with Body Mass Index (BMI) as the phenotype, we evaluate the performance of our proposed approaches. By incorporating LD scores as covariates in FDR-controlled GWAS analyzes, we demonstrate their effectiveness in selecting informative LD scores and improving the identification of significant SNPs. Our methods alleviate computational burden and enhance interpretability while retaining essential information from LD scores. In general, our study contributes to the advancement of statistical methods in GWAS and provides practical guidance for researchers looking to improve the precision of genetic association analyses.

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