Calibrating genome wide significance by minor allele frequency across three major populations

Conventional genome-wide association study (GWAS) thresholds, notably 5 × 10⁻⁸, were established under assumptions that may not hold across diverse populations and whole-genome sequencing (WGS) analyses. Given the complex linkage disequilibrium structure of the human genome, a single fixed threshold risks inadequate type I error control. Here, we sought to derive minor allele frequency (MAF)-specific, population-tailored significance thresholds using the Li-Ji method across European, African, and Asian cohorts from the 1000 Genomes Project. We partitioned the genome into natural linkage disequilibrium (LD) blocks defined by the LDetect database and applied rigorous quality control measures before generating LD matrices. Using the Li-Ji method-we estimated the effective number of independent tests for each block across six MAF thresholds and then aggregated the results for each population. The resulting effective tests were used to calculate Bonferroni-adjusted significance thresholds. Our analysis revealed that for common variants (MAF ≥ 0.05), the significance thresholds in European and Asian populations were somewhat lower than the conventional 5 × 10⁻⁸ benchmark, whereas the African population required considerably more stringent corrections. The inclusion of rarer variants further increased the effective number of independent tests across all groups, thereby shifting the significance thresholds to levels even more stringent than the 5 × 10⁻⁸ benchmark. By applying the Li-Ji method, this study establishes that MAF-specific and population-specific significance thresholds provide a more accurate framework for GWAS analyses. Our findings suggest that the conventional 5 × 10⁻⁸ threshold may be suboptimal, particularly when evaluating rare variants or diverse populations, with important implications for future biobank-scale and precision genomic research.