Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets

TLDR

Genome‑wide association studies rely on commercial microarrays that assay over a million SNPs, and imputation further expands the number of variants, but the large number of correlated markers due to linkage disequilibrium complicates multiple‑testing correction. The authors aim to develop a more accurate and computationally efficient method for estimating the effective number of independent markers (M_e) to adjust genome‑wide significance thresholds. They implemented the method in the free GEC software and applied it to 13 Illumina/Affymetrix arrays and to HapMap and 1000 Genomes reference panels to calculate M_e and the corresponding p‑value thresholds that control the genome‑wide type‑I error at 0.05. The analysis indicates that a p‑value threshold of ~10⁻⁷ is appropriate for early commercial arrays, ~5×10⁻⁸ for current or merged arrays, ~10⁻⁸ for all common SNPs in the 1000 Genomes dataset, and ~5×10⁻⁸ for common SNPs within genes.

Abstract

Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M e) for the adjustment of multiple testing, but current methods of calculation for M e are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M e. Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M e, and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10−7 as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10−8 for current or merged commercial genotyping arrays, ~10−8 for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10−8 for the common SNPs only within genes.