Association signals in GWAS are usually prioritized solely by p values. Here, we attempt to improve the power of GWAS by using a weighted false discovery rate control procedure to detect associations of low-frequency variants with effect sizes similar to or even larger than those of common variants. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to test for association with fasting glucose levels in the Atherosclerosis Risk in Communities Study (ARIC) population. In addition to finding several previously identified sequence variations, we identified a low-frequency variant (rs1209523; minor allele frequency = 0.043) near FOXA2 that was associated with fasting glucose levels in European Americans (EAs) (n = 7428, p value = 1.3 × 10-5). The association between rs1209523 and glucose levels was also significant in African Americans (AAs) (n = 2029, p value = 6.7 × 10-3) of the ARIC and was confirmed by replication in both EAs and AAs of the Dallas Heart Study (n = 963 and 1571, respectively; p values = 5.3 × 10-3 and 5.8 × 10-4, respectively) and in EAs of the Cooper Center Longitudinal Study (n = 2862; p value = 1.6 × 10-2). A meta-analysis of these five populations yielded an estimated effect size of -1.31 mg/dl per minor allele (p value = 2.2 × 10-11). This study reveals that there is a cache of less-frequent variants in GWAS arrays that can be identified via analytical approaches accounting for allele frequencies.
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