Recent discoveries of hundreds of common susceptibility SNPs from genome-wide association studies provide a unique opportunity to examine population genetic models for complex traits. In this report, we investigate distributions of various population genetic parameters and their interrelationships using estimates of allele frequencies and effect-size parameters for about 400 susceptibility SNPs across a spectrum of qualitative and quantitative traits. We calibrate our analysis by statistical power for detection of SNPs to account for overrepresentation of variants with larger effect sizes in currently known SNPs that are expected due to statistical power for discovery. Across all qualitative disease traits, minor alleles conferred "risk" more often than "protection." Across all traits, an inverse relationship existed between "regression effects" and allele frequencies. Both of these trends were remarkably strong for type I diabetes, a trait that is most likely to be influenced by selection, but were modest for other traits such as human height or late-onset diseases such as type II diabetes and cancers. Across all traits, the estimated effect-size distribution suggested the existence of increasingly large numbers of susceptibility SNPs with decreasingly small effects. For most traits, the set of SNPs with intermediate minor allele frequencies (5-20%) contained an unusually small number of susceptibility loci and explained a relatively small fraction of heritability compared with what would be expected from the distribution of SNPs in the general population. These trends could have several implications for future studies of common and uncommon variants.genetic prediction | missing heritability | population genetics L arge meta-analyses of genome-wide association studies (GWAS) have now identified more than 1,000 susceptibility loci for complex traits. Nevertheless, for most complex traits, the fraction of heritability explained by common variants remains below 10-15%, even for traits for which large numbers of loci have been detected [e.g., dozens to over 200 (1-3)]. We and others (4, 5) have projected that complex traits are likely to have an increasingly large number of susceptibility loci that have correspondingly smaller individual contributions to heritability. A fraction of these loci could be detected in future GWAS with large, but realistic, sample sizes, but they are still unlikely to fully explain a large fraction of missing heritability (4). The spectrum of genetic variation is greater than originally anticipated and suggests that the underlying genomic architecture of many diseases and traits could be more complex. Availability of newer-generation genotyping chips and sequencing technologies has raised the hope that future studies of uncommon and rare variants could increase the rate of discovery and explain an additional fraction of heritability, and eventually approach clinically useful discriminatory performance for genetic risk models.The discoveries generated from GWAS provide important insights ...