Genome-wide association studies (GWAS) provide an important approach for identifying common genetic variants that predispose to human disease. However, odds ratio (OR) estimates for the reported findings from GWAS discovery data are typically affected by a bias away from the null sometimes referred to the "winner's curse". Also standard confidence intervals (CIs) may have far from the desired coverage rates. We applied a bias reduction method to GWAS findings from several major complex human diseases, including breast cancer, colorectal cancer, lung cancer, prostate cancer, type I diabetes, and type II diabetes. We found the simple bias correction procedure allows one to estimate bias-adjusted ORs that have substantial consistency with ORs from subsequent replication studies, and that corresponding selection-adjusted CIs appear to help quantify the uncertainty of the findings. Selection-adjusted ORs and CIs can provide a reliable summary of GWAS data, and can help to choose single nucleotide polymorphisms for subsequent validation studies.