In this talk, we consider detection of signal regions associated with disease outcomes in Genome-Wide  Association Studies (GWAS). Gene- or region-based methods have become increasingly popular in GWAS as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in  a pre-specified region, e.g., a gene. In view of massive intergenic regions in GWAS and substantial interests in identifying signal regions for subsequent fine mapping, we propose a computationally efficient quadratic scan (Q-SCAN) statistic based method to detect the existence and the locations of signal regions by  scanning the genome continuously. The proposed method accounts for the correlation (linkage disequilibrium) among genetic variants, and allows for signal regions to have both signal  and neutral variants, and signal variants whose effects can be in different directions. We study the asymptotic properties of the proposed Q-SCAN statistics. We derive an asymptotic threshold that controls for the family-wise error rate, and show that under regularity conditions the proposed method consistently selects the true signal regions. We perform simulation studies to evaluate the finite sample performance of the proposed method. Our simulation results showed that the proposed procedure outperforms the existing methods, especially when signal regions have signal variants whose  effects are in different directions, or are contaminated with neutral variants, or have correlated variants. We apply the proposed method to analyze a lung cancer genome-wide association study to identify the genetic regions  that are associated with lung cancer risk.