The existing methods for genetic-interaction detection in genome-wide association studies are designed from different paradigms, and their performances vary considerably for different disease models. One important reason for this variability is that their construction is based on a single-correlation model between SNPs and disease. Due to potential model preference and disease complexity, a single-objective method will therefore not work well in general, resulting in low power and a high false-positive rate.