(2) Biological data is inherently extremely noisy, so the underlying complexities of biological systems (such as linkage disequilibrium and genetic heterogeneity) need to be incorporated into the statistical models for disease association studies. The chances of developing many common diseases such as cancer, autoimmune diseases and cardiovascular diseases involves complex interactions between multiple genes and several endogenous and exogenous environmental agents or covariates. Many previous disease association studies could not produce significant results because of the lack of incorporation of statistical interactions in their mathematical models explaining the disease outcome.