Computational Power and Gene Identification

It is know that some of the most common and most serious diseases that plague humans are caused not by a single genetic mutation, but by a combination of many genetic and environmental factors. The situation is further complicated by the fact that most complex diseases have a large number of clinical traits such as various symptoms, body metrics and family history, and methods such as genome-wide gene expression profiling can identify tens of thousands of molecular traits associated with the disease.

Some disease conditions may share common genetic components. For example heart disease and cancer. Furthermore some diseases are characterized by more than one clinical trait. Asthma, for instance, is characterized by more than 50 clinical traits, some related to environment or activity levels, some to symptoms such as wheeziness and tightness of the chest and others to lung physiology. Some of these traits are highly correlated with each other.

Clinical traits, symptoms, diseases and genes are both the unknowns and parameters of the complex equation. Pure computation power is the ultimate tool to find out gene disease interactions. Computational approaches will identify gene-disease-symptom relationships on clinical trials.