Neurological and mental disorders occur often, with approximately 450 million people suffering from them worldwide. Like most other common diseases, neurological disorders are hypothesized to be highly complex, with interactions among genes and risk factors playing a major role in the process. In recent years it has become obvious that for common diseases there may be more complex interactions among genes with and without strong independent main effects. These effects are more difficult to detect using traditional methodologies. In this manuscript the author introduces the concept of epistasis and the challenges associated with detecting it. Next, she briefly mentions a number of bioinformatics approaches that have been developed to deal with this issue. Multifactor dimensionality reduction is a methodology developed specifically to deal with the challenge of detecting interaction effects in the absence of statistically detectable main effects in studies of common disorders, such as Alzheimer disease or brain cancer. Finally, the author describes the future directions for this technique and related methodologies.
All Science Journal Classification (ASJC) codes
- Clinical Neurology