Computational analysis of gene-gene interactions using multifactor dimensionality reduction.
Understanding the relationship between DNA sequence variations and biologic traits is expected to improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearities in the genotype-to-phenotype mapping relationship as a result of gene-gene interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known as multifactor dimensionality reduction that was specifically designed for the identification of multilocus genetic effects is presented. Several case studies that demonstrate the detection of gene-gene interactions in common diseases such as atrial fibrillation, Type II diabetes and essential hypertension are also discussed.
Moore
Jason H
JH
Dartmouth Medical School, Computational Genetics Laboratory, 706 Rubin Building, HB7937, One Medical Center Drive, Lebanon, NH 03756, USA. jason.h.moore@dartmouth.edu
D019295:Computational Biology / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease; D006801:Humans; D012984:Software