THE CRITICAL NEED FOR COMPUTATIONAL INTELLIGENCE IN HUMAN GENETICS

摘要

An important goal of human genetics is to identify which genes and which specific DNA sequence variations (polymorphisms) play an important role in determining susceptibility to common diseases such as cancer, essential hypertension, and schizophrenia. Human genetics has been very successful in identifying rare DNA sequence variations (mutations) that predict with virtual certainty whether an individual will develop a rare disease such as cystic fibrosis or sickle cell anemia. However, this same level of success has not been observed for common diseases that represent the majority of the public health and economic burden. This general lack of success is due to the enormous complexity of common diseases, such as cardiovascular disease, that are determined by many genes and many environmental factors that interact in a nonlinear manner. Gene-gene interactions (epistasis) and gene-environment interactions (plastic reaction norms) are important components of the genetic architecture of common diseases and are receiving increased attention as geneticists and epidemiologists move from studying diseases with simple etiologies to diseases with very complex etiologies. I will briefly introduce some of the computational challenges we are facing in human genetics, hopefully as a way to motivate computer scientists to establish meaningful collaborations with geneticists and epidemiologists on the front lines of identifying genetic risk factors for common human diseases. This new domain needs computational attention.