Bioinformatics, genomics and diabetes

Abstract

Bioinformatic analysis has been a key in unraveling the genetic basis of diabetes mellitus, which figured predominantly among target diseases for research after the human genome project. Despite extensive research the genetic contribution using current methods explains less than 10 % of predisposition. Data from next generation sequencing is bound to alter diagnosis, pathogenesis and treatment targets. Insight into the fine genetic architecture allows a fine grained classification of the diabetes spectrum, allowing primary preventive methods in at-risk individuals. In this quest the role of computational, statistical and pattern recognition would play increasingly major roles.