Evolutionary Medicine and Informatics: Personal Genomics
Predicting Adaptive and Disease Propensities of Mutations in Individuals
Personal Genomics is a branch of genomics wherein individual genomes are analyzed using bioinformatics techniques with the aim of predicting their association with disease and adaptation. Individual genomes can differ at millions of positions, many located in genes strongly associated with heritable diseases. Hence, an experimental biological approach alone is not practical for decoding the functional effects of personal mutations; computational approaches are a key to this endeavor. The field of personal genomics has thus begun to occupy a central position in genomics research.
Our development of innovative approaches in personal genomics include technological methods that generate new data from individuals whose genomes are associated with unique diseases and disorders; statistical approaches that compare these genomes across ethnically and geographically distinct human populations, as well as across species closely and distantly related to us; theoretical approaches that use present and past patterns to predict the mutations that are most likely to be beneficial or detrimental; and, finally, functional approaches that use complex models to actually measure the different effects of each of these mutations. With this combined interdisciplinary approach, we can investigate how, where, why, and when specific mutations occurred in our genomes, and what their functional significance is to us, as well as to future generations.
Personal Genomics Team
Core: Yuseob Kim, Sudhir Kumar
Key: Valentin Dinu, Philip Hedrick, Matthew Scotch, Brian Verrelli
Senior: Alan Filipski, Li Liu
Junior: Vanessa Gray, Kim Kukurba, Glenn Markov, Aditya Paliwal, Michael Suleski
Dan Peterson, Glen Stecher, Max Sanderford
Atul Butte, Stanford Medical School
Joel Dudley, Stanford Medical School