Lab Medicine & Pathology
Analysis of Genome-Wide Data to Identify Genetic Risk Factors for Diseases
These researchers use a variety of statistical techniques to analyze de-identified genome-wide association data. These datasets include those generated by the NIH, as well as those from the University of Minnesota Genomics Center. The goal will be to better understand patterns of sequence variation and genomic architecture (deletions, duplications, and inversions), as well as to devise, evaluate, and utilize methods to identify genes that contribute to disease susceptibility.
Professor Nathan Pankratz
Nicolas Paredes Sepulveda