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 downloaded from public databases, such as dbGaP, as well as those from local sources. 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.
A bibliography of this group’s publications is attached.
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