Privacy Preserving Analytics for Kawasaki Disease in African-Americans (Special Webinar)

Date: 

Tue Aug 6, 2013

Host: 

Jane Burns and Jihoon Kim
University of California, San Diego

Category: 

Kawasaki Disease (DBP 4)

Time: 11:00AM Pacific, 2:00PM Eastern.

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ABSTRACT

Preserving research subject privacy while accelerating discovery through inter-institutional collaborations remains a challenge. Genotype data itself is considered an identifiable code similar to a social security number or fingerprint, so individual-level data cannot be shared while still preserving privacy. Collaborations between countries are further restricted by government-level policies prohibiting sharing of individual-level data. Previous work in this application have been tested merely on simulated data, but not on the real-world setting. We will use a real-life challenge associated with the analysis of single nucleotide polymorphisms (SNPs) and susceptibility to Kawasaki disease (KD) among U.S. children of African American (AA) descent to test these privacy-preserving algorithms and solve practical challenges. 

 

SPEAKER BIOGRAPHY

Jane Burns, MD, is Professor of Pediatrics in the UCSD School of Medicine and Director of the Kawasaki Disease Research Center at UCSD. In 2009 she received the Pioneer Award for Kawasaki Disease Research. Chosen for her outstanding commitment, compassion, and life-long career devoted to Kawasaki Disease research, awareness and treatment, Dr. Burns will be the lead investigator on iDASH’s Kawasaki genomics project.

Jihoon Kim, MS, is senior statistician for the Division of Biomedical Informatics. He has a master’s degree in statistics and has extensive experience in supporting investigators in various statistical matters, including design of experiments, sample size calculations, implementation of software to support statistical analyses, and reporting results in scientific publications. Furthermore, he has a master’s degree in bioinformatics, and has led the development of open-source software for integration of gene expression data from diverse platforms.

 

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