Genomics and Medicine
Genomics research has started to change the "one-size-fits-all" approach to standard of care in medicine. Pharcogenomics looks into the use of specific drugs and drug dose depending on genetic makeup. Examples include common blood-thinning drug warfarin/Coumadin, antiviral drug abacavir/Ziagen, breast cancer drug trastuzumab/Herceptin among others. Several types of genetic testing are now routinely to a patient available including prenatal, newborn screening, diagnostic and carrier testing. Stanford Hospital has started a clinical service based on Whole Genome Sequencing.
Following is a 20 minute YouTube video where Professor Michael Snyder discusses possibilites of genomics and healthcare at TEDxGunnHighSchool.
Genomics and Privacy
Genome data, like other biometrics such as fingerprints and iris scans, is personally identifiable information (PII). Studies have shown that de-identified genomics data, when combined with other public data sources, can result in increased risk of re-identification. Furthermore, genomic data is shared between close family members and progeny which makes privacy a long term consideration. Our understanding of genomic data with respect to disease related risk factors is continuously expanding thus making risk analysis an ongoing process.
Genomics and Collaboration
Big Data Healthcare is here now - large scale data analytics and cost of sequencing are fast becoming affordable. There are ongoing initiatives to harness this data towards personalized medicine. However, these datasets are currently siloed in large institutions e.g. Million Veteran Program, 23andMe, Geisinger Genomic Study. In order for Big Data Healthcare initiatives to succeed, we will need to bring insights from various datasets together while accounting for complex security and privacy issues.
Following is a 15 minute YouTube video where Professor David Haussler speaks at TEDxSantaCruz about collaboration, from first human genome to establishment of Global Aliiance for Genomics and Healthcare.
Our heartfelt congratulations to winners of iDASH 2016 Challenge
Task 1: Team Bradley Malin, Vanderbilt University
Summary: Practical Protection of Genomic Data Sharing through Beacon Services (privacy-preserving output release)
Description: Given a sample Beacon database, participating teams were asked to develop solutions to mitigate the Bustamante attack. Each algorithm was evaluated based on the maximum number of correct queries that it can respond before any individual can be re-identified by the attack.
Task 2: Team Tal Rabin, IBM
Summary: Privacy-Preserving Search of Similar Cancer Patients across Organizations (secure multiparty computing)
Description: The scenario of this challenge is to find top-k most similar patients in a database on a panel of genes. The similarity is measured by the edit distance between a query sequence and sequences in the database. Participating teams were expected to come up with different algorithms that can provide good approximation to the actual edit distance and also be efficient.
Task 3: Team Kristin Lauter, Microsoft Research
Summary: Testing for Genetic Diseases on Encrypted Genomes (secure outsourcing)
Description: This is to calculate the probability of genetic diseases through matching a set of biomarkers to encrypted genomes that stored in a commercial cloud service. The requirement is that the entire matching process (only consider the exact match for each variation) needs to be carried out using homomorphic encryption so that no trace is left behind during the computation.