Stanford Researchers Completing Genome Analysis with Patient Privacy Ensured

Aug. 23, 2017
Stanford researchers are now using a “genome cloaking” technique that scours complete human genomes for the presence of disease-associated genes without revealing any genetic information not directly associated with the inquiry.

Stanford researchers are now using a “genome cloaking” technique that scours complete human genomes for the presence of disease-associated genes without revealing any genetic information not directly associated with the inquiry.

This genome cloaking technique, devised by biologists, computer scientists and cryptographers at Stanford University, addresses many concerns about genomic privacy and potential discrimination based on an individual’s genome sequence, according to researchers.

Using the technique, the researchers were able to identify the responsible gene mutations in groups of patients with four rare diseases; pinpoint the likely culprit of a genetic disease in a baby by comparing his DNA with that of his parents; and determine which out of hundreds of patients at two individual medical centers with similar symptoms also shared gene mutations. They did this all while keeping 97 percent or more of the participants’ unique genetic information completely hidden from anyone other than the individuals themselves, according to the research.

“We now have the tools in hand to make certain that genomic discrimination doesn’t happen,” said Gill Bejerano, Ph.D., associate professor of developmental biology, of pediatrics and of computer science at the university. “There are ways to simultaneously share and protect this information. Now we can perform powerful genetic analyses while also completely protecting our participants’ privacy.”

The researchers specifically hope that routine implementation of their technique will help individuals overcome any qualms about privacy that may keep them from sharing their genome sequences. In particular, people may be concerned that DNA sequences or genetic variants currently unassociated with diseases may in the future be linked with as-yet-unidentified increases in risk.   

“These are techniques that the cryptography community has been developing for some time,” said Dan Boneh, Ph.D., professor of computer science and of electrical engineering.  “Now we are applying them to biology. Basically, if you have 1 million people with genomic data they would like to keep private, this approach lets researchers analyze the data in aggregate and only report on findings that are pertinent. An individual might have dozens of anomalous genes, but the researchers and clinicians will only learn about the genes relevant to the study, and nothing else.”

A key component of the technique is the involvement of the individual whose genome is to be studied. In particular, each individual encrypts their genome (with the help of a simple algorithm on their own computer or smartphone) into a linear series of values describing the presence or absence of the gene variants under study, without revealing any other information about their genetic sequence. The encrypted information is uploaded into the cloud and the researchers then use a secure, multi-party computation (a cryptographic technique that ensures the input data remain private) to conduct the analysis and reveal only those gene variants likely to be pertinent to the investigation, researchers explained.

“In this way, no person or computer, other than the individuals themselves, has access to the complete set of genetic information,” said Boneh. “In each case, the analysis was performed within seconds or minutes with moderate computing power. They hope to extend the technique to include diseases caused by combinations of multiple genetic variants or to handle tens of thousands of sequences such as those found in genome-wide association studies.”

Ultimately, stated the researchers, the goal is to find the best way to both share the genetic information with researchers while also protecting each patient’s privacy in order to advance medical knowledge.

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