• New responsible data sharing technique w

    From ScienceDaily@1:317/3 to All on Wednesday, March 09, 2022 21:30:46
    New responsible data sharing technique will enable better understanding
    of disease-causing genetic variants
    Using federated analysis on protected health data sets can lead
    scientists to a more nuanced understanding of heritable disease

    Date:
    March 9, 2022
    Source:
    University of California - Santa Cruz
    Summary:
    Scientists may better understand and test for the genetic
    variations that cause cancer and other heritable diseases through
    the application of federated analysis, a novel strategy for securely
    sharing and analyzing genomic data developed at the UC Santa Cruz
    Genomics Institute.



    FULL STORY ========================================================================== Scientists may better understand and test for the genetic variations
    that cause cancer and other heritable diseases through the application
    of a novel strategy for securely sharing and analyzing genomic data
    developed at the UC Santa Cruz Genomics Institute.


    ========================================================================== Understanding the clinical significance of rare genetic variants requires analyzing large amounts of genomic and clinical data. Privacy policies, however, restrict the sharing of this information between institutions,
    and no single institution is likely to have all the resources needed
    for a robust analysis.

    In a paper published March 9 in the journal Cell Genomics, UCSC
    researchers showed that an approach called federated analysis can
    overcome this problem by "bringing the code to the data." This is the
    first application of federated analysis to enable classification of
    previously unclassified genetic variants.

    "We have to find ways to get to the data that respect privacy, but still
    let researchers do their research, so the federated model is definitely
    the way of the future," said James Casaletto, a PhD candidate at UCSC's
    Baskin School of Engineering and the paper's lead author.

    The study focused on genetic variants of the breast cancer genes BRCA1
    and BRCA2. People who inherit harmful variants of one of these genes
    have increased risks of breast, ovarian and other cancers. Many people, however, have variants of unknown significance (VUS) in these genes,
    meaning scientists don't know if these variants are harmful or not.

    The new study provides a more nuanced understanding of BRCA1 and
    BRCA2 variants. It also serves as a "proof of concept" of a novel data
    sharing and analysis technique for assessing the clinical implications
    of genetic variants.



    ========================================================================== Specific VUS are individually rare but collectively it is common for
    VUS in general to occur in the human population. To better understand
    exactly which VUS are disease-causing, researchers need to perform
    delicate analysis on a wide set of data, which can then be interpreted
    by experts to make clinical conclusions.

    "This has to do with the everyday person who maybe wonders if there's a
    history of cancer in her family, and if she's inherited that family cancer risk," said Melissa Cline, a research scientist at the UCSC Genomics
    Institute. "All of this work is going to the aim of making genetic
    testing better." However, most of the world's human health data is
    'siloed', or stored inaccessibly due to privacy laws, and institutions
    may be prohibited from exporting genomic data they collect, making it inaccessible to researchers who study genetic variants.

    Additionally, engineering the software needed to execute these analyses is complex and usually cannot be undertaken by the average geneticist. UCSC researchers are addressing these two problems with their novel approach
    of federated analysis.

    In the federated analysis approach, researchers bring the code to
    the data, avoiding the need to export sensitive data at all. UCSC
    Genomics Institute software is sent in a "container" to any collaborating institution around the world that is home to a valuable but protected set
    of genomic data. The collaborating institution then uses the software
    to analyze their data within their institution's secure environment,
    generating summary data that does not reveal personal information about individual patients.



    ==========================================================================
    This approach ensures that patient-level data meets the strict privacy
    rules of an institution that do not allow them to export data, but
    allows researchers to collect a much wider pool of genomic data which
    can lead to better clinical conclusions. Moreover, federated analysis
    can get around the issues of uploading, downloading and moving around
    huge data sets that can be prohibitively large.

    "[The paper is] a proof of concept that we have this container technology, we've leveraged it for BRCA1 and BRCA2, we've also demonstrated in the
    research that it can be used for other genes -- genotypes and phenotypes," Casaletto said.

    For this project, UCSC researchers collaborated with the RIKEN Center
    for Integrative Medical Sciences in Japan to analyze their biobank of
    BRCA1 and BRCA2 genomic data. These genes are inherited from a person's
    parents and when mutated can lead to an increased risk of breast, ovarian,
    and other cancers.

    They used this to make discoveries about which specific variants in the
    BRCA1 and BRCA2 genes led to cancer and which left patients unaffected,
    moving the needle on a number of previously uncertain variants.

    In undertaking this analysis, the researchers were able to help address
    the lack of diversity in genetic databases.

    "The genetics of white people are highly over-represented, the genetics
    of non- white people are much more of a mystery, due to a lot of
    historical biases in data collection," Cline said. "We were also able
    to add together a little more knowledge on Japanese genetics than was previously available." Further collaboration using federated analysis
    with institutes worldwide could similarly do much to address the lack
    of representation of non-white people and empower insitutions that may
    be resource-poor to contribute to the global genomic data pool.

    "What's been done in the past is basically a lot less data sharing,
    so the name of the game is really global data sharing," Cline said.

    The researchers also work with the The Global Alliance for Genomics and
    Health (GA4GH), which helps set policy and create technical standards
    for responsible, ethical data sharing. They gave guidance on what
    data sharing can be done legally and responsibly, and helped establish
    methods to make the Genomics Institute software portable across different operating systems and environments to allow for collaboration like that
    in this project.

    Other coauthors of the paper include Charles Markello of UCSC, Michael
    Parsons and Amanda Spurdle of Australia's Berghofer Medical Research
    Institute, and Yusuke Iwasaki and Yukihide Momozawa of Japan's RIKEN
    Center for Integrative Medical Sciences.


    ========================================================================== Story Source: Materials provided by
    University_of_California_-_Santa_Cruz. Original written by Emily
    Cerf. Note: Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. James Casaletto, Michael Parsons, Charles Markello, Yusuke Iwasaki,
    Yukihide Momozawa, Amanda B. Spurdle, Melissa Cline. Federated
    analysis of BRCA1 and BRCA2 variation in a Japanese cohort. Cell
    Genomics, 2022; 2 (3): 100109 DOI: 10.1016/j.xgen.2022.100109 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/03/220309131901.htm

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