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Neuroscience Data in the Cloud Opportunities and Challenges: Proceedings of a Workshop (2020) / Chapter Skim
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8 Genetic Data
Pages 51-54

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From page 51...
... . • Genetic studies in vulnerable populations, such as underserved communities or those with mental health disorders, present challenges related to participant engagement and the risk of stigmatization (Jakeman, Nalls, Neale, Rosati)
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From page 52...
... CURRENT PROMISING PRACTICES FOR MANAGING GENETIC DATA IN THE CLOUD Neale said there are large datasets already available in the cloud and a clear NIH investment in building infrastructure and supporting upload and access with a variety of different approaches. Nalls, for example, is working with hybrid models that combine cloud computing with high-performance systems such as the high-performance computing Biowulf cluster at NIH.1 This sandbox approach, said Nalls, allows researchers to test their software locally or on a small local cluster before going to production scale in the cloud, thus maximizing resources and reducing costs.
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From page 53...
... Engaging study participants for genetic research studies of vulnerable populations raises significant challenges, said Neale. Partnering with different ancestral groups from study inception is advisable although not
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From page 54...
... However, he acknowledged that there are potential risks for group characterization that can emerge from studies of vulnerable populations, which can cause distress. Rosati added that mental health disorders, addiction, and some other conditions that could be revealed by genetic information are associated with a substantial amount of stigma.
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Key Terms

  • genetic information
  • genomic data
  • cloud computing
  • hybrid models
  • neale added

  • combine cloud
  • health disorders
  • research participants
  • kristen rosati
  • local cluster

  • treatment purposes
  • treats genetic
  • withholding genetic
  • genetic results
  • aging genetic

  • regarding genetic
  • genetic datasets
  • treating genetic
  • managing genetic
  • genetic research

  • genetic testing
  • thorny issues
  • inform research
  • substantial amount
  • lyn jakeman

  • personal data
  • software locally
  • maximizing resources
  • computing biowulf
  • jonathan cohen


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