Observational Study of Advanced Data Analytics in Genetic Conditions

Background: The genes a person is born with can sometimes cause serious diseases. Genetic diseases are rare, but they can have a big impact on the people they affect. Researchers have already made great strides in understanding how some genes cause disease. But they would like to have even better tools to analyze and understand genetic data. To create these new tools, they need to gather health and genetic data from a lot of people. Objective: This natural history study will gather medical information from people with genetic conditions. Eligibility: People of any age who (1) are known or suspected to have a genetic condition or (2) have a family member with a known or suspected genetic condition. Design: Participants will come to the clinic for up to 4 days. Tests to be performed will vary depending on the nature of each participant s health issue. The tests may include: Blood and saliva. Blood may be drawn from a vein; cells and saliva may be collected by rubbing the inside of the cheek with a swab. These would be used for genetic testing. Imaging scans. Participants may have X-rays or other scans of their bodies. They may lie still on a table while a machine records the images. Heart tests. Participants may lie still while a technician places a probe on their chest. They may also have stickers attached to wires placed on their chest. Photographs and recordings. Pictures may be taken of facial features, skin changes, or other effects of the genetic condition. Video and audio recordings may also be made. Some people may be able to participate via telehealth.

Study Description: We hypothesize that the use of advanced, computationally-based analytic techniques can provide insights into the causes, manifestations, and mechanisms of genetic diseases. To address this hypothesis, we will collect phenotypic and biologic data relevant to genetic conditions and will study computational tools that analyze these data. Objectives: Primary Objectives: To collect, collate, and analyze datasets relevant to genetic conditions using advanced computational approaches, with the objective of developing and iterating novel methods that can efficiently and accurately parse diverse, complex datasets related to genetic conditions to reveal novel clinical and biological insights, and that can be compared to current and other state-of-the-art approaches. Endpoints: Primary Endpoint: Not applicable Secondary Endpoints: Not applicable