Sequencing and sample core: genetic errors of immunity
- Institution
- Washington University, MO
- Principal investigator
- Dusan Bogunovic
- Funding
- $414K
- Duration
- 2025–2030
Moonbase score
Still being scored
Abstract
Project Summary—Core A The exponential growth of next-generation sequencing has revolutionized the study of genetic errors of immunity. We, as a community, have collectively identified over 500 genetic lesions leading to immune dysregulation syndromes. These syndromes encompass susceptibility to infections, autoinflammation, autoimmunity, allergy and cancer. The identification and characterization of genetic lesions is paving the way for the use of personalized medicine and the study of rare genetic syndromes. Despite the tremendous success achieved, the genetic cause of the disease remains to be identified in over 70% of families. Furthermore, even when a genetic lesion is identified, it may not segregate perfectly with the disease. Thus, some individuals carrying the mutation are very ill, whereas others display little or no clinical disease (low clinical penetrance of the disease). Both of these problems — the absence of a known genetic cause of disease and variable disease penetrance — require further investigation. We now have opportunities to study samples from human subjects with an unprecedented degree of granularity, and advances in next-generation sequencing are making it possible to decipher increasingly complex datasets for genetic and molecular signatures. These samples were obtained from human subjects with unknown genetic lesions or known genetic lesions, from families in which clinical disease has incomplete penetrance. The human sequencing and sample core will streamline the datasets available for all enrolled patients and their families, by storing samples, sequencing (DNA and RNA) in bulk and at single-cell levels at typical and extraordinary depths, and will store the sequencing results, sharing these data with the bioinformatics core and associated projects. By reducing barriers and uniformizing data from different sources in our shared approaches it should be possible to increase the efficiency of data collection and downstream analyses for all P01 projects.
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