Academic Journal
Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort
| Title: | Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort |
|---|---|
| Authors: | James Casaletto, Michael Parsons, Charles Markello, Yusuke Iwasaki, Yukihide Momozawa, Amanda B. Spurdle, Melissa Cline |
| Source: | Cell Genomics, Vol 2, Iss 3, Pp 100109- (2022) |
| Publisher Information: | Elsevier, 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Genetics LCC:Internal medicine |
| Subject Terms: | Variant of uncertain significance, federated computing, variant classification, benign, pathogenic, data privacy, Genetics, QH426-470, Internal medicine, RC31-1245 |
| More Details: | Summary: More than 40% of the germline variants in ClinVar today are variants of uncertain significance (VUSs). These variants remain unclassified in part because the patient-level data needed for their interpretation is siloed. Federated analysis can overcome this problem by “bringing the code to the data”: analyzing the sensitive patient-level data computationally within its secure home institution and providing researchers with valuable insights from data that would not otherwise be accessible. We tested this principle with a federated analysis of breast cancer clinical data at RIKEN, derived from the BioBank Japan repository. We were able to analyze these data within RIKEN’s secure computational framework without the need to transfer the data, gathering evidence for the interpretation of several variants. This exercise represents an approach to help realize the core charter of the Global Alliance for Genomics and Health (GA4GH): to responsibly share genomic data for the benefit of human health. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2666-979X |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666979X22000295; https://doaj.org/toc/2666-979X |
| DOI: | 10.1016/j.xgen.2022.100109 |
| Access URL: | https://doaj.org/article/aa60e37234ec45e0bf36d5bbcfcd63d6 |
| Accession Number: | edsdoj.60e37234ec45e0bf36d5bbcfcd63d6 |
| Database: | Directory of Open Access Journals |
| ISSN: | 2666979X |
|---|---|
| DOI: | 10.1016/j.xgen.2022.100109 |
| Published in: | Cell Genomics |
| Language: | English |