Children’s rare disease cohorts: an integrative research and clinical genomics initiative
| Autor: | Andrew Quitadamo, Michael Field, Meredith Park, Pankaj B. Agrawal, Timothy W. Yu, Shira Rockowitz, Annapurna Poduri, Lily Li-Wen Wang, Scott B. Snapper, Lacey Smith, Beth Rosen Sheidley, Devon Knight, Catherine A. Brownstein, Alan H. Beggs, Mary Carmack, Nicholas LeCompte, Piotr Sliz, Emma Sexton, David A. Williams, Ingrid A. Holm, Susan Kornetsky |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
lcsh:QH426-470 Relational database Computer science lcsh:Medicine Genomics 030105 genetics & heredity Data type Article 03 medical and health sciences Genetics Molecular Biology Genetics (clinical) Exome sequencing business.industry Medical record lcsh:R Paediatrics Data science Personalized medicine Data processing lcsh:Genetics 030104 developmental biology Workflow business Genetic databases Medical genomics Rare disease |
| Zdroj: | NPJ Genomic Medicine npj Genomic Medicine, Vol 5, Iss 1, Pp 1-12 (2020) |
| ISSN: | 2056-7944 |
| Popis: | While genomic data is frequently collected under distinct research protocols and disparate clinical and research regimes, there is a benefit in streamlining sequencing strategies to create harmonized databases, particularly in the area of pediatric rare disease. Research hospitals seeking to implement unified genomics workflows for research and clinical practice face numerous challenges, as they need to address the unique requirements and goals of the distinct environments and many stakeholders, including clinicians, researchers and sequencing providers. Here, we present outcomes of the first phase of the Children’s Rare Disease Cohorts initiative (CRDC) that was completed at Boston Children’s Hospital (BCH). We have developed a broadly sharable database of 2441 exomes from 15 pediatric rare disease cohorts, with major contributions from early onset epilepsy and early onset inflammatory bowel disease. All sequencing data is integrated and combined with phenotypic and research data in a genomics learning system (GLS). Phenotypes were both manually annotated and pulled automatically from patient medical records. Deployment of a genomically-ordered relational database allowed us to provide a modular and robust platform for centralized storage and analysis of research and clinical data, currently totaling 8516 exomes and 112 genomes. The GLS integrates analytical systems, including machine learning algorithms for automated variant classification and prioritization, as well as phenotype extraction via natural language processing (NLP) of clinical notes. This GLS is extensible to additional analytic systems and growing research and clinical collections of genomic and other types of data. |
| Databáze: | OpenAIRE |
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