Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes
Samantha N Piekos1,2, Sadhana Gaddam1, Pranav Bhardwaj3
1Program in Epithelial Biology, Stanford University, Stanford, California, United States of America.
Plos Computational Biology
|September 20, 2021
Summary
Biomedical Data Commons (BMDC) integrates fragmented data to accelerate Type 1 Diabetes (T1D) research. This knowledge graph identifies novel B-lymphocyte genetic variants, aiding disease understanding and therapeutic development.
Area of Science:
- Genomics and Bioinformatics
- Immunology
- Computational Biology
Background:
- Biomedical data fragmentation hinders research, especially for Type 1 Diabetes (T1D).
- Genetic contributions of B-lymphocytes in T1D are understudied compared to T-lymphocytes and beta-islet cells.
Purpose of the Study:
- To create a knowledge graph (Biomedical Data Commons - BMDC) for integrating multi-formatted biomedical data.
- To develop a pipeline utilizing B-lymphocyte epigenome and connectome data to identify T1D-associated genetic variants.
- To assess the role of B-lymphocyte genetic variants in Type 1 Diabetes.
Main Methods:
- Development of the Biomedical Data Commons (BMDC) knowledge graph.
- Creation of a computational pipeline integrating B-lymphocyte epigenome and connectome data.
- Deployment of BMDC to analyze genetic variants in the context of T1D.
Main Results:
- BMDC significantly accelerates data analysis for T1D research.
- Identified T1D-associated variants are predominantly common, non-coding, and of unknown clinical significance.
- Prioritized variants show cell-type specificity but associate with known T1D genes (e.g., HLA-DQB1, HLA-DRB1, IL2RA) and novel candidates.
Conclusions:
- The BMDC platform enhances accessibility and reproducibility of biomedical data.
- The pipeline effectively prioritizes cell-type-specific genetic variants for T1D.
- Identified novel variants offer new avenues for mechanistic and clinical validation in T1D research.
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