Tutorial: Understanding Genome-Wide Association Studies (GWAS) and Database Design
1. Introduction to Genetics and GWAS
- 1.1. What is DNA, Genes, and Genomes?
- 1.2. Genetic Variation: SNPs and Alleles
- 1.3. What is a Genome-Wide Association Study (GWAS)?
- 1.4. The Importance of Large Datasets in GWAS
2. GWAS Data: Characteristics and Representation
- 2.1. Genotype Data: SNPs and Numerical Encoding
- 2.2. Phenotype Data: Traits and Measurements
- 2.3. Metadata: Annotations and Demographics
- 2.4. Exploring Real GWAS Data
3. Open GWAS Data Resources
- 3.1. Introduction to Open GWAS Datasets
- 3.2. The 1000 Genomes Project
- 3.3. The UK Biobank
- 3.4. dbGaP (Database of Genotypes and Phenotypes)
- 3.5. Other Relevant Resources
- 3.6. Tips for Working with Open Datasets
4. GWAS Data File Types and Structures
- 4.1. VCF (Variant Call Format)
- 4.2. Understanding VCF in the Context of GWAS
- 4.3. Other Relevant File Considerations
5. Database Design for GWAS Data
- 5.1. Relational vs. Columnar Databases: A Brief Overview
- 5.2. Why VAST DB is Well-Suited for GWAS
- 5.3. Multi-Table, Partitioned Approach (Recommended)
- 5.4. Single-Table (Wide-Column) Approach: Considerations and Trade-offs
- 5.5. Data Types and Schema Design
- 5.6. Example Schema and Data Representation
6. Hands-on Demo with Sample GWAS Data
- 6.1. Obtaining and Preparing Sample GWAS Data
- 6.2. Setting Up VAST DB for the Demo
- 6.3. Running Example Queries and Analyses
- 6.4. Performing comparative timing tests.
7. Querying and Analysis in VAST DB
- 7.1. Basic Queries: Selecting Individuals and SNPs
- 7.2. Advanced Queries: Statistical Analysis
- 7.3. Performance Optimization: Predicate Pushdown and Partitioning
- 7.4. Snapshot Strategies.
8. Demonstrating VAST DB's Value: Performance Benchmarks and Comparisons
- 8.1. Common GWAS Analysis Workflows and Bottlenecks
- 8.2. Benchmarking Methodology: Defining Relevant Metrics
- 8.3. Comparative Analysis: VAST DB vs. Traditional Approaches
- 8.4. Real-World Case Studies and Examples
- 8.5. Visualizing Performance Differences.
9. Practical Considerations and Future Directions
- 9.1. Data Ingestion and Quality Control
- 9.2. Scalability and Performance Tuning
- 9.3. Ethical and Privacy Considerations
- 9.4. Future Trends in GWAS and Database Technology