variant-analysis-practice

Practice workflow for variant filtering, annotation, and interpretation using VCF-style data.

Goal

To demonstrate basic steps used in variant analysis, including quality filtering, population frequency filtering, and clinical database review.

Planned Workflow

1. Start with VCF-style variant data
2. Filter low-quality variants
3. Prioritize rare variants
4. Review known clinical significance using resources such as ClinVar
5. Summarize candidate variants for interpretation

Skills Demonstrated

• Variant filtering logic
• VCF interpretation
• Population frequency reasoning
• Clinical genomics terminology
• Reproducible analysis with Python

Example Variant Filtering

Starting from the sample dataset:

Filtering criteria:

• Remove variants with quality < 30
• Remove variants with frequency > 0.01

Filtered Results

See filtered_variants.csv

Remaining candidate variants:

• BRCA1 (rare, high quality)
• CFTR (very rare, high quality)

These candidates would be further evaluated using databases such as ClinVar and population resources.

Code Example

A simple Python script (filter_variants.py) was used to filter variants based on quality and population frequency.