Conclusion
Estimated time: 5 minutes
Review how each resource informs the choice of candidate genetic variant, experimental model, and follow-up experiment.
What you accomplished
The workflow includes all 54 published variant rows and adds bulk-tissue expression from GTEx, cell-type measurements from HuBMAP, protein information from Pharos, and variant predictions from ProtVar. The GTEx, HuBMAP, and Pharos results include their source identifiers and retrieval dates.
From computational results to experimental follow-up
The combined results support a qualitative decision about what to study next.
GTEx helps compare tissues for follow-up, and HuBMAP helps assess a possible cell model. Pharos shows what is known about a protein and which research tools are available, while ProtVar provides predictions that help select experiments of protein function or stability.
For the TNNT2 VUS, one possible follow-up experiment would compare wild-type and D259A troponin complexes across calcium concentrations. This experiment would test whether p.Asp259Ala changes calcium-regulated thin-filament activity.
Clinical interpretation also draws on phenotype match, segregation with disease, population frequency, and functional studies of variant effect.
A reusable prioritization checklist
- Name the exact variant and the source of its annotation.
- Ask what biological level each dataset measures.
- Record unavailable data separately from a measured zero.
- Save dataset versions, identifiers, and retrieval dates for reproducibility.
- Distinguish druggability from disease relevance.
- End with a clear next step that can be tested.
Suggested next steps and reading
These papers offer several ways to continue the analysis:
- Variant prioritization: Stenton et al. (2024), co-authored by Elizabeth A. Worthey, compares rare-disease variant prioritization methods and the evidence used to rank candidate genetic variants.
- Protein structure: Varga et al. (2025) explains how protein structures can inform variant interpretation and how the choice of structural model affects the analysis.
- Splicing and RNA evidence: Truty et al. (2021) examines how RNA analysis can help interpret variants predicted to affect splicing.
- Clinical WGS workflow: Worthey et al. (2012) describes an early clinical whole-genome sequencing workflow that considered variant quality, functional effect, disease knowledge, and phenotype relevance together.
Use the complete workflow notebook to apply this approach to another expert-reviewed variant table.
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Watch the Conclusion video to complete the module.