Benchmarking Methods for Identifying Causal Mutations

1. Benchmarking Methods for Identifying Causal Mutations Tal Friedman

2. Rare Genetic Diseases • Our goal: identify and diagnose rare genetic diseases • Difficult for clinicians due to incredibly low exposure • Often not already documented

3. PhenomeCentral • Clinicians upload patient data

4. PhenomeCentral • Matchmaking algorithm displays most similar patients • Get additional evidence from other clinicians

5. Background • Phenotype: Observable characteristics • Human Phenotype Ontology (HPO) Robinson et. al

6. Exomiser (Robinson et. al, 2014)

7. Objectives • Reproduce Exomiser performance • Expand to new patient similarity domain

8. Patient Simulation • Control Genome • Disease • Infected Patient • Mutation • HPO Terms

9. Results

10. Patient Similarity • Phenotypic similarity algorithm • Hypothesis: same disease/causal gene • Combine Exomiser results

11. Patient Pair Simulation • Patient 1 • Patient 2 • Control Genome A • Control Genome B • Sampled mutation • Sampled mutation • Disease • Sampled HPO terms • Sampled HPO terms • Phenotypic Noise & Imprecision • Phenotypic Noise & Imprecision

12. Results (preliminary)

13. Challenges • Data • Data • More data

14. Challenges ROC Curve for Phenotypic Similarity Algorithm

15. Questions!