Benchmarking Methods for Identifying Causal Mutations Tal Friedman

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

Overview Why are we doing any of this? Background What you did Why this was important Finding the cause of rare genetic diseases (introduce rare genetic diseases) We have this PC thing, trying to help clinicians find additional patients and candidate genes It uses the HPO to do some fancy stuff But, we don’t really know how well it’s working Exomiser is a good start (how well is it working) 1) try to recreate results, 2) extend to patient matching domain

PhenomeCentral Clinicians upload patient data

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

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

Exomiser (Robinson et. al, 2014)

Objectives Reproduce Exomiser performance Expand to new patient similarity domain

Patient Simulation Control Genome Mutation HPO Terms Infected Patient Disease

Results

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

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

Results (preliminary)

Challenges Data More data

Challenges ROC Curve for Phenotypic Similarity Algorithm

ROC Curves For a binary classifier Plots TPR vs. FPR for varying threshold values Often compared with AUC

Acknowledgements

Questions!