benchmarking methods for identifying causal mutations

Benchmarking Methods for Identifying Causal MutationsTal Friedman

Rare Genetic Diseases

•Our goal: identify and diagnose rare genetic diseases

•Difficult for clinicians due to incredibly low exposure

•Often not already documented

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

Series10

100

200

300

400

500

600

700

371

547

659

# of causal genes as top hit (N=999)

First attemptSecond attemptExomiser reported

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

Phenotypic Noise & Imprecision

Results (preliminary)

Series10

100

200

300

400

500

600

700

800

900

397483

768

# of causal genes as top hit (N=890)

ExomiserAveragingPhenomeCentral

Challenges

•Data

•Data

•More data

ChallengesROC Curve for Phenotypic Similarity Algorithm

Questions!