PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes.

Yannis J Trakadis,Sébastien Lévesque,Caroline Buote,Jean-François Therriault,Hugo Larochelle,Pierre-Étienne Jacques

doi:10.1186/1755-8794-7-22

Abstract

BackgroundWe propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test.Twenty test-patients with varied syndromes were selected from the literature. For each patient, the mutation, phenotypic data, and genetic diagnosis were available. Next, control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient’s phenotype and all variations present in the corresponding exome. The physician did not have access to the individual mutations. PhenoVar filtered the search using a cut-off phenotypic match threshold to prevent undesired discovery of incidental findings and ranked the OMIM entries according to diagnostic score.ResultsWhen assigning the same weight to all variants in the exome, PhenoVar predicted the correct diagnosis in 10/20 patients, while in 15/20 the correct diagnosis was among the 4 highest ranked diagnoses. When assigning a higher weight to variants known, or bioinformatically predicted, to cause disease, PhenoVar’s yield increased to 14/20 (18/20 in top 4). No incidental findings were identified using our cut-off phenotypic threshold.ConclusionThe phenotype-driven approach described could render widespread use of ES more practical, ethical and clinically useful. The implications about novel disease identification, advancement of complex diseases and personalized medicine are discussed.

Highlights

We propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test
On average 3942 variants were obtained per filtered exome, of which 53 and 23 were included as disease-causing in Human Gene Mutation Database (HGMD) and ClinVar, respectively
When ranking the possible diagnoses solely based on phenotypic weight, PhenoVar predicted the correct diagnoses in three patients but did not rank the correct diagnosis as part of the top 20 possible diagnoses in 9 out of 20 patients (Table 3, Column 3)

Summary

Introduction

We propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test. The mutation, phenotypic data, and genetic diagnosis were available. Control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient’s phenotype and all variations present in the corresponding exome. We hereby present PhenoVar, a software consistent with this phenotype-driven approach, and provide preliminary evidence of its potential benefits. Potential emotional distress over disease risk even among healthy individuals

Results

Discussion

Conclusion